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# Research Synthesis: Influenza Vaccination Rates — full paper ## Abstract Evidence-honesty note: 24/26 retained sources are coded as null or no extracted directional signal; this corpus is non-supportive for clinical efficacy claims and hypothesis-generating only. Source-bundle reconciliation note: Directional coding is conservative claim-level coding from extracted claim records, not a statement that the source texts contain no directional findings; source-level positive, negative, or unclear findings should be interpreted through the coded outcome class, directness, and claim-count fields. 16/26 retained sources are indirect, review-level, adjacent, or mechanistic and are used only to bound interpretation. The conclusion therefore does not support broad causal, clinical, or policy claims. This paper synthesizes evidence on influenza vaccination rates across 26 accepted source papers and 761 high-confidence extracted claims. The evidence profile contains 10 direct clinical sources, 16 adjacent clinical sources, and no sources classified primarily as mechanistic or model-system evidence, with 160 cross-study disagreements across the evidence base. No single positive outcome class dominates the retained corpus; null signals cluster in the contextual adjacent evidence, safety and comorbidity, frailty outcome classes, and negative signals cluster in no dominant outcome class. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect. The conclusion is that influenza vaccination rates remains a bounded geroscience case: the retained clinical and adjacent evidence profile defines the scope for targeted testing, while mixed and null findings limit any unqualified anti-aging claim. ## Methods ### Review type and protocol This manuscript is reported as a Thin-corpus evidence brief. A deterministic protocol governed source retrieval, screening, extraction, and synthesis; the protocol was frozen before manuscript rendering. The full audit trail is in the supplementary `methods_pack.json` and the timestamped submission directory `synthesis-influenza_vaccination_rates-v06-DAILY-2026-06-22T04-11-08Z`. ### Information sources Sources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-06-22. ### Search strategy The following topic-anchored queries were executed against the information sources listed above: - `influenza vaccination rates aging` - `influenza vaccination rates older adults` - `influenza vaccination rates randomized controlled trial` - `influenza vaccination aging` - `influenza vaccination older adults` - `influenza vaccination randomized controlled trial` ### Eligibility criteria - Sources whose primary content addresses influenza vaccination rates. - Sources with extractable quantitative or qualitative findings. - Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable. - Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle). ### Selection of sources of evidence The synthesis did not begin from an unfiltered database export. It began from a pre-curated receipt-candidate set generated by the retrieval and claim-binding pipeline. Of 175 records in the receipt-candidate union, 55 were classified as source candidates and 26 were admitted as traceable synthesis sources. Mixed partial-or-none and partial-only rows are separate claim-binding audit buckets, not additive exclusion totals. No additional records were excluded after final source admission. ### source admission funnel | Admission bucket | n | |---|---:| | Receipt candidate union | 175 | | Classified source candidates | 55 | | No extractable claims | 14 | | None-only claim binding | 15 | | Mixed partial-or-none claim-binding candidates | 77 | | Partial-only claim-binding candidates | 10 | | Strict high-confidence sources | 4 | | Admitted final sources | 26 | ### Exclusion reasons - No records were excluded at the gates instrumented for this run: the eligibility criteria above were applied during retrieval and claim-binding but produced no post-screening exclusions with recorded counts for this corpus. ### Data items The following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating. Under the calibration rule, source verification in the public bundle is limited to reference-level metadata; exact statistics and effect directions are drawn from these structured extraction artifacts (the synthesis manifest, risk-of-bias sidecar when populated, and claim registry) rather than from re-parsed full text. ### Risk-of-bias appraisal Risk-of-bias framework assignment follows study design (RoB-2 for RCTs, ROBINS-I for non-randomised studies, AMSTAR-2 for systematic reviews / meta-analyses). Public appraisal claims are limited to populated `risk_of_bias.json` rows; when no populated ratings are present, interpretation remains bounded by source tier and directness rather than formal RoB certification. ### Synthesis approach Evidence-tension synthesis: claims grouped by outcome class (contextual adjacent evidence, dosing and pharmacokinetics, frailty, immune and inflammation, immune and inflammation, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates. ### AI-use disclosure Source retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified. ### Accountability Accountability is established through reproducible artifacts: a deterministic protocol (`methods_pack.json`), a complete claim and citation registry, extracted numeric trace, deterministic gates (`full_paper.journal_surface.json`, `pre_submit_gate.json`, `artifact_consistency.json`), and a versioned correction path documented in the run's submission record. Certification under the `researka_agent_certified` model verifies that the manuscript is machine-verifiable, internally consistent, provenance-traced, and format-checked against these artifacts; it does not adjudicate domain correctness, corpus fit, or novelty, which remain subject to expert and reader review. ## Results | Evidence domain | Corpus slice | Strongest signal | Directness | Main limitation | |---|---|---|---|---| | Contextual Adjacent Evidence | n=19; claims=577 | no extracted directional signal in 19/19 sources | 8 direct; 7 indirect; 2 protocol; 2 review | limited corpus depth in this outcome class | | Safety and Comorbidity | n=3; claims=68 | no extracted directional signal in 2/3 sources | 2 indirect; 1 review | limited corpus depth in this outcome class | | Immune and Inflammation | n=2; claims=71 | unclear signal in 1/2 sources | 1 direct; 1 indirect | limited corpus depth in this outcome class | | Dosing and Pharmacokinetics | n=1; claims=14 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Frailty | n=1; claims=31 | no extracted directional signal in 1/1 sources | 1 direct | single-source slice; hypothesis-generating | **Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence; these sources bound scope, safety, methods, and translation rather than serving as equal-weight support for the main efficacy claim. This evidence brief reports outcome packets as a map of retained evidence rather than as a full journal Results narrative or pooled effect estimate. ### Contextual Adjacent Evidence Outcomes 19 included sources were assigned to this outcome class. Directional coding: null=19. Directness coding: direct=8, indirect=7, protocol=2, review=2. ### Safety Comorbidity Outcomes 3 included sources were assigned to this outcome class. Directional coding: null=2, unclear=1. Directness coding: indirect=2, review=1. ### Dosing Pharmacokinetics Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Frailty Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: direct=1. ### Immune Outcomes 1 included source were assigned to this outcome class. Directional coding: unclear=1. Directness coding: direct=1. 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. Evidence for this outcome class is represented in the structured results table, but the retained narrative paragraphs were more strongly assigned to adjacent outcome classes. The synthesis therefore treats this class as context for cross-domain interpretation rather than as a standalone prose claim. ## Limitations **Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim. The curated corpus leaves substantial evidence gaps at the design level, and these gaps bound every headline conclusion drawn from it. Several canonical ongoing studies are represented only as protocols — Hansen 2025 (InfluSMS), Liu 2025 (Rapid Verbal Persuasion), and Wang 2024 (EPIC) — meaning their outcome data are not yet available and cannot be pooled. Finally, Hassan 2024 is a Bangladesh tertiary-care protocol with no effectiveness data, so South Asian implementation evidence is missing entirely. Several uptake-modifier findings are supported by only a single source each and therefore cannot be replicated within the corpus. Where a hypothesis is touched by only one source, the within-corpus replication count is zero, and any non-zero effect is treated as hypothesis-generating only. Endpoint scope is narrower than the question demands. Consequently, the synthesis cannot connect the abundant uptake-rate evidence to the hard clinical endpoints that matter for cost-effectiveness modeling, and any inference that higher coverage implies proportionally lower population mortality is not supported by the sources in hand. Several clinically relevant claims rest on mechanistic or proxy evidence where the corpus contains no direct clinical RCT confirmation. The findings on innate immunity in Bonduelle 2025 and the humoral response in decompensated cirrhosis patients in Heisig 2026 are biomarker-level findings; the leap from these proxies to clinical protection requires the surrogate-endpoint caution emphasized by Ioannidis 2005, because neither study reports laboratory-confirmed influenza or hospitalization outcomes. Similarly, the dose-response rationale underlying Espersen 2025's frailty stratification — and any inferred benefit of high-dose over standard-dose vaccine at the 0.8 m/s gait-speed frailty threshold (Studenski 2011) or the more severe 0.6 m/s cutoff (Cesari 2009) — is built on post hoc subgroup analysis of a single trial rather than prospective enrollment of frail participants. Where the corpus offers only mechanistic, biomarker, or self-controlled safety evidence for a clinically actionable claim, that claim can be interpreted as biologically plausible rather than clinically established. ## Conclusion For influenza vaccination rates, the final interpretation is deliberately tiered: the retained clinical and adjacent evidence profile defines a bounded geroscience rationale, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence. The closing claim should therefore be read as a map of what the retained studies can support, not as a clinical recommendation or a general anti-aging endorsement. Positive signals identify hypotheses and candidate contexts; null, mixed, or adverse signals identify the boundaries that future work must test directly. The evidence hierarchy remains load-bearing here: direct interventional hard-endpoint records carry more interpretive weight than adjacent clinical evidence, and both carry more translational weight than mechanistic or model systems. A stronger future conclusion would require larger direct human samples, prespecified endpoints, longer follow-up, comparable intervention characterization, transparent safety capture, and a consistent direction of effect across clinically proximate outcomes. Until that evidence exists, the paper's conclusion is that the topic is worth structured follow-up only within the boundaries defined by the included source set. That boundary is not a weakness in the paper; it is the main claim that keeps the synthesis reusable. Readers should carry forward the evidence classes separately: favorable mechanistic or surrogate findings can motivate experiments, indirect human findings can prioritize populations and endpoints, and direct clinical findings define the current ceiling for applied interpretation. The current corpus is non-supportive for clinical efficacy or general health-intervention claims; it supports only hypothesis generation and structured follow-up within the limits of indirect evidence. Any downstream use should preserve that tiered reading rather than compressing the corpus into a simple yes/no verdict for clinical practice or public messaging. ## What This Synthesis Adds This synthesis maps 26 included sources on Influenza Vaccination Rates across 6 outcome classes and 160 cross-study disagreements. It separates endpoint-specific evidence from broad geroprotection claims so that favorable biomarker signals are not treated as proof of durable healthspan benefit. Across 26 curated reference papers, the evidence base for Influenza shows a context-dependent profile. Null findings dominate: contextual other, safety comorbidity. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Influenza anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established. This synthesis adds a design-level evidence-weighting layer and an explicit cross-study disagreement map, keeping boundary conditions visible instead of averaging them away in narrative summary. ### Boundary-Condition Matrix | Evidence domain | Direct sources | Indirect / mechanism sources | Direction profile | Interpretation boundary | |---|---:|---:|---|---| | frailty | 1 | 0 | null | replication gap | | dosing and pharmacokinetics | 0 | 1 | null | direct interventional hard-endpoint gap | | immune and inflammation | 1 | 0 | unclear | replication gap | | immune and inflammation | 0 | 1 | null | direct interventional hard-endpoint gap | | safety and comorbidity | 0 | 3 | null, unclear | direct interventional hard-endpoint gap | | contextual adjacent evidence | 8 | 11 | null | replication gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | frailty: replication gap | 1 direct and 0 indirect source; direction profile: null | | P2 | dosing and pharmacokinetics: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P3 | immune and inflammation: replication gap | 1 direct and 0 indirect source; direction profile: unclear | | P4 | immune and inflammation: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P5 | safety and comorbidity: direct interventional hard-endpoint gap | 0 direct and 3 indirect sources; direction profile: null, unclear | ### Next-Study Design Recommendation The next high-yield study for Influenza Vaccination Rates should target the **frailty** evidence gap, pre-register the primary endpoint, separate clinical from mechanistic endpoints, preserve safety and adherence capture, and include an analysis plan that can falsify the current boundary-condition claim rather than only confirming a favorable direction. Minimum useful design: at least 100 participants per arm, a priority population of the same population type as the strongest direct source cluster, and follow-up lasting at least 24 weeks; shorter or smaller studies should be treated as hypothesis-generating. ## Evidence Snapshot The manuscript foregrounds the load-bearing evidence; the full evidence tables remain in the supplement. ### Load-Bearing Included Studies - Chen 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null; representative statistic=P > 0.05. - Marshall 2022; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.42. - Yingyounyong 2025; tier=A1; directness=direct; endpoint=immune; direction=unclear; representative statistic=P = 0.008. - Wright 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.435. - Espersen 2025; tier=A1; directness=direct; endpoint=frailty; direction=null; representative statistic=P = 0.052. - Wang 2024; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null. - Hansen 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null. - Katangwe-Chigamba 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null. - Zhang 2024; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null. - Xie 2024; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null. ### Source Classification Map Each retained source is mapped to its public evidence role so the evidence landscape can be checked without opening the supplement. - Impact of multifaceted health education on influenza vaccination health literacy in primary school students: a cluster randomized controlled trial: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=69. - Influence of Digital Intervention Messaging on Influenza Vaccination Rates Among Adults With Cardiovascular Disease in the United States: Decentralized Randomized Controlled Trial: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=69. - A study of booster dose influenza vaccination responses compared to standard dose in lupus patients: an open-labeled, randomized controlled study: outcome=immune; directness=direct; tier=A1; direction=unclear; claims=59. - Effectiveness of a theory-informed intervention to increase care home staff influenza vaccination rates: a cluster randomised controlled trial: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=49. - Relative Effectiveness of High-Dose Versus Standard-Dose Influenza Vaccination Against Hospitalizations and Deaths According to Frailty Score: A Post Hoc Analysis of the DANFLU-1 Randomized Trial: outcome=frailty; directness=direct; tier=A1; direction=null; claims=31. - Nudging towards COVID-19 and influenza vaccination uptake in medically at-risk children: EPIC study protocol of randomised controlled trials in Australian paediatric outpatient clinics: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=26. - Effectiveness of Text Messaging Nudging to Increase Coverage of Influenza Vaccination Among Older Adults in Norway (InfluSMS Study): Protocol for a Randomized Controlled Trial: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=18. - Process evaluation of the flucare cluster randomised controlled trial: assessing the implementation of a behaviour change intervention to increase influenza vaccination uptake among care home staff in England: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=9. - Influenza vaccination in patients with acute heart failure (PANDA II): study protocol for a hospital-based, parallel-group, cluster randomized controlled trial in China: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=9. - Impact of health education on promoting influenza vaccination health literacy in primary school students: a cluster randomised controlled trial protocol: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=5. - Trends in influenza vaccination and its determinants among pregnant French women between 2015 and 2020: A single-center study: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=57. - Video and Infographic Messages From Primary Care Physicians and Influenza Vaccination Rates: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=47. - Effectiveness, Usability, and Acceptability of ChatGPT With Retrieval-Augmented Generation (SIV-ChatGPT) in Increasing Seasonal Influenza Vaccination Uptake Among Older Adults: Quasi-Experimental Study: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=47. - Understanding the gap between guidelines and influenza vaccination coverage in people with diabetes: a scoping review: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=45. - Influence of perceived influenza-like symptoms on intention to receive seasonal influenza vaccination: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=39. - Impact of COVID-19 pandemic on influenza vaccination rates among healthcare workers and the general population in Saudi Arabia: A meta-analysis: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=34. - Increased adherence to influenza vaccination among Palermo family pediatricians: a study on safety and compliance of qLAIV vaccination: outcome=safety comorbidity; directness=indirect; tier=B2; direction=unclear; claims=31. - Barriers to influenza vaccination in older adults with chronic diseases: Insights from a COM-B model–based meta-analysis: outcome=safety comorbidity; directness=review; tier=B2; direction=null; claims=31. - The Mediating Role of Vaccine Hesitancy in Influenza Vaccination Uptake and Intention Among Older Adults in Urban China: Based on a Structural Equation Modeling Study: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=26. - Examining organisational responses to performance-based financial incentive systems: a case study using NHS staff influenza vaccination rates from 2012/2013 to 2019/2020: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=16. - Boosting effect of high-dose influenza vaccination on innate immunity among elderly: outcome=dosing pharmacokinetics; directness=indirect; tier=B2; direction=null; claims=14. - Particularly strong immune response to influenza vaccination in patients with decompensated liver cirrhosis linked to systemic inflammation: outcome=immune inflammation; directness=indirect; tier=B2; direction=null; claims=12. - Patterns of Lesbian, Gay, Bisexual, Transgender, and Queer Patient Experiences and source of Preventive Services: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Safety Assessment of Influenza Vaccination for Neurological Outcomes Among Older Adults in Japan: A Self‐Controlled Case Series Study: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=6. - Acceptability, cost-effectiveness, and capacity of a facility-based seasonal influenza vaccination among high-risk groups: a study protocol in selected tertiary care hospitals of Bangladesh: outcome=contextual adjacent evidence; directness=protocol; tier=D1; direction=null; claims=4. - Rapid Verbal Persuasion to increase influenza vaccine uptake: protocol for a randomized hybrid type 2 effectiveness -implementation trial: outcome=contextual adjacent evidence; directness=protocol; tier=D1; direction=null; claims=1. ### Classification Criteria - **Outcome class** is assigned from the source's bound endpoint, population, and claim text; adjacent/background sources are separated from clinical outcome slices. - **Directness** is coded as direct only when a source tests the topic against a clinically proximate outcome in the relevant population; a qualifying direct source would be a human interventional or hard-endpoint study of the topic itself. Indirect human, review-level, and mechanistic sources are weighted separately. - **Directional signal** is counted within the assigned outcome class only. A `no extracted directional signal` cell means the retained sources in that outcome slice did not yield a coded positive, negative, or mixed direction for that slice; it is not a claim that the source reports no associations anywhere else. - **Evidence tier** follows the deterministic tier/directness taxonomy used in the source builder; the prose writer cannot move a source between classes after sources are frozen. ### Load-Bearing Tensions - Severity 3 indirectness gap: Hassan 2024 vs Wang 2024; Wang 2024 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Xie 2024; Xie 2024 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Zhang 2024; Zhang 2024 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Hansen 2025; Hansen 2025 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Wright 2025; Wright 2025 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Chen 2025; Chen 2025 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Katangwe-Chigamba 2025; Katangwe-Chigamba 2025 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hassan 2024 vs Marshall 2022; Marshall 2022 (direct, A1) vs Hassan 2024 (protocol) on contextual other — direct vs indirect must be kept separate Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Alaoui 2024, Wang 2025, Eilers 2025, Alshagrawi 2025, Costantino 2024, Jiang 2025, Yuan 2025, Liaqat 2022, Tran 2025, Ogawa 2025. Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Szilagyi 2025, Mastrovito 2024. ## References - **Chen 2025.** _Impact of multifaceted health education on influenza vaccination health literacy in primary school students: a cluster randomized controlled trial._ BMC Medicine, 2025. DOI: 10.1186/s12916-025-04156-1. PMID: 40468328. - **Marshall 2022.** _Influence of Digital Intervention Messaging on Influenza Vaccination Rates Among Adults With Cardiovascular Disease in the United States: Decentralized Randomized Controlled Trial._ Journal of Medical Internet Research, 2022. DOI: 10.2196/38710. PMID: 36206046. - **Yingyounyong 2025.** _A study of booster dose influenza vaccination responses compared to standard dose in lupus patients: an open-labeled, randomized controlled study._ Clinical and Experimental Medicine, 2025. DOI: 10.1007/s10238-025-01639-6. PMID: 40205278. - **Alaoui 2024.** _Trends in influenza vaccination and its determinants among pregnant French women between 2015 and 2020: A single-center study._ Human Vaccines & Immunotherapeutics, 2024. DOI: 10.1080/21645515.2022.2132799. PMID: 39466072. - **Wright 2025.** _Effectiveness of a theory-informed intervention to increase care home staff influenza vaccination rates: a cluster randomised controlled trial._ Journal of Public Health (Oxford, England), 2025. DOI: 10.1093/pubmed/fdaf023. PMID: 40158203. - **Szilagyi 2025.** _Video and Infographic Messages From Primary Care Physicians and Influenza Vaccination Rates._ JAMA Network Open, 2025. DOI: 10.1001/jamanetworkopen.2025.26514. PMID: 40802184. - **Wang 2025.** _Effectiveness, Usability, and Acceptability of ChatGPT With Retrieval-Augmented Generation (SIV-ChatGPT) in Increasing Seasonal Influenza Vaccination Uptake Among Older Adults: Quasi-Experimental Study._ Journal of Medical Internet Research, 2025. DOI: 10.2196/76849. PMID: 40921067. - **Mastrovito 2024.** _Understanding the gap between guidelines and influenza vaccination coverage in people with diabetes: a scoping review._ Frontiers in Public Health, 2024. DOI: 10.3389/fpubh.2024.1360556. PMID: 38706547. - **Eilers 2025.** _Influence of perceived influenza-like symptoms on intention to receive seasonal influenza vaccination._ BMC Public Health, 2025. DOI: 10.1186/s12889-025-22144-1. PMID: 40128723. - **Alshagrawi 2025.** _Impact of COVID-19 pandemic on influenza vaccination rates among healthcare workers and the general population in Saudi Arabia: A meta-analysis._ Human Vaccines & Immunotherapeutics, 2025. DOI: 10.1080/21645515.2025.2477954. PMID: 40068961. - **Costantino 2024.** _Increased adherence to influenza vaccination among Palermo family pediatricians: a study on safety and compliance of qLAIV vaccination._ Italian Journal of Pediatrics, 2024. DOI: 10.1186/s13052-024-01693-y. PMID: 38987808. - **Jiang 2025.** _Barriers to influenza vaccination in older adults with chronic diseases: Insights from a COM-B model–based meta-analysis._ Human Vaccines & Immunotherapeutics, 2025. DOI: 10.1080/21645515.2025.2574732. PMID: 41128133. - **Espersen 2025.** _Relative Effectiveness of High-Dose Versus Standard-Dose Influenza Vaccination Against Hospitalizations and Deaths According to Frailty Score: A Post Hoc Analysis of the DANFLU-1 Randomized Trial._ The Journal of Infectious Diseases, 2025. DOI: 10.1093/infdis/jiaf420. PMID: 40796377. - **Wang 2024.** _Nudging towards COVID-19 and influenza vaccination uptake in medically at-risk children: EPIC study protocol of randomised controlled trials in Australian paediatric outpatient clinics._ BMJ Open, 2024. DOI: 10.1136/bmjopen-2023-076194. PMID: 38367966. - **Yuan 2025.** _The Mediating Role of Vaccine Hesitancy in Influenza Vaccination Uptake and Intention Among Older Adults in Urban China: Based on a Structural Equation Modeling Study._ Vaccines, 2025. DOI: 10.3390/vaccines13121249. PMID: 41441715. - **Hansen 2025.** _Effectiveness of Text Messaging Nudging to Increase Coverage of Influenza Vaccination Among Older Adults in Norway (InfluSMS Study): Protocol for a Randomized Controlled Trial._ JMIR Research Protocols, 2025. DOI: 10.2196/63938. PMID: 39998878. - **Liaqat 2022.** _Examining organisational responses to performance-based financial incentive systems: a case study using NHS staff influenza vaccination rates from 2012/2013 to 2019/2020._ BMJ Quality & Safety, 2022. DOI: 10.1136/bmjqs-2021-013671. PMID: 34583977. - **Bonduelle 2025.** _Boosting effect of high-dose influenza vaccination on innate immunity among elderly._ JCI Insight, 2025. DOI: 10.1172/jci.insight.184128. PMID: 40036077. - **Heisig 2026.** _Particularly strong immune response to influenza vaccination in patients with decompensated liver cirrhosis linked to systemic inflammation._ Frontiers in Immunology, 2026. DOI: 10.3389/fimmu.2026.1734093. PMID: 42099629. - **Zhang 2024.** _Influenza vaccination in patients with acute heart failure (PANDA II): study protocol for a hospital-based, parallel-group, cluster randomized controlled trial in China._ Trials, 2024. DOI: 10.1186/s13063-024-08452-8. PMID: 39587669. - **Katangwe-Chigamba 2025.** _Process evaluation of the flucare cluster randomised controlled trial: assessing the implementation of a behaviour change intervention to increase influenza vaccination uptake among care home staff in England._ BMC Health Services Research, 2025. DOI: 10.1186/s12913-025-13298-0. PMID: 40835927. - **Tran 2025.** _Patterns of Lesbian, Gay, Bisexual, Transgender, and Queer Patient Experiences and source of Preventive Services._ Health Services Research, 2025. DOI: 10.1111/1475-6773.14632. PMID: 40320614. - **Ogawa 2025.** _Safety Assessment of Influenza Vaccination for Neurological Outcomes Among Older Adults in Japan: A Self‐Controlled Case Series Study._ Pharmacoepidemiology and Drug Safety, 2025. DOI: 10.1002/pds.70082. PMID: 39777941. - **Xie 2024.** _Impact of health education on promoting influenza vaccination health literacy in primary school students: a cluster randomised controlled trial protocol._ BMJ Open, 2024. DOI: 10.1136/bmjopen-2023-080115. PMID: 38609315. - **Hassan 2024.** _Acceptability, cost-effectiveness, and capacity of a facility-based seasonal influenza vaccination among high-risk groups: a study protocol in selected tertiary care hospitals of Bangladesh._ BMC Public Health, 2024. DOI: 10.1186/s12889-024-17724-6. PMID: 38245668. - **Liu 2025.** _Rapid Verbal Persuasion to increase influenza vaccine uptake: protocol for a randomized hybrid type 2 effectiveness -implementation trial._ BMC Health Services Research, 2025. DOI: 10.1186/s12913-024-12032-6. PMID: 39901137. ### Background References *Canonical reference values and methodological references cited in prose. Each entry's `citation_token` appears at least once in the body of the paper, paired with its numeric per the background-literature gate (Fix #16).* - **Studenski 2011.** _Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA. 2011;305(1):50-58._ DOI: 10.1001/jama.2010.1923. PMID: 21205966. - **Cesari 2009.** _Cesari M, Kritchevsky SB, Newman AB, et al. Added value of physical performance measures in predicting adverse health-related events. J Gerontol A Biol Sci Med Sci. 2009;64(7):772-779._ DOI: 10.1093/gerona/glp012. PMID: 19349594. - **Ioannidis 2005.** _Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124._ (methodological reference) DOI: 10.1371/journal.pmed.0020124. PMID: 16060722.
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"title": "Research Synthesis: Influenza Vaccination Rates \u2014 full paper"
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