Research Synthesis: Influenza Vaccination Rates

Interpretation below therefore separates primary clinical-trial evidence from review-level, preclinical, and other indirect evidence.

Abstract

This synthesis tests the thesis that evidence for Influenza Vaccination Rates is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation.

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. 17/26 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 included source papers and 776 high-confidence extracted claims.

The evidence profile contains 9 direct clinical sources, 17 adjacent clinical sources, and no sources classified primarily as mechanistic or model-system evidence, with a high-density pairwise disagreement map across the evidence base.

No single positive outcome class dominates the retained corpus; null signals cluster in the contextual adjacent evidence, safety and comorbidity, deficiency prevalence 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 should be treated as a bounded geroscience hypothesis: the retained clinical and adjacent evidence profile defines the scope for targeted testing, while mixed and null findings limit any unqualified anti-aging claim.

Results

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.

Evidence domain	Corpus slice	Strongest signal	Directness	Main limitation
Contextual Adjacent Evidence	n=18; claims=518	no extracted directional signal in 18/18 sources	7 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
Population / prevalence	n=1; claims=74	no extracted directional signal in 1/1 sources	1 indirect	single-source slice; hypothesis-generating
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

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

18 included sources were assigned to this outcome class. Directional coding: null=18. Directness coding: direct=7, 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.

Population / prevalence Outcomes

1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1.

Dose / exposure 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 corpus assembled for this synthesis reflects the trade-off between a 55-candidate intake pool and a 26-paper analytic set, and the resulting gaps have direct consequences for the headline conclusions. The aspirational 70% Healthy benchmark cited in Szilagyi 2025 is therefore addressable here only as an external target, not as an internal comparator, and any claim of convergence on that threshold from this corpus is unsupported.

Population specificity constrains the external validity of the corpus more than the prevalence figures suggest. Low- and middle-income country representation outside China and Saudi Arabia is thin, the LGBTQ+ preventive-services subgroup is captured only in Tran 2025, and pediatric, pregnant, and immunocompromised-but-non-SLE populations are essentially absent. Generalization beyond the enrolled populations — for example, to Sub-Saharan African, South Asian, or Pacific-Islander adults — is not supported.

Endpoint coverage is uneven and several clinically consequential outcomes are simply not measured within the admitted set. No source in the 26-paper corpus reports confirmed laboratory-attributable influenza incidence, hospitalization for influenza or pneumonia, intensive-care utilization, mortality, or cost-effectiveness as a primary endpoint, and the three D1/Protocol sources — Hassan 2024, Liu 2025, Zhang 2024 — by construction contribute only design and anticipated effect-size information rather than outcome data. Liu 2025 anticipates a roughly 10% absolute increase in vaccination uptake from rapid verbal persuasion, but this is a prespecified estimate, not an observed effect. The four safety comorbidity and immune/immune inflammation sources (Costantino 2024, Jiang 2025, Ogawa 2025, Heisig 2026) capture adherence and short-window adverse-event signal rather than the hard outcomes that would adjudicate net clinical benefit. Ioannidis 2005 reminds readers that surrogate associations do not guarantee hard-outcome validity, a caution that applies directly here: the proxy outcomes available cannot, on their own, support inferences about hospitalization or mortality reduction.

A mechanism-to-clinic gap runs through several claims that are otherwise clinically attractive. Until mechanism-bearing sources are paired with hard-outcome trials in the same population, mechanistic findings should be treated as hypothesis-generating, not as evidence of clinical benefit.

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 7 outcome classes and a high-density pairwise disagreement map. 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 vaccination case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established.

The strongest unresolved contrast is the indirectness gap between Hassan 2024 and Xie 2024 on contextual adjacent evidence (severity 3/5), which defines the boundary condition future studies must test rather than smooth over.

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
deficiency prevalence	0	1	null	direct interventional hard-endpoint 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	7	11	null	replication gap

Evidence-Gap Priority

Priority	Gap	Rationale
P1	frailty: replication gap	1 direct and 0 indirect source; direction profile: null
P2	deficiency prevalence: direct interventional hard-endpoint gap	0 direct and 1 indirect source; direction profile: null
P3	dosing and pharmacokinetics: direct interventional hard-endpoint gap	0 direct and 1 indirect source; direction profile: null
P4	immune and inflammation: replication gap	1 direct and 0 indirect source; direction profile: unclear
P5	immune and inflammation: direct interventional hard-endpoint gap	0 direct and 1 indirect source; direction profile: null

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.
• 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.
• Xie 2026; tier=B2; directness=indirect; endpoint=deficiency prevalence; 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.

• Chen 2025: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=69.
• Marshall 2022: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=69.
• Yingyounyong 2025: outcome=immune; directness=direct; tier=A1; direction=unclear; claims=59.
• Wright 2025: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=49.
• Espersen 2025: outcome=frailty; directness=direct; tier=A1; direction=null; claims=31.
• Hansen 2025: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=18.
• Katangwe-Chigamba 2025: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=9.
• Zhang 2024: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=9.
• Xie 2024: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=5.
• Xie 2026: outcome=deficiency prevalence; directness=indirect; tier=B2; direction=null; claims=74.
• Szilagyi 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=47.
• Wang 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=47.
• Mastrovito 2024: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=45.
• Eilers 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=39.
• Alshagrawi 2025: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=34.
• Costantino 2024: outcome=safety comorbidity; directness=indirect; tier=B2; direction=unclear; claims=31.
• Jiang 2025: outcome=safety comorbidity; directness=review; tier=B2; direction=null; claims=31.
• Yuan 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=26.
• Andrew 2004: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=24.
• Liaqat 2022: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=16.
• Bonduelle 2025: outcome=dosing pharmacokinetics; directness=indirect; tier=B2; direction=null; claims=14.
• Heisig 2026: outcome=immune inflammation; directness=indirect; tier=B2; direction=null; claims=12.
• Tran 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7.
• Ogawa 2025: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=6.
• Hassan 2024: outcome=contextual adjacent evidence; directness=protocol; tier=D1; direction=null; claims=4.
• Liu 2025: 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 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
• Severity 3 indirectness gap: Xie 2024 vs Mastrovito 2024; Xie 2024 (direct, A1) vs Mastrovito 2024 (review) on contextual other — direct vs indirect must be kept separate

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-2026-06-24T16-33-43Z-R2.

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: 2026-06-24.

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 178 records in the receipt-candidate union, 58 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	178
Classified source candidates	58
No extractable claims	0
None-only claim binding	2
Mixed partial-or-none claim-binding candidates	38
Partial-only claim-binding candidates	0
Strict high-confidence sources	0
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, deficiency prevalence, dosing and pharmacokinetics, frailty, 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.

References

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• 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.
• 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.
• 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.
• Andrew 2004. Rates of influenza vaccination in older adults and factors associated with vaccine use: A secondary analysis of the Canadian Study of Health and Aging. BMC Public Health, 2004. DOI: 10.1186/1471-2458-4-36. PMID: 15306030.
• 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

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).

• 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.