The cross-sectional analysis examined survey data from 27,651 U.S. adults whose average age was 53.1 years. Fifty-five point eight percent were female, 67.4% were non-Hispanic White, 6.3% had immunosuppression and 13.8% had one or more cancers. Researchers used descriptive and inferential statistics, including logistic regression analyses, with SAS version 9.4. They used 95% confidence intervals and a P-value threshold of .05 to determine statistical significance.

Adults with one or more cancers had an adjusted odds ratio of 1.31 for reporting influenza vaccination in 2021 compared with the comparison group (95% CI, 1.19–1.45; P < .001). Adults with a weak immune status also had higher adjusted odds, with an adjusted odds ratio of 1.47 (95% CI, 1.26–1.71; P < .001). Delay in care because an appointment was not available was associated with higher odds of vaccination, with an adjusted odds ratio of 1.13 (95% CI, 1.02–1.26; P = .02).

By contrast, reporting a delay in care because no doctor would accept the respondent’s health insurance was associated with lower adjusted odds of vaccination, with an adjusted odds ratio of 0.82 (95% CI, 0.70–0.97; P = .03). These findings describe adjusted associations in survey data and do not establish that cancer, immune status or healthcare access barriers caused vaccination decisions. The study’s reported scope is the U.S. adult population represented in the 2022 National Health Interview Survey, including people with immunosuppression and cancer; the supplied report does not provide results for additional populations or seasons. The authors concluded that the identified factors warrant intensified efforts to increase vaccination rates, particularly among vulnerable adults.

Subject of Research: Determinants of influenza vaccination among U.S. adults in the 2021/2022 season

Article Title: Determinants of influenza vaccination during the 2021/2022 influenza season in the United States: interactive roles of immunosuppression, cancer diagnosis, and healthcare access barriers

Article References: Adegbile, O. E., Kanmodi, K. K., Abib, O. M., & Brooks, B. (2026). Determinants of influenza vaccination during the 2021/2022 influenza season in the United States: interactive roles of immunosuppression, cancer diagnosis, and healthcare access barriers. BMC Public Health. https://doi.org/10.1186/s12889-026-29966-7

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29966-7

Keywords: influenza vaccination, 2021/2022 influenza season, 2022 National Health Interview Survey, U.S. adults, cancer, immunosuppression, healthcare access, health insurance, logistic regression, Determinants, influenza, vaccination

The research team, led by Gail M. Rosenbaum, Amir Goren, Maheen Shermohammed, Michelle N. Meyer and Christopher F. Chabris of Geisinger’s Behavioral Insights Team, together with collaborators at Geisinger’s Department of Laboratory Medicine, MIT and the National Bureau of Economic Research, set out to test two intertwined ideas. The first was whether communicating personalized risk could serve as an effective behavior-change intervention for vaccination. The second was whether patients react differently when the risk assessment is attributed to an algorithm, or when the algorithm’s prediction is accompanied by personalized reasons, a form of what researchers call explainable AI. Both questions matter because healthcare systems are increasingly deploying machine learning models to identify high-risk patients, yet most evidence about patient attitudes comes from surveys and hypothetical vignettes rather than actual clinical behavior.

The intervention rested on a previously validated machine learning model, developed with the company Medial EarlySign, that predicts which patients face elevated risk of influenza and its complications, including cardiovascular events and worsening of chronic conditions such as diabetes. Influenza is not a trivial illness for vulnerable groups: it can trigger heart attacks, hospitalizations and death, and vaccination remains the most effective preventive measure. The model mined electronic health record data to assign each patient a risk score, and for some messages the system also generated the specific medical reasons, drawn from the patient’s own record, that contributed to the high-risk classification.

Across the three trials, registered on ClinicalTrials.gov under identifiers NCT04323137, NCT05009251 and NCT05509283, patients identified by the algorithm as high risk were randomly assigned either to receive no message at all, to receive a generic reminder encouraging flu vaccination, or to receive one of several messages informing them that they were at high risk for flu and its complications. Some of those high-risk messages mentioned that an algorithm had been used to determine the risk; others cited the personalized medical reasons behind the prediction; and still others did both or neither. The primary outcome in every trial was the same and unambiguous: whether the patient actually received a flu vaccine, verified through the health system’s records.

The results showed a consistent, if modest, behavioral effect. Among patients told they were at high risk, vaccination rates were 1.1 to 1.4 percentage points higher, a relative increase of roughly 3.3 to 5.4 percent, compared with patients who received only a simple reminder to get vaccinated. Compared with patients who received no message whatsoever, the high-risk messages raised vaccination by 1.7 to 3.5 percentage points, relative gains ranging from 3.3 to 14.7 percent depending on the trial. In a population where baseline vaccination rates varied considerably across the studies, from about 51 percent down to under 24 percent in the passive control groups, these effects are small in absolute terms but meaningful at the scale of an entire health system, where even a single percentage point represents thousands of additional vaccinated patients.

The more striking finding concerned transparency. Vaccination rates were statistically indistinguishable across message arms that disclosed algorithmic involvement and those that did not, and adding personalized explanations for the algorithm’s prediction did not change outcomes either. In other words, patients were neither averse to nor appreciative of the fact that a machine had helped determine their risk. This null result carries considerable weight because laboratory studies of so-called algorithm aversion and algorithm appreciation have produced conflicting conclusions, with some experiments showing people distrust algorithmic judgment after seeing it err, others showing people prefer it to human judgment, and research on medical AI specifically suggesting patients may resist it, particularly for subjective or identity-relevant judgments.

Why the discrepancy between the lab and the field? The authors’ design offers some clues. In a realistic healthcare context, the message patients received was embedded in an ongoing relationship with a trusted health system, and the actionable recommendation, getting a flu shot, was low-cost, familiar and clearly beneficial. Attitudes measured in hypothetical scenarios may simply fail to predict behavior when the stakes are concrete and the decision is easy. The team also examined whether the number of personalized risk reasons included in a message mattered, and whether patients’ actual underlying risk level, not just the risk level communicated to them, moderated the effect, analyses reported in extensive supplementary materials covering dozens of tables and figures.

The trials were methodologically rigorous by the standards of the nudge literature. All three were preregistered, with power analyses conducted in advance; the studies were designed to detect effects of around two percentage points, consistent with effect sizes typical of large-scale vaccination nudges. The research team posted de-identified data and analysis code publicly on the Open Science Framework, and the study protocols and statistical analysis plans are available through the trial registrations. A fourth study was also conducted and is described in the supplementary materials. The work was supported in part by the National Institute on Aging of the National Institutes of Health under award number P30AG034532, though the funders had no role in study design, analysis or the decision to publish.

The findings arrive amid a broader wave of large-scale field experiments on vaccination messaging, including megastudies of text-message nudges in pharmacies and health systems, electronic nudges delivered through national registries in Denmark and Finland, and trials of financial incentives for COVID-19 vaccination. Those studies have shown that simple, well-timed reminders can reliably move vaccination rates, but also that effects vary across contexts and populations, and that some nudges fail when scaled. The new results add a distinctive twist: the content that moves people is not the technology behind the message but the personal information it conveys. What mattered was telling patients something true and relevant about their own bodies, not whether that information came from a human clinician or a statistical model.

For health systems weighing how to deploy predictive algorithms, the practical implications are twofold. First, algorithmically identified high-risk patients represent a tractable target for outreach, and informing them of their elevated risk produces a small but reliable increase in vaccination beyond standard reminders. Second, at least for this kind of low-stakes, clearly beneficial intervention, fears that patients will recoil from algorithmic involvement appear overstated, and investing in elaborate explainability features may not be necessary to preserve patient trust. The authors caution that the results come from one US health system and one preventive behavior, so generalization to more consequential or contested medical decisions remains an open question. But as a demonstration that personalized, algorithm-assisted risk communication can change real health behavior at scale, without triggering the aversion that laboratory studies predicted, the study marks an important step toward evidence-based deployment of medical AI in everyday care.

Subject of Research: Algorithm-assisted personalized risk communication to increase influenza vaccination

Article Title: Algorithm-assisted personalized risk communication to encourage flu vaccination in the USA: three randomized field trials

Article References: Rosenbaum, G. M., Goren, A., Shermohammed, M., Wolk, D. M., Tice, A. M., Doyle, J. J., Jr., Meyer, M. N., & Chabris, C. F. (2026). Algorithm-assisted personalized risk communication to encourage flu vaccination in the USA: three randomized field trials. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02603-4

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02603-4

Keywords: artificial intelligence, influenza vaccination, randomized controlled trials, risk communication, algorithm aversion, explainable AI, health behavior, nudges, machine learning, preventive medicine, vaccine hesitancy, Nature Human Behaviour

At the molecular level, the circulating strain belongs to the family Orthomyxoviridae and carries a negative-sense, single-stranded, segmented RNA genome that encodes the structural and non-structural proteins required for its replication cycle. The two primary surface glycoproteins, hemagglutinin and neuraminidase, govern the critical steps of host cell attachment, viral entry, and release of progeny virions. Hemagglutinin binds to alpha-2,6-linked sialic acid receptors, which are abundantly expressed on human upper respiratory epithelial cells, anchoring the virus to its preferred portal of entry. Once attached, the virion is taken up by receptor-mediated endocytosis, and the low pH of the endosome triggers a conformational change in hemagglutinin that fuses the viral envelope with the endosomal membrane. This allows uncoating of the viral ribonucleoprotein complexes and their import into the host cell nucleus, where the viral RNA-dependent RNA polymerase executes replication and transcription. Neuraminidase then cleaves terminal sialic acid residues from the host cell surface, preventing newly formed virions from aggregating and permitting efficient budding and dissemination throughout the respiratory tract.

Environmental conditions in the National Capital Region play a decisive role in shaping transmission dynamics, and they differ sharply from the patterns seen in temperate climates. Rather than producing a single crisp winter peak, northern India experiences seasonal viral persistence predisposed by high ambient humidity, monsoon precipitation, and subsequent winter thermal inversions that trap polluted air close to the ground. High humidity and poor air circulation in enclosed spaces facilitate the stability and transport of fine micro-aerosols alongside the larger respiratory droplets generated by coughing, sneezing, and even vocalization. Indoor crowding during spells of inclement weather increases close-contact exposure, while contaminated environmental surfaces serve as secondary vectors for indirect mucosal self-inoculation when people touch their faces after contact with contaminated fomites. Following exposure, the virus exhibits an incubation period of roughly one to four days, a window during which asymptomatic or presymptomatic viral shedding can seed community transmission chains well before the first overt clinical presentation, complicating efforts to contain spread through symptom-based screening alone.

The clinical spectrum of A(H1N1) infection ranges from mild, uncomplicated upper respiratory illness to fulminant, life-threatening systemic disease. Typical uncomplicated cases present with abrupt-onset fever, non-productive cough, pharyngeal erythema, nasal congestion, severe retro-orbital or generalized headache, diffuse myalgia, and profound constitutional fatigue. Pediatric cohorts frequently show prominent gastrointestinal involvement, including nausea, vomiting, and diarrhea, layered on top of systemic febrile illness. In a subset of patients, viral tropism for the lower respiratory tract precipitates rapid tissue damage, characterized by necrotizing tracheobronchitis, diffuse alveolar damage, impaired gas exchange, and eventual acute respiratory distress syndrome. Viral destruction of the respiratory epithelium also strips away a critical innate barrier, leaving the lower airways highly vulnerable to secondary bacterial superinfections, most notably those caused by Streptococcus pneumoniae, Staphylococcus aureus, and Streptococcus pyogenes. These bacterial complications historically account for a substantial share of influenza-related morbidity and mortality, which is why clinicians are urged to watch for clinical deterioration beyond the expected viral course.

Identifying high-risk populations is central to early intervention and rational allocation of scarce clinical resources. Pregnant women represent an exceptionally vulnerable cohort because physiological alterations in cell-mediated immunity, decreased chest wall compliance, and elevated maternal oxygen consumption collectively heighten the risk of rapid pulmonary decompensation. The extreme age groups face elevated danger as well: infants under five years experience heightened morbidity because of immature immune responses, while adults aged sixty-five and older are compromised by immunosenescence, the gradual decline of immune function with age. Individuals with underlying chronic comorbidities, including diabetes mellitus, chronic obstructive pulmonary disease, bronchial asthma, congestive heart failure, chronic renal insufficiency, hepatic dysfunction, neurological disorders, morbid obesity, and active immunosuppression, face significantly higher rates of hospitalization, intensive care unit admission, and death. For these groups, clinicians emphasize that even seemingly mild respiratory symptoms early in an influenza wave warrant a low threshold for testing and antiviral treatment.

Clinical monitoring during the surge must emphasize rapid detection of red-flag indicators that signal acute physiological deterioration. In adults, key warning signs requiring immediate emergency evaluation include progressive dyspnea, tachypnea, persistent central chest pressure, low peripheral oxygen saturation, central cyanosis, acute alteration of mental status, persistent high fever unresponsive to antipyretics, or a clinical relapse following transient symptom resolution. That last pattern, a biphasic worsening after apparent improvement, is particularly concerning because it often heralds secondary bacterial pneumonia. In pediatric patients, critical warning indicators include intercostal retractions, grunting, nasal flaring, lethargy, poor oral fluid intake leading to dehydration, persistent irritability, and breakthrough febrile seizures. Prompt recognition of these signs facilitates timely admission to higher-level care facilities capable of providing advanced respiratory support, including oxygen therapy and mechanical ventilation, which can be decisive in severe lower respiratory tract disease.

Definitive clinical management, the analysis stresses, relies on rigorous diagnostic testing and targeted pharmacological therapy rather than empirical broad-spectrum interventions. Reverse transcription-polymerase chain reaction remains the gold standard diagnostic technique for hospitalized or severe cases, enabling accurate differentiation of H1N1 from co-circulating respiratory pathogens such as SARS-CoV-2, respiratory syncytial virus, adenovirus, and bacterial pneumonia. This differential matters because treatment pathways diverge sharply: antivirals benefit influenza, whereas antibacterial agents are clinically ineffective against a primary viral pathogen and contravene global antimicrobial stewardship principles when used empirically. The authors are emphatic that antibiotics should be strictly reserved for cases with confirmed or highly suspected secondary bacterial co-infections, a discipline that protects both individual patients from unnecessary drug exposure and the broader community from accelerating antimicrobial resistance, an already serious problem in South Asian healthcare settings.

On the therapeutic front, neuraminidase inhibitors, particularly oral oseltamivir, form the cornerstone of antiviral therapy for patients with severe, progressive, or complicated disease, as well as for all high-risk individuals presenting with respiratory illness during the surge. Timing is critical: antiviral treatment provides optimal benefit when initiated within 48 hours of symptom onset, significantly dampening viral replication, shortening the duration of illness, and lowering the incidence of severe complications. Meta-analytic evidence from hospitalized H1N1pdm09 cohorts has linked early neuraminidase inhibitor use with reduced mortality, reinforcing the case for prompt empirical treatment of high-risk patients even before laboratory confirmation returns. Because the drug targets the neuraminidase protein rather than the hemagglutinin antigen that drifts seasonally, it retains activity against the A/Missouri/11/2025-like variant now circulating in India, providing a reliable pharmacological backstop while vaccination programs catch up.

Prevention, ultimately, remains the most cost-effective strategy, and the authors outline a comprehensive, layered framework. Annual seasonal influenza vaccination sits at its core, with the current vaccine exhibiting strong antigenic concordance with the circulating A(H1N1)pdm09 clade, a consequence of the World Health Organization’s strain selection for the 2025-2026 Northern Hemisphere season. Around that pharmacological anchor, the framework integrates non-pharmaceutical interventions: home isolation of symptomatic individuals, frequent hand hygiene, respiratory etiquette, enhanced indoor ventilation, and the use of well-fitted N95 respirators in high-density environments such as public transport, markets, and healthcare facilities. For a densely populated tropical and subtropical megaregion where monsoon humidity and winter thermal inversions prolong viral viability, these measures collectively reduce the effective reproduction number of the virus. The 2026 surge, the analysis concludes, is a reminder that seasonal influenza remains a formidable and recurring public health threat, but one whose trajectory can be substantially blunted by surveillance, vaccination, judicious antiviral use, and disciplined antimicrobial stewardship.

Subject of Research: The 2026 seasonal resurgence of influenza A(H1N1)pdm09 in India and evidence-based strategies for its prevention and clinical management

Article Title: H1N1 seasonal surge in India, 2026: Emerging public health threat and evidence based preventive strategies

Article References: Choudhary, O. P., & Choudhary, P. (2026). H1N1 seasonal surge in India, 2026: Emerging public health threat and evidence based preventive strategies. New Microbes and New Infections, 73, Article 101836. https://doi.org/10.1016/j.nmni.2026.101836

Image Credits: AI Generated

DOI: 10.1016/j.nmni.2026.101836

Keywords: H1N1, influenza A(H1N1)pdm09, India, Delhi-NCR, seasonal influenza, oseltamivir, neuraminidase inhibitors, influenza vaccination, RT-PCR, respiratory transmission, public health surveillance, antimicrobial stewardship