Influenza pandemics considerably burden affected health systems due to surges in inpatient admissions and associated costs. Previous studies underestimate or overestimate 2009/2010 influenza A/H1N1 pandemic hospital admissions and costs. We robustly estimate overall and age‐specific weekly H1N1 admissions and costs between June 2009 and March 2011 across 170 English hospitals. We calculate H1N1 admissions and costs as the difference between our administrative data of all influenza‐like‐illness patients (seasonal and pandemic alike) and a counterfactual of expected weekly seasonal influenza admissions and costs established using time‐series models on prepandemic (2004–2008) data. We find two waves of H1N1 admissions: one pandemic wave (June 2009–March 2010) with 10,348 admissions costing £20.5 million and one postpandemic wave (November 2010–March 2011) with 11,775 admissions costing £24.8 million. Patients aged 0–4 years old have the highest H1N1 admission rate, and 25‐ to 44‐ and 65+‐year‐olds have the highest costs. Our estimates are up to 4.3 times higher than previous reports, suggesting that the pandemic's burden on hospitals was formerly underassessed. Our findings can help hospitals manage unexpected surges in admissions and resource use due to pandemics.

Background Nudge-based social norm messages conveying high influenza vaccination coverage levels signal a strong social norm, encouraging vaccination, but also a low risk of infection, discouraging vaccination and promoting free-riding. The complex interplay between these two signals can result in ambiguous vaccination decision-making at varying coverage levels. We aimed to measure different vaccination coverage levels' (VCLs) effect on influenza vaccination intention through an online experiment.Methods UK residents aged 18 years or older were eligible to participate in this online experiment and recruited via Prolific. They were stratified by gender and randomly assigned to a control group with no message (n=202) or one of seven treatment groups (n=1 163) with different messages of VCLs (ie, proportion of vaccinated people [10%, 25%, 50%, 65%, 75%, 85%, or 95%]) in the respondents' environment. Effect on respondents' vaccination intention was measured with self-reported intention and three elicited behaviour measures: opening an online map locating nearby private flu jab providers; time looking at this map; and downloading a calendar reminder to vaccinate. Linear regressions, probit, logistic, and double hurdle models were used, controlling for population behaviour perceptions, risk attitudes, and behavioural and socioeconomic characteristics collected through individual questionnaires.

Nudge'-based social norms messages conveying high population influenza vaccination coverage levels can encourage vaccination due to bandwagoning effects but also discourage vaccination due to free-riding effects on low risk of infection, making their impact on vaccination uptake ambiguous. We develop a theoretical framework to capture heterogeneity around vaccination behaviors, and empirically measure the causal effects of different messages about vaccination coverage rates on four self-reported and behavioral vaccination intention measures. In an online experiment, N = 1365 UK adults are randomly assigned to one of seven treatment groups with different messages about their social environment's coverage rate (varied between 10% and 95%), or a control group with no message. We find that treated groups have significantly greater vaccination intention than the control. Treatment effects increase with the coverage rate up to a 75% level, consistent with a bandwagoning effect. For coverage rates above 75%, the treatment effects, albeit still positive, stop increasing and remain flat (or even decline). Our results suggest that, at higher coverage rates, free-riding behavior may partially crowd out bandwagoning effects of coverage rate messages. We also find significant heterogeneity of these effects depending on the individual perceptions of risks of infection and of the coverage rates.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750–5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.