Bandwagoning, free-riding and heterogeneity in influenza vaccine decisions: An online experiment.

'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.

Excess influenza hospital admissions and costs due to the 2009 H1N1 pandemic in England.

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.

The COVID-19 Pandemic and In-Person Visit Rate Disruptions Among Patients With Hematologic Neoplasms in the US in 2020 to 2021.

Importance: The COVID-19 pandemic has led to a reduction in routine in-person medical care; however, it is unknown whether there have been any changes in visit rates among patients with hematologic neoplasms. Objective: To examine associations between the COVID-19 pandemic and in-person visits and telemedicine use among patients undergoing active treatment for hematologic neoplasms. Design, Setting, and Participants: Data for this retrospective observational cohort study were obtained from a nationwide electronic health record-derived, deidentified database. Data for patients with hematologic neoplasms who had received at least 1 systemic line of therapy between March 1, 2016, and February 28, 2021, were included. Treatments were categorized into 3 types: oral therapy, outpatient infusions, and inpatient infusions. The data cutoff date was April 30, 2021, when study analyses were conducted. Main Outcomes and Measures: Monthly visit rates were calculated as the number of documented visits (telemedicine or in-person) per active patient per 30-day period. We used time-series forecasting methods on prepandemic data (March 2016 to February 2020) to estimate expected rates between March 1, 2020, and February 28, 2021 (if the pandemic had not occurred). Results: This study included data for 24 261 patients, with a median age of 68 years (IQR, 60-75 years). A total of 6737 patients received oral therapy, 15 314 received outpatient infusions, and 8316 received inpatient infusions. More than half of patients were men (14 370 [58%]) and non-Hispanic White (16 309 [66%]). Early pandemic months (March to May 2020) demonstrated a significant 21% reduction (95% prediction interval [PI], 12%-27%) in in-person visit rates averaged across oral therapy and outpatient infusions. Reductions in in-person visit rates were also significant for all treatment types for multiple myeloma (oral therapy: 29% reduction; 95% PI, 21%-36%; P = .001; outpatient infusions: 11% reduction; 95% PI, 4%-17%; P = .002; inpatient infusions: 55% reduction; 95% PI, 27%-67%; P = .005), for oral therapy for chronic lymphocytic leukemia (28% reduction; 95% PI, 12%-39%; P = .003), and for outpatient infusions for mantle cell lymphoma (38% reduction; 95% PI, 6%-54%; P = .003) and chronic lymphocytic leukemia (20% reduction; 95% PI, 6%-31%; P = .002). Telemedicine visit rates were highest for patients receiving oral therapy, with greater use in the early pandemic months and a subsequent decrease in later months. Conclusions and Relevance: In this cohort study of patients with hematologic neoplasms, documented in-person visit rates for those receiving oral therapy and outpatient infusions significantly decreased during the early pandemic months but returned to close to projected rates in the later half of 2020. There were no statistically significant reductions in the overall in-person visit rate for patients receiving inpatient infusions. There was higher telemedicine use in the early pandemic months, followed by a decline, but use was persistent in the later half of 2020. Further studies are needed to ascertain associations between the COVID-19 pandemic and subsequent cancer outcomes and the evolution of telemedicine use for care delivery.

SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals.

OBJECTIVE: The COVID-19 pandemic demonstrates the need for understanding pathways to healthcare demand, morbidity, and mortality of pandemic patients. We estimate H1N1 (1) hospitalization rates, (2) severity rates (length of stay, ventilation, pneumonia, and death) of those hospitalized, (3) mortality rates, and (4) time lags between infections and hospitalizations during the pandemic (June 2009 to March 2010) and post-pandemic influenza season (November 2010 to February 2011) in England. METHODS: Estimates of H1N1 infections from a dynamic transmission model are combined with hospitalizations and severity using time series econometric analyses of administrative patient-level hospital data. RESULTS: Hospitalization rates were 34% higher and severity rates of those hospitalized were 20%-90% higher in the post-pandemic period than the pandemic. Adults (45-64-years-old) had the highest ventilation and pneumonia hospitalization rates. Hospitalizations did not lag infection during the pandemic for the young (<24-years-old) but lagged by one or more weeks for all ages in the post-pandemic period. DISCUSSION: The post-pandemic flu season exhibited heightened H1N1 severity, long after the pandemic was declared over. Policymakers should remain vigilant even after pandemics seem to have subsided. Analysis of administrative hospital data and epidemiological modelling estimates can provide valuable insights to inform responses to COVID-19 and future influenza and other disease pandemics.

Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic.

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.