Evaluating infectious disease outbreak potential and mitigation effectiveness on cruise ships.

The cruise ship sector is a major part of the tourism industry, and an estimated over 30 million passengers are transformed worldwide each year. Cruise ships bring diverse populations into proximity for many days, facilitating the transmission of respiratory illnesses. The objective of this study is to develop a modeling framework to inform the development of viable disease risk management policies and measures to control disease outbreaks on cruises. Our model, parameterized and calibrated using the data of the COVID-19 outbreak on the Diamond Princess cruise ship in 2020, is used to assess the impact of the mitigation measures such as mask wearing, vaccination, on-board and pre-traveling testing measures. Our results indicate mask wearing in public places as the cheapest and most affordable measure can drop the number of cumulative confirmed cases by almost 50%. This measure along with the vaccination by declining the number of the cumulative confirmed cases by more than 94% is the most effective measure to control outbreaks on cruises. According to our findings, outbreaks are more predominant in the passenger population than the crew members, however, the protection measures are more beneficial if they are applied by both crew members and passengers. Regarding the testing measure, pre-traveling testing is more functional than the on-board testing to control outbreaks on cruises.

Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights.

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so-called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use USA county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Modelling disease mitigation at mass gatherings: A case study of COVID-19 at the 2022 FIFA World Cup.

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.

Regional Disparities in the Uptake of Differentiated Influenza Vaccines in the United States.

Significant racial/ethnic inequities in the uptake of differentiated influenza vaccines (DIVs) have been previously reported, though less is known about regional disparities. We conducted a retrospective longitudinal study (2014/15-2017/18 influenza seasons) among privately insured adults aged 65 + years in the US. The exposure was the beneficiary's area of residence (US Census Bureau division) and the outcome was the type of influenza vaccine: differentiated (high-dose [HDV], adjuvanted, recombinant, and cell-based) versus conventional standard-dose egg-based. Multilevel logistic regression modeling, guided by a causal diagram, was used to assess the influence of socio-demographics, medical, healthcare utilization, community, and vaccinator characteristics in confounding or mediating regional disparities. Among those vaccinated in physician offices, beneficiaries in the East North Central region were twice as likely to receive a DIV vs those in the South Atlantic, whereas those in the East and West South Central were least likely. Disparities became more pronounced in models adjusted for individual and community characteristics, suggesting that crude uptake estimates understate the true magnitude of disparities. A vaccinator's previous HDV use was most influential in explaining regional differences. Similar but less pronounced patterns emerged for vaccinations in pharmacies/facilities. Regional disparities remained even in fully adjusted models, pointing to currently poorly understood factors that may include quality of healthcare, client health literacy and engagement, and other political and cultural factors.

Development of an early alert model for pandemic situations in Germany.

The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead. In both cases, F1 scores were above 98% and 97%, respectively, hence demonstrating the potential of using digital traces for building an early alert system for pandemics in Germany.

Estimated health and economic impact of using high-dose influenza vaccine on respiratory and circulatory plus respiratory hospitalizations of older adults in Australia.

Background: Standard dose influenza vaccine provides moderate protection from infection, but with lower effectiveness among the elderly. High dose and adjuvanted vaccines (HD-TIV and aTIV) were developed to address this. This study aims to estimate the incremental health and economic impact of using HD-TIV (high dose trivalent vaccine) instead of aTIV (adjuvanted trivalent vaccine) on respiratory and circulatory plus respiratory hospitalizations of older people (≥65 years) in Australia. Methods: This is a modelling study comparing predicted hospitalization outcomes in people receiving HD-TIV or aTIV during an average influenza season in Australia. Hospitalization records of Australian adults ≥65 years of age from 01 April to 30 November during 15 influenza seasons (2002-2017 excluding 2009, which was a pandemic) were extracted from the Australian Institute of Health and Welfare [AIHW] and used to calculate hospitalisation rates during an average season. Relative vaccine effectiveness data for aTIV and HD-TIV were used to estimate morbidity burden related to influenza. Results: Between 2002 and 2017, the average respiratory hospitalization rate among older people during influenza season (April-November) was 3,445/100,000 population-seasons, with an average cost of AU$ 7,175 per admission. The average circulatory plus respiratory hospitalization rate among older Australian people during that time was 10,393/100,000 population-seasons, with an average cost of AU$ 7829 per admission. For older Australians, HD-TIV may avert an additional 6,315-9,410 respiratory admissions each year, with an incremental healthcare cost saving of AU$ 15.9-38.2 million per year compared to aTIV. Similar results were also noted for circulatory plus respiratory hospitalizations. Conclusions: From the modelled estimations, HD-TIV was associated with less economic burden and fewer respiratory, and circulatory plus respiratory hospitalizations than aTIV for older Australians.

Workplace absenteeism due to COVID-19 and influenza across Canada: A mathematical model.

The continual distress of COVID-19 cannot be overemphasized. The pandemic economic and social costs are alarming, with recent attributed economic loss amounting to billions of dollars globally. This economic loss is partly driven by workplace absenteeism due to the disease. Influenza is believed to be a culprit in reinforcing this phenomenon as it may exist in the population concurrently with COVID-19 during the influenza season. Furthermore, their joint infection may increase workplace absenteeism leading to additional economic loss. The objective of this project will aim to quantify the collective impact of COVID-19 and influenza on workplace absenteeism via a mathematical compartmental disease model incorporating population screening and vaccination. Our results indicate that appropriate PCR testing and vaccination of both COVID-19 and seasonal influenza may significantly alleviate workplace absenteeism. However, with COVID-19 PCR testing, there may be a critical threshold where additional tests may result in diminishing returns. Regardless, we recommend on-going PCR testing as a public health intervention accompanying concurrent COVID-19 and influenza vaccination with the added caveat that sensitivity analyses will be necessary to determine the optimal thresholds for both testing and vaccine coverage. Overall, our results suggest that rates of COVID-19 vaccination and PCR testing capacity are important factors for reducing absenteeism, while the influenza vaccination rate and the transmission rates for both COVID-19 and influenza have lower and almost equal affect on absenteeism. We also use the model to estimate and quantify the (indirect) benefit that influenza immunization confers against COVID-19 transmission.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Public health impact and cost-effectiveness of implementing a 'pre-vaccination screening' strategy with the dengue vaccine in Puerto Rico.

BACKGROUND: The World Health Organization (WHO) recommended 'pre-vaccination screening' as its preferred implementation strategy when using the licensed dengue vaccine (CYD-TDV; Dengvaxia, Sanofi), so that only individuals with previous dengue infection are vaccinated. The US Centers for Disease Control and Prevention (CDC) recommended use of CYD-TDV to prevent dengue in children with previous laboratory-confirmed dengue infection in regions where dengue is endemic. Here, we evaluate the public health impact and cost-effectiveness of a 'pre-vaccination screening' strategy in Puerto Rico. METHODS: The current analysis builds upon a previously published transmission model used to assess the benefits/risks associated with dengue vaccination. For 'pre-vaccination screening', three alternative testing methods were assessed: one using an immunoglobulin G (IgG) enzyme-linked immunosorbent assay (ELISA) dengue serotest, another with dengue serotesting using a rapid diagnostic test (RDT), and one using both sequentially (as recommended in Puerto Rico). The time horizon considered was 10 years. RESULTS: In Puerto Rico, the disability-adjusted life years (DALYs) averted for 'pre-vaccination screening' with an ELISA-based program, RDT-based program, and both sequentially would be a median 1,192 (95% CI: 716-2,232), 2,812 (95% CI: 1,579-5,019), and 1,017 (95% CI: 561-1,738), respectively. These benefits would arise from the reduction in cases: median 24,961 (95% CI: 17,480-36,782), 58,273 (95% CI: 40,729-84,796), 20,775 (95% CI: 14,637-30,374) fewer cases, respectively. The cost per DALY averted from a payer perspective would be US$12,518 (95 %CI: US$4,749-26,922), US$10,047 (95% CI: US$3,350-23,852), and US$12,334 (95% CI: US$4,965-26,444), respectively. All three strategies would be cost saving from a societal perspective. CONCLUSIONS: Our study supports the WHO and CDC 'pre-vaccination screening' guidance for CYD-TDV implementation. In Puerto Rico, regardless of the testing strategy and even with a relatively low rate of testing, it would be cost-effective from a payer perspective and cost saving from a societal perspective.

What explains racial/ethnic inequities in the uptake of differentiated influenza vaccines?

We investigated the role of individual, community and vaccinator characteristics in mediating racial/ethnic disparities in the uptake of differentiated influenza vaccines (DIVs; including high-dose, adjuvanted, recombinant and cell-based vaccines). We included privately-insured (commercial and Medicare Advantage) ≥65 years-old community-dwelling health plan beneficiaries in the US with >1 year of continuous coverage and who received ≥1 influenza vaccine during the study period (July 2014-June 2018). Of 2.8 million distinct vaccination claims, 60% were for DIVs; lower if received in physician offices (49%) compared to pharmacies/facilities (74%). Among those vaccinated in physician offices, non-whites had lower odds of receiving a DIV if they lived in a non-minority county (0.77;95%CI 0.75-0.80) and even lower odds if they lived in a minority county (0.62;0.60-0.63). Differences in education, household income, medical history, community and vaccinator characteristics did not fully explain the disparities. Similar patterns emerged for vaccinations in pharmacies/facilities, although disparities disappeared altogether after controlling for socio-economic and vaccinator characteristics. When vaccinated in physician offices, minority county residents were less likely to receive a DIV, especially for non-whites (0.72;0.67-0.78). These disparities disappeared for whites, but not for non-whites, after controlling for community and vaccinator characteristics. We found an alarming level of inequity in DIV vaccine uptake among fully insured older adults that could not be fully explained by differences in sociodemographic, medical, community, and vaccinator characteristics. New strategies are urgently needed to address these inequities.

Estimating social contacts in mass gatherings for disease outbreak prevention and management: case of Hajj pilgrimage.

BACKGROUND: Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. METHODS: We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram. RESULTS: The simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site. CONCLUSIONS: This study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events.

Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world.

BACKGROUND: The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic. METHODS: In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each region's SARS-COV-2 transmission dynamic. RESULTS: We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPI's, over and above the ones identified in i) and ii). CONCLUSION: In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPI's) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV.

Disease transmission and mass gatherings: a case study on meningococcal infection during Hajj.

BACKGROUND: Mass gatherings can not only trigger major outbreaks on-site but also facilitate global spread of infectious pathogens. Hajj is one of the largest mass gathering events worldwide where over two million pilgrims from all over the world gather annually creating intense congestion. METHODS: We developed a meta-population model to represent the transmission dynamics of Neisseria meningitidis and the impact of Hajj pilgrimage on the risk of invasive meningococcal disease (IMD) for pilgrims population, local population at the Hajj site and country of origin of Hajj pilgrims. This model was calibrated using data on IMD over 17 years (1995-2011) and further used to simulate potential changes in vaccine policy and endemic conditions. RESULTS: The effect of increased density of contacts during Hajj was estimated to generate a 78-fold increase in disease transmission that impacts not only pilgrims but also the local population. Quadrivalent ACWY vaccination was found to be very effective in reducing the risk of outbreak during Hajj. Hajj has more limited impact on IMD transmission and exportation in the pilgrim countries of origin, although not negligible given the size of the population considered. CONCLUSION: The analysis performed highlighted the amplifying effect of mass gathering on N. meningitidis transmission and confirm vaccination as a very effective preventive measure to mitigate outbreak risks.

Influenza vaccine in chronic obstructive pulmonary disease among elderly male veterans.

BACKGROUND: Prior studies have established those elderly patients with chronic obstructive pulmonary disease (COPD) are at elevated risk for developing influenza-associated complications such as hospitalization, intensive-care admission, and death. This study sought to determine whether influenza vaccination could improve survival among elderly patients with COPD. MATERIALS/METHODS: This study included Veterans (age ≥ 65 years) diagnosed with COPD that received care at the United States Veterans Health Administration (VHA) during four influenza seasons, from 2012-2013 to 2015-2016. We linked VHA electronic medical records and Medicare administrative files to Centers for Disease Control and Prevention National Death Index cause of death records as well as influenza surveillance data. A multivariable time-dependent Cox proportional hazards model was used to compare rates of mortality of recipients of influenza vaccination to those who did not have records of influenza vaccination. We estimated hazard ratios (HRs) adjusted for age, gender, race, socioeconomic status, comorbidities, and healthcare utilization. RESULTS: Over a span of four influenza seasons, we included 1,856,970 person-seasons of observation where 1,199,275 (65%) had a record of influenza vaccination and 657,695 (35%) did not have a record of influenza vaccination. After adjusting for comorbidities, demographic and socioeconomic characteristics, influenza vaccination was associated with reduced risk of death during the most severe periods of influenza seasons: 75% all-cause (HR = 0.25; 95% CI: 0.24-0.26), 76% respiratory causes (HR = 0.24; 95% CI: 0.21-0.26), and 82% pneumonia/influenza cause (HR = 0.18; 95% CI: 0.13-0.26). A significant part of the effect could be attributed to "healthy vaccinee" bias as reduced risk of mortality was also found during the periods when there was no influenza activity and before patients received vaccination: 30% all-cause (HR = 0.70; 95% CI: 0.65-0.75), 32% respiratory causes (HR = 0.68; 95% CI: 0.60-0.78), and 51% pneumonia/influenza cause (HR = 0.49; 95% CI: 0.31-0.78). However, as a falsification study, we found that influenza vaccination had no impact on hospitalization due to urinary tract infection (HR = 0.97; 95% CI: 0.80-1.18). CONCLUSIONS: Among elderly patients with COPD, influenza vaccination was associated with reduced risk for all-cause and cause-specific mortality.

"Hot-spotting" to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory.

Vaccinating individuals with more exposure to others can be disproportionately effective, in theory, but identifying these individuals is difficult and has long prevented implementation of such strategies. Here, we propose how the technology underlying digital contact tracing could be harnessed to boost vaccine coverage among these individuals. In order to assess the impact of this "hot-spotting" proposal we model the spread of disease using percolation theory, a collection of analytical techniques from statistical physics. Furthermore, we introduce a novel measure which we call the efficiency, defined as the percentage decrease in the reproduction number per percentage of the population vaccinated. We find that optimal implementations of the proposal can achieve herd immunity with as little as half as many vaccine doses as a non-targeted strategy, and is attractive even for relatively low rates of app usage.

Large-scale frequent testing and tracing to supplement control of Covid-19 and vaccination rollout constrained by supply.

Non-pharmaceutical interventions (NPI) were implemented all around the world in the fight against COVID-19: Social distancing, shelter-in-place, mask wearing, etc. to mitigate transmission, together with testing and contact-tracing to identify, isolate and treat the infected. The majority of countries have relied on the former measures, followed by a ramping up of their testing and tracing capabilities. We present here the cases of South Korea, Italy, Canada and the United States, as a look back to lessons that can be drawn for controlling the pandemic, specifically through the means of testing and tracing. By fitting a disease transmission model to daily case report data in each of the four countries, we first show that their combination of social-distancing and testing/tracing have had a significant impact on the evolution of their first wave of pandemic curves. We then consider the hypothetical scenario where the only NPI measures implemented past the first pandemic wave consisted of isolating individuals due to repeated, country-scale testing and contact tracing, as a mean of lifting social distancing measures without a resurgence of COVID-19. We give estimates on the average isolation rates needed to occur in each country. We find that testing and tracing each individual of a country, on average, every 4.5 days (South Korea), 5.7 days (Canada), 6 days (Italy) and 3.5 days (US), would have been sufficient to mitigate their second pandemic waves. We also considered the situation in Canada to see how a frequent large-scale asymptomatic testing and contact tracing could have been used in combination with vaccination rollout to reduce the infection in the population. This could offer an alternative approach towards preventing and controlling an outbreak when vaccine supply is limited, while testing capacity has been increasingly enhanced.

Economic evaluation of high-dose inactivated influenza vaccine in adults aged ≥65 years: A systematic literature review.

INTRODUCTION: Due to waning immunity, adults aged ≥65 years are at increased risk of seasonal influenza infection and its complications. Adding to this risk, older adults have reduced responses to influenza vaccines. A high-dose trivalent inactivated influenza vaccine (Fluzone High-Dose, Sanofi Pasteur) (IIV3-HD) was developed to improve protection against influenza in adults aged ≥65 years and has been licensed in the US since 2009 and in Canada since 2015. Post-licensure studies have shown that IIV3-HD is more effective than standard-dose trivalent inactivated influenza vaccine (IIV3-SD) at protecting against influenza infection in this population. Here, we performed a systematic review of economic analyses of IIV3-HD in adults aged ≥65 years. METHODS: On June 9, 2019, using the Ovid search platform, we searched Econlit, Embase, and Ovid MEDLINE® for original studies published in peer-reviewed journals examining the economics or cost-effectiveness of IIV3-HD in adults aged ≥65 years. Two reviewers independently selected studies and assessed their quality. RESULTS: Seven studies were selected, all performed in the US or Canada. Five studies were funded by IIV3-HD manufacturer, and the remaining two by the US National Institute of General Medical Sciences. In all studies, IIV3-HD reduces healthcare resource utilization and is cost-effective or cost-saving compared to IIV3-SD. The main driver is reduced hospitalizations for cardiorespiratory events. CONCLUSION: IIV3-HD is cost-saving or cost-effective versus IIV3-SD in adults aged ≥65 years. Reduced cardiorespiratory complications are an important driver of these economic benefits. A video summary of the article can be accessed via the Supplementary data link at the end of this article.

Estimating public health and economic benefits along 10 years of Fluzone® High Dose in the United States.

OBJECTIVE: The objective of this study was to estimate the public health and economic benefits (from a Medicare perspective) of Fluzone High-Dose® in the US elderly population, since its introduction in 2010. METHODS: A budget impact model was developed using a decision tree framework and applied over 9 influenza seasons (2010/11 to 2018/19). The decision tree model was designed to capture influenza cases, hospitalizations possibly related to influenza or laboratory confirmed influenza, and influenza-related deaths. The analysis included influenza vaccines recommended by ACIP since 2010: SD TIV (trivalent), SD QIV (quadrivalent), HD TIV, aTIV (adjuvanted), ccQIV (cell-cultured). Two strategies were compared to evaluate the impact of HD TIV: a 'with HD TIV' strategy representative of the US vaccine landscape, and a 'without HD TIV' where the absence of HD TIV was modelled. Clinical and economic inputs were based on public US data from the CDC and national databases, while data on vaccine effectiveness were extracted from published literature and clinical trials. The impact of HD TIV was further explored in five scenario analyses and deterministic sensitivity analyses (DSA). RESULTS: Over 10 years, it is estimated that HD TIV resulted in an averted 1,333,479 influenza cases, 769,476 medical visits, 40,004 ED presentations, 520,342 cardiorespiratory hospitalizations and 73,689 deaths and generated an absolute $4.6 billion in savings, translating into a return on investment of 214.4%. Hospitalizations costs represented 98.4% and 98.3% of the management costs in the 'with HD TIV' strategy and 'without HD TIV' strategy respectively. Hospitalizations and HD TIV relative vaccine efficacy vs SD TIV as a major cost driver were further confirmed in scenario analysis and DSA. HD TIV remained cost saving under all the scenarios. CONCLUSION: The model showed that HD TIV higher efficacy translated into increased averted health and economic outcomes. HD TIV represented a cost saving intervention from a payer perspective since its introduction.

Interventions on health care providers to improve seasonal influenza vaccination rates among patients: a systematic review and meta-analysis of the evidence since 2000.

BACKGROUND: Seasonal influenza vaccination (SIV) rates remain suboptimal in many populations, even in those with universal SIV. OBJECTIVE: To summarize the evidence on interventions on health care providers (physicians/nurses/pharmacists) to increase SIV rates. METHODS: We systematically searched/selected full-text English publications from January 2000 to July 2019 (PROSPERO-CRD42019147199). Our outcome was the difference in SIV rates between patients in intervention and non-intervention groups. We calculated pooled difference using an inverse variance, random-effects model. RESULTS: We included 39 studies from 8370 retrieved citations. Compared with no intervention, team-based training/education of physicians significantly increased SIV rates in adult patients: 20.1% [7.5-32.7%; I2 = 0%; two randomized controlled trials (RCTs)] and 13.4% [8.6-18.1%; I2 = 0%; two non-randomized intervention studies (NRS)]. A smaller increase was observed in paediatric patients: 7% (0.1-14%; I2 = 0%; two NRS), and in adult patients with team-based training/education of physicians and nurses together: 0.9% (0.2-1.5%; I2 = 30.6%; four NRS). One-off provision of guidelines/information to physicians, and to both physicians and nurses, increased SIV rates in adult patients: 23.8% (15.7-31.8%; I2 = 45.8%; three NRS) and paediatric patients: 24% (8.1-39.9%; I2 = 0%; two NRS), respectively. Use of reminders (prompts) by physicians and nurses slightly increased SIV rates in paediatric patients: 2.3% (0.5-4.2%; I2 = 0%; two RCTs). A larger increase was observed in adult patients: 18.5% (14.8-22.1%; I2 = 0%; two NRS). Evidence from both RCTs and NRS showed significant increases in SIV rates with varied combinations of interventions. CONCLUSIONS: Limited evidence suggests various forms of physicians' and nurses' education and use of reminders may be effective for increasing SIV rates among patients.