Modelling the Impact on Greenhouse Gas Emissions From Using a High Dose Compared With a Standard Dose Influenza Vaccine in Adults Aged 65 Years and Older in France

Modelling the Impact on Greenhouse Gas Emissions From Using a High Dose Compared With a Standard Dose Influenza Vaccine in Adults Aged 65 Years and Older in France | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modelling the Impact on Greenhouse Gas Emissions From Using a High Dose Compared With a Standard Dose Influenza Vaccine in Adults Aged 65 Years and Older in France Laurence Allard, Hélène Bricout, Thierry Rigoine de Fougerolles, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7470484/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: The healthcare system accounts for 8% of total carbon dioxide emissions in France. Vaccines are proven to mitigate the burden of infectious diseases and reduce healthcare utilization. This study aimed to assess the environmental impact on the French healthcare system of using a high-dose (HD) influenza vaccine instead of a standard-dose (SD), by modelling greenhouse gas (GHG) emissions in the care pathway for 65+ in France. Methods: A model was developed applying GHG emission factors to avoided health outcomes through HD instead of SD vaccination, considering the increased GHG emissions from vaccine production. Outcomes from a health economic model populated with French epidemiological data were used, estimating avoided influenza-related primary care visits, emergency visits, and hospitalizations for the HD vaccine based on superior vaccine efficacy (24.2% higher for the HD vaccine than the SD vaccine). A vaccination coverage rate of 60% was used as a baseline and 75% as an exploratory scenario in line with World Health Organization (WHO) targets. Two hospitalization approaches were considered: 1/ based on influenza hospitalizations and 2/ including cardio-respiratory complications triggered by influenza. Emissions factors from France were used for hospitalizations, while UK data were adjusted and used for primary care visits and emergency visits. Results: During an average season, if 60% of older adults were vaccinated with HD instead of SD, 5.0 kilotons of carbon dioxide equivalent (kt CO 2 eq) could be avoided per year considering hospitalizations for influenza, and 32.2 kt CO 2 eq when cardio-respiratory complications are included. At the 75% WHO target, from 6.2 to 40.2 kt CO 2 eq would be avoided. Avoided hospitalizations represented up to 95% of the avoided carbon emissions. The higher GHG emissions from HD vaccine production were compensated by avoided GHG emissions from prevented health outcomes in the approach for influenza hospitalizations, and the intervention exceeded the carbon-efficiency threshold when including cardio-respiratory complications. Conclusions: Despite a higher carbon footprint from vaccine production, the use of an HD vaccine rather than an SD vaccine reduced GHG emissions throughout the care pathway and is therefore at least carbon efficient while delivering tangible public health benefits for 65+. carbon footprint France high dose influenza vaccine influenza patient care pathway sustainability vaccination Figures Figure 1 Figure 2 BACKGROUND France has pledged to reach carbon neutrality by 2050 as part of its National Low-Carbon Strategy (NLCS) (1). As a member of the Alliance for Action on Climate Change and Health (ATACH), a World Health Organization (WHO) initiative, France has committed to build low-carbon and climate-resilient health systems (2). Since the French healthcare system represents approximately 8% of total national annual carbon emissions, reducing its carbon footprint will be instrumental in reducing greenhouse gas emissions by 75% by 2050 to achieve the NLCS target (3). While only 11% and 2% of healthcare GHG emissions are related to direct sources from Scope 1 and Scope 2, respectively, the remaining 87% stem from indirect sources attributable to the pharmaceutical supply chain, delivery of healthcare services, and patient travel (scope 3 and beyond) (3). A detailed emissions breakdown for Scope 3 shows that 29% of total GHG emissions come from the purchase of drugs, 21% from the purchase of medical devices, 11% from alimentation, 9% from patient and visitor transport, 8% from buildings, 5% from waste and services, and 4% from professional transport (3). In addition to measuring the carbon footprint of the healthcare system in France, the Shift Project, a non-profit-organization, has drafted a low-carbon trajectory for the healthcare sector (3). Similarly, the Healthcare New Deal published by the Borne Commission in August 2023 presents a detailed outline of potential levers to reduce the carbon footprint of the healthcare system (4). From these analyses, reducing the carbon footprint of medicines is one of the key drivers to achieve an overall reduction in the carbon footprint of the healthcare system. In this context in France the Caisse Nationale d’Assurance Maladie (the French national insurance fund) is formalizing a methodological framework to measure emissions from the drug lifecycle, and the French health authorities are assessing how to take the carbon footprint of drugs into account in its future clinical and health economic evaluations (5). However, reduction of GHG emissions from medicines will not be sufficient to achieve carbon neutrality by 2050. For this, paradigm shift is needed, with a greater focus on prevention that ultimately reduces health care energy and resource consumption (3). Of seven levers to reduce care pathway GHG emissions, the prevention of diseases and their complications (e.g. emergency room visits, hospitalizations) is highlighted by the Sustainable Markets Initiative (SMI) Health Task Force as being of particular importance (6). Although levers to decarbonize the healthcare system have been identified, few studies have assessed and quantified the impact of healthcare interventions on GHG emissions (7, 8). To effectively activate these decarbonization levers, there is a need for accurate and exhaustive measurement of the environmental impact of prevention interventions, which is currently lacking (9). The importance of decarbonization of healthcare systems is evidenced by the multi-factorial impact of climate change on public health, which disproportionately affects the elderly population (10). Changing weather patterns and temperature are affecting the circulation of infectious diseases and may affect the frequency, duration, and intensity of epidemics such as seasonal influenza (11). On average, influenza epidemics affect 2 to 6 million individuals each year in France (12). From 2011 to 2022, a yearly average of approximately 20,000 influenza-related hospitalizations and 9,000 deaths were attributed to influenza, of which 40% and 90% respectively were in adults aged 65 and over (13). Based on the most recent data, the 2022–2023 influenza season resulted in approximately 2 million general practitioner (GP) consultations for influenza-like illness, about 110,000 emergency room (ER) visits, and more than 15,000 hospitalizations following ER visit for influenza or influenza-like illness contributing heavily to hospital saturation in a context of triple-demic (14). Influenza can trigger respiratory and cardiovascular complications outcomes and lead to loss of autonomy (15), and the elderly are disproportionately affected by the disease given their weaker immune system and increased risk of developing severe outcomes (10, 16). Seasonal influenza vaccination campaigns significantly contribute to reducing healthcare resource utilization (HCRU) and alleviation of the burden on healthcare systems during winter epidemics (13). Every year, influenza vaccination is recommended to the most vulnerable groups to reduce the public health burden of influenza, but vaccination coverage rates (VCR) remain far below the optimal level of 75% set by WHO (13, 17, 18). During three influenza seasons, from 2021 to 2024, a high-dose (HD) influenza vaccine with a good safety profile and proven superior vaccine efficacy compared to the standard dose (SD) vaccine was available in France for adults aged 65 and older (19, 20). The HD vaccine contains four times more antigen than the SD to address the immunosenescence observed in older population in whom protection following SD vaccination is suboptimal. The superiority of HD over SD vaccine has been systematically highlighted in the prevention of seasonal influenza and its complications in patients aged 60 years old and above, initially in randomized controlled trials (RCT) (20, 21), then confirmed in retrospective observational studies during different seasons (22). HD superiority was also confirmed in a recent meta-analysis of RCTs comparing HD with SD vaccines (23). This superiority is associated with a reduction in laboratory-confirmed cases of influenza, hospitalizations for influenza and/or pneumonia, hospitalizations for any cause, hospital admissions linked to respiratory complications or cardiorespiratory events. This demonstrated superiority has led the French NITAG (National Immunisation Technology Advisory Group) to preferentially recommend the use of improved influenza vaccines such as HD over SD (24). This study explores the impact of using the more efficacious HD vaccine against influenza in adults aged 65 and older in France on GHG emissions from the healthcare system perspective, spanning vaccine production to end of life. METHODS This study used a holistic approach to assess the carbon efficiency profile of substituting SD by HD vaccines in adults aged 65 and older in France for an average influenza season from the healthcare system perspective. The scope of the study spanned the entire product lifecycle from raw material extraction and production to end of life. For the use phase, an extended definition was applied in line with technical guidance from the Sustainable Healthcare Coalition (SHC) to measure GHG emissions in the patient care pathway (25). LifecycleGHGemissions for the production, distribution and disposal of both SD and HD influenza vaccines were documented in comparative lifecycle analyses, however, GHG emissions related to the use phase were not available in France (26). To address this gap, patient care pathway emissions associated with the use of the vaccine were mapped. The patient care mapping highlighted two pathways, the vaccination journey and the influenza patient care pathway, including its complications given their impact on healthcare resources utilization and associated GHG emissions. Each year, the French health authorities send a voucher for a fully reimbursed vaccine—dispensed in a pharmacy and administered by a pharmacist, general practitioner or nurse—to all at-risk individuals eligible for influenza vaccination. For both HD and SD vaccines, vaccination takes place either directly in a one-step journey in a pharmacy, in a two-step journey at the GP practice after collection of the vaccine in a pharmacy, or by a nurse at home, in hospital, or at another healthcare setting (Figure 1).The COVID-19 pandemic has accelerated the adoption of pharmacy-based immunization, which now represents approximatively 50% of vaccinated individuals. No significant difference in the proportion between pharmacy-based and GP-based vaccination has been observed between HD and SD vaccinated individuals. The vaccination journey of HD and SD vaccines was therefore assumed to be similar in older adults, leading to no difference in GHG emissions by switching from SD to HD vaccines for this step of the care pathway (the use phase). For the influenza patient care pathway, the reduction in HCRU from vaccination was assessed via three events: GP consultations, ER visits, and hospitalizations.The influenza burden avoided by substituting SD with HD vaccine in the elderly population was obtained from a published health economic studybased on recent French data on the influenza burden (27). The model has been assessed and approved with no major limitations by the French Health Authorities in 2020 and in 2025 (28) and adapted in multiple other countries (29-34).In this study, the public health benefits as a result of the superior clinical efficacy of HD versus SD was modelled based on two VCR scenarios and two disease burden approaches. The base case scenario assumed a vaccination rate of 60%, close to the post-COVID-19 VCR measured by the French Public Health Agency, Santé publique France (59.9% for the 2020-2021 season, 56.8% for 2021-2022) (35). In a scenario analysis, a VCR of 75%, in line with the WHO and French national target for influenza VCR in older adults was explored (17). Given the difficulty in accurately measuring the influenza burden due to the large range of possible complications, two approaches were considered to best assess influenza-related hospitalizations (15). The ‘influenza-approach’ considered an average influenza-related excess respiratory hospitalization rate over 5 seasons. The ‘cardio-respiratory’ approach included hospitalizations possibly related to influenza; i.e. average cardio-respiratory hospitalizations rate over 5 seasons (36). In the ‘influenza’ approach, a relative vaccine efficacy of 24.2% of HD versus SD was used, based on the results of a published individually randomized clinical trial, while SD vaccine efficacy was assumed to be 50% (20). In the ‘cardio-respiratory’ approach, a relative vaccine efficacy (HD compared to SD) of 17.9% was used, as evidenced by a published meta-analysis including more than ten seasons of HD vs SD comparative clinical data (37). Avoided health outcomes and associated HCRU were therefore modelled in 2 scenarios, for 60% and 75% VCR, using 2 approaches covering influenza hospitalizations and cardio-respiratory complications (Table 1). At 60% VCR, using HD instead of SD avoided approximately 68,650 influenza cases, 16,450 GP consultations, 390 ER visits and 2,080 hospitalizations (18,150 hospital bed days) in the influenza approach, while 19,160 hospitalizations (166,040 hospital bed days) are avoided in the cardio-respiratory approach. The hospital bed days result from applying an average hospital length of stay of approximately 8 days (16) . At 75% VCR, the study found that approximately 17,170 more influenza cases, 4,110 more GP consultations, 100 more ER visits, and 510 more hospitalizations (4,530 hospital bed days) would be avoided in the influenza approach, with a total of 23,960 hospitalizations (207,550 hospital bed days) avoided if cardio-respiratory outcomes are included (Table 1). To estimate GHG emissions avoided through reduced HCRU as a result of the superior clinical efficacy provided by HD vs SD vaccine, unitary emissions factors were applied to each of the avoided healthcare utilization events. In the absence of nationwide unitary emissions factors for GP consultations, ER visits, and hospitalizations, the study extrapolated data from the National Healthcare Service (NHS) England for GP consultations and ER visits (38) and used sub-national data for hospitalizations. In its sustainability report published in 2022, the Assistance Publique–Hôpitaux de Paris (AP-HP) provided a unitary emissions factor of 182 kilograms of carbon dioxide equivalent (kg CO2eq) per bed-day excluding patient and visitor travel (39). NHS England reported a corresponding factor of 117 kg CO2eq, and 125 kg CO2eq when adding patient and visitor travel. A ratio of 1.55 was found between the French and English healthcare systems which was then applied to NHS emissions factors for GP consultations and ER visits, 60 kg CO2eq and 71 kg CO2eq, respectively, excluding patient and visitor travel, resulting in an emission factor of 93 kg CO2eq for a GP consultation and 110 kg CO2eq for an ER visit. To best capture the impact on GHG emission factors on the care pathway, patient and visitor travel were measured in France and added to the unitary emissions factors. The distance, breakdown of mode of transportation for daily travel and travel emissions factors applied in the calculations were similar to those used by the Shift Project in its French healthcare system carbon footprint assessment (40). When adding patient travel and, for hospitalization, visitor travel, the emissions factors used as a base case amounted to 95 kg CO2eq per GP consultation, 117 kg CO2eq per ER visit, and 184 kg CO2eq per bed-day in hospital (Table 2). Given the limitations and uncertainties around the emissions factors used as the base case, a sensitivity analysis was performed using top-down and bottom-up values. For the upper bound, a similar bridging approach was used substituting bottom-up AP-HP hospitalization emissions with Montpellier Centre Hospitalier Universitaire (CHU) hospitalization emissions factor of 249 kg CO2eq per bed day (41). The respective associated ratio from NHS England data was applied to the two other emissions factors, resulting in 129 kg CO2eq per GP consultation, 157 kg CO2eq per ER visits, and 249 kg CO2eq per hospital bed-day when adding patient and visitor travel (Table 2). The lower bound relied solely on NHS England emissions factors but applied a ratio (France : United Kingdom) of carbon intensity of healthcare systems based on top-down values from Pichler et al. , time-adjusted to account for the reduction of GHG emissions in France and the UK between the year of the Pichler et al. study(2014), and the year of reference of the NHS England study (2019) (42). The ratio of 0.88 resulted in the following emissions factors: 55kg CO2eq per GP consultation, 69 kg CO2eq per ER visit, and 105 kg CO2eq per bed-day at the hospital (Table 2). A model was used to apply the emissions factors to the avoided HCRU events for both scenarios and approaches. Once modelled, care pathway GHG emissions were consolidated with those from the lifecycle assessment (LCA) of SD and HD vaccines using an individual perspective as LCA data were produced per unit, for one manufactured vaccine. A comparative study conducted by Sanofi in 2019 compared the LCA data of its own SD and HD vaccines (produced France and the US, respectively), and found that an additional 0.6 kg CO2eq was emitted per dose to produce and distribute HD compared SD vaccines, while GHG emissions from end of life were similar. The higher carbon footprint of HD vaccines can be explained by their formulation, which contains four times more antigen than SD and therefore requires more raw materials and energy at the manufacturing phase than SD vaccines. The filling and packing manufacturing steps for the HD vaccine were recently relocated from the United States to France, which contributes to the reduction of the carbon footprint of HD vaccines. LCA data were not extrapolated at the population level as a possible production scale up was not covered in the Sanofi study. Given the uncertainties around carbon emissions input parameters and the attempt to model the full health care pathway in addition to product LCA, a panel of 7 experts was consulted during a scientific meeting to critically review and validate the objectives, scope, methodology, data inputs, and results of the study. The profiles of the experts ranged from carbon footprinting specialists to healthcare practitioners (infectiologist, cardiologist, pharmacist), health economist, and representatives from patients’ associations. In addition to endorsing the study design, several key strengths and limitations were noted and are reported in the present article. The experts also participated as either co-authors or contributors to this article. RESULTS The impact of seasonal influenza epidemics on environmental emissions ranged on average from 47.8 kilotonnes (kt) CO2eq in the influenza approach, to 345.1 kt CO2eq in the cardio-respiratory approach, assuming as a baseline of 60% of adults aged 65 years old and above vaccinated with SD (Fig. 2 ). This means that, when divided by the total number of cases, which is the same for both approaches, an average influenza case has a global warming impact of 72.2 kg CO2eq, and 521.3 kg CO2eq if cardio-vascular complications are included. No difference in GHG emissions was modelled for the vaccination journey, given the similarities in vaccination practices for SD and HD. For the influenza patient care pathway, GHG emissions were avoided through reduced HCRU attributable to the superior vaccine efficacy of HD versus SD. At 60% VCR, using HD instead of SD avoided 5.0 kt CO2eq (influenza hospitalization approach) to 32.2 kt CO2eq (cardio-respiratory approach). In the 75% VCR scenario, substituting SD with HD would avoid from 6.2 kt CO2eq (influenza hospitalization approach) to 40.2 kt CO2eq in the cardio-respiratory approach (Fig. 2 ). For each point of VCR, using HD instead of SD in the care pathway would avoid 82.6 t CO2eq (influenza hospitalization approach) to 535.9 t CO2eq (cardio-respiratory approach). In both scenarios and approaches, the main driver of avoided GHG emissions was hospitalizations, which represented up to 95% of all GHG emissions avoided in the cardio-respiratory approach. In the influenza approach, hospitalizations accounted for 67% of avoided GHG emissions, followed by GP consultations (32%) and ER visits (1%). In the cardio-respiratory approach, hospitalizations, GP consultations, and ER visits represented 96%, 4%, and 0.1% of avoided GHG emissions, respectively. In both approaches, regardless of the health outcomes considered, the four main sources of emissions avoided by using HD rather than SD vaccines were reduced use in pharmaceuticals, reduced use in medical devices, reduced building energy use, and averted patient travel. The sensitivity analysis provided estimates for the lower and upper bounds, respectively, of 2.8 and 6.7 kt CO 2 eq at 60% VCR for the influenza hospitalization approach, and 18.4 and 43.5 kt CO 2 eq when including cardio-respiratory complications. Overall, results were 43% lower than the base case in the lower bound and 35% higher than the base case in the upper bound. The carbon efficiency of the intervention was assessed at an individual level, comparing the incremental emissions of HD production with reduced GHG emissions in the care pathway per dose. Based on comparative LCA data, HD production emits 0.6 kg CO2eq more than SD per dose. When divided by the number of vaccinated individuals, using HD rather than SD avoids approximately 0.6 kg CO2eq during the use phase in the influenza hospitalization approach and 4.1 kg CO2eq in the cardio-respiratory approach per vaccinated individual. Although the production of HD vaccines emits more GHG emissions than SD vaccines, the upstream emissions are compensated for by avoided GHG emissions in the care pathways, as adopting HD breaks even in the influenza hospitalization approach and is carbon-saving when considering cardio-respiratory complications. DISCUSSION This first-of-a-kind study in France assessed the carbon-efficiency profile of using a more efficacious influenza vaccine in the elderly population. Depending on the approach used, avoided care pathway GHG emissions at 60% VCR could amount to 5.0 to 32.2 kt CO2eq. This is equivalent to approximately 1,370 to 8,890 round trips by plane from Paris to New York (40, 43). In the healthcare sector, the avoided carbon footprint can be compared to the total emissions of 280 to 1,840 trips around the world with thermal ambulances (44, 45). In a 60% VCR scenario in the influenza approach, the avoided carbon footprint is equivalent to ~€0.5M annual costs as per the French carbon trading scheme price for 2024 (46). According to guidance from French environmental authorities, this level of avoidance of carbon emissions can be projected to savings of €1.2 and €3.8M in 2030 and 2050, respectively (47). When compared with other investments to reduce an equivalent amount of GHG emissions, this avoided carbon footprint is equivalent to an investment of ~€9.1M in converting ambulances from thermal to electric engines (44). In the cardio-respiratory approach, these figures increase to ~€8.0-24.9M of projected savings in 2030 and 2050 respectively, and ~€59.0M of equivalent investment in ambulances (Table 3). The results show that the intervention is carbon neutral in the most conservative approach (i.e. excluding cardio-respiratory complications), and possibly with a net avoidance when including avoided cardio-respiratory outcomes. Capturing avoided disease complications when mapping the care pathway is thus critical to fully appreciate the avoided GHG emissions from a healthcare system perspective. This study provides additional evidence to support the hypothesis that prevention with vaccines may contribute to the decarbonization of healthcare systems through reduced HCRU, in addition to delivering tangible public health benefits. Prevention has been identified as a lever of decarbonization by both the SMI and the Shift Project (3, 6). Two other studies in the United Kingdom have demonstrated that immunization of infants and children against Respiratory Syncytial Virus (RSV) with nirsevimab and school-based seasonal influenza vaccination programs can significantly reduce GHG emissions from the healthcare system (7, 8). Beyond prevention, other decarbonization levers can be activated, such as optimizing disease management with better treatments or digital care as evidenced in chronic diseases such as diabetes (48, 49). This study highlights the importance of adopting a holistic perspective on GHG emissions, that considers the impact of medicines on the carbon footprint of healthcare systems, in addition to the product lifecycle GHG emissions, to be able to assess the carbon efficiency profile of the intervention in a comprehensive manner. As health authorities reflect on a common methodology for drug LCA, it is also important to include patient care pathway emissions when considering environmental factors in heath technology assessments (50). The SMI includes the evaluation of innovative approaches using common methodological frameworks and tools to produce robust evidence of the environmental benefits of certain drugs on the patient care pathway. While the therapeutical impact of drugs is measured and evaluated through health economic models, limited literature is available to explain how this impact on HCRU can be translated in terms of carbon footprint. The SHC sustainable care pathway method and health economic modelling are possible approaches to approximate the impact of GHG emissions on healthcare systems. A sensitivity analysis can be used to address some of the uncertainty associated with emission factors and provide realistic range of the expected impact (51). Given the novelty of the study, it is also recommended to gather multi-disciplinary perspectives, including epidemiology, health economics, and carbon footprinting aspects, through an expert scientific meeting to pressure-test the study method and interpretation of results. Although there is a growing body of published evidence on unitary emissions factors in the field of healthcare, country-level data remains scarce, with nationwide data only available in England. In the absence of more granular data, emissions factors that have been considered are not specific to influenza nor to the older adult population. More precisely, the hospitalization emissions factor used as the base case is not representative of Metropolitan France but of hospitals in Paris only. Patient and visitor travel were added ad hoc, representing 5% and 2% of estimated GHG emissions, respectively. The extrapolation of GP consultation and ER visit emissions factors based on NHS England data, in the absence of accurate and representative equivalent data in France, also represents a limitation. This study is a first attempt in France to illustrate the broader opportunities presented by such data to assess the potential benefit of certain interventions. Beyond measuring the carbon footprint of the healthcare system, producing unitary emissions factors per HCRU outcome enables organizations and individuals to quantify the impact of interventions on the decarbonization of the care pathway. This calls for greater multi-stakeholder efforts, as exemplified through the SHC in the UK and spearheaded by the Shift Project in France (52, 53). This study focuses on the impact on GHG emissions from the healthcare system perspective and does not cover other environmental factors such as waste or water pollution, or the impact on biodiversity. The analysis was based on the results of a published health economic model which relies on a set of peer-reviewed assumptions and data, in terms of population size, VCR, quantification of the burden of a mean influenza season, and measurement of influenza vaccine efficacy. In particular, VCR in older adults is now below the 60% applied as the base case scenario in this study, based on the latest data on vaccine uptake. The study focused on three HCRU outcomes, with limitations on the estimates of ER visits avoided, and excluded influenza-related intensive care unit admissions and the possibility of re-hospitalization, which were evidenced in several studies (54). Deaths, as well as the impact of using a more effective vaccine on reducing the length of hospitalization and improving quality of life for patients were not included in our evaluation. These assumptions could mean that actual carbon emissions savings would be much more significant than presented due to additional averted HCRU. For the LCA carbon footprint, data from a comparative LCA performed by Sanofi were used (internal data), with Vaxigrip Tetra (SD) being manufactured in France and Efluelda (HD) partly produced in France. Other SD vaccines are used in France but produced in outside of France, and thus may have a different carbon footprint. CONCLUSION In France, the use of an HD vaccine, which is more effective than an SD vaccine against influenza and its complications in older adults, is carbon efficient as well as providing significant co-benefits by reducing the public health burden and economic burden of influenza and relieving the saturation of healthcare systems during winter epidemics. In addition to a consideration of drug lifecycle emissions, this study demonstrates the importance of considering care pathway emissions when assessing the decarbonization potential of an intervention, highlighting the importance of producing reliable unitary emissions factors as a prerequisite to such analyses. Abbreviations AP-HP, Assistance Publique–Hôpitaux de Paris; ATACH, Alliance for Action on Climate Change and Health; CDC, Centers for Disease Control and Prevention; CHU, Montpellier Centre Hospitalier Universitaire; CO2eq, carbon dioxide equivalent; ECDC, European Centre for Disease Prevention and Control; ER, emergency room; GHG, greenhouse gas; GP, general practitioner; GRADE, Grading of Recommendations Assessment, Development and Evaluation; HAS, French Health Authorities; HCRU, healthcare resource utilization; HD, high-dose; kg, kilogram; kt, kilotonne; LCA, lifecycle assessment; NCIRS, National Centre for Immunisation Research and Surveillance; NHS, National Healthcare Service; NLCS, National Low-Carbon Strategy; RSV, Respiratory Syncytial Virus; SD, standard dose; SHC, Sustainable Healthcare Coalition; SMI, Sustainable Markets Initiative; STIKO, Standing Committee on Vaccination; VCR, vaccine coverage rate; WHO, World Health Organization Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Not applicable Competing interests HB, LA and PDT are employees of Sanofi and may hold shares or stock options in the company. PC received consulting fees from Sanofi, and has participated in advisory boards conducted by Sanofi, Seqirus, and Pfizer. PL has received payment or honoraria for lectures, presentations, speakers bureau, manuscript writing or educational events from Astrazeneca, GlaxoSmithKline, Janssen, Moderna, Merck Sharp & Dohme, Pfizer, Sanofi Pasteur, Seqirus DC is part of CERES, which received fees from Sanofi to perform a critical review of this work. TRDF and SDM are employees of CVA, which received consulting fees from Sanofi to conduct the research. The authors report no other conflicts of interest. Funding This work was funded by Sanofi. Authors’ contributions TRDF, SDM, and PDT: Structured the analysis, performed the modelling, contributed to the interpretation of data, critical review and approval of the manuscript. LA, HB: Participated in data analysis, reviewed and approved the manuscript. 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FARDOGRIP: Etude du fardeau de la grippe en France. Data on file. Influenza-associated excess respiratory hospitalisation rates per influenza season. 2019. Lee JKH, Lam GKL, Shin T, Samson SI, Greenberg DP, Chit A . Efficacy and effectiveness of high-dose influenza vaccine in older adults by circulating strain and antigenic match: An updated systematic review and meta-analysis. Vaccine. 2021; 39 Suppl 1 :A24-A35. Tennison I, Roschnik S, Ashby B, Boyd R, Hamilton I, Oreszczyn T, et al. Health care's response to climate change: a carbon footprint assessment of the NHS in England. Lancet Planet Health. 2021; 5 (2):e84-e92. Assistance Publique - Hôpitaux de Paris . Premiers résultats du Bilan Carbone de l’AP-HP sur l’ensemble de ses activités. 2022. The Shift Project . Le Bilan Carbone de La Santé En France: Combien d’émissions de Gaz à Effet de Serre? 2021. Centre hospitalier universitaire de Montpellier . Le CHU souhaite intégrer sa stratégie Bas Carbone au prochain projet d’Etablissement. 2023. Pichler P-P, Jaccard IS, Weisz U, Weisz H . International comparison of health care carbon footprints. Environmental Research Letters. 2019; 14 (6):064004. L'Agence de l'environnement et de la maîtrise de l'énergie . Fiche identité - Bilans GES. Available from: https://bilans-ges.ademe.fr/bilans/consultation/93c6887b-b1cd-11ed-8fce-005056b7acd1/tableau-declaration. Automobile Propre . Voiture électrique : l’impact carbone des batteries au cœur d’une étude suédoise. 2017. Réseau de Transport d’Electricité . éCO2mix - Les émissions de CO2 par kWh produit en France. 2023. Ministère de l’économie des finances et de la souveraineté industrielle et numérique . Journal officiel électronique authentifié n° 0303 du 30/12/2023. 2023. Ministère de la transition écologique . Quelle valeur accorder au CO2 pour parvenir à la neutralité carbone en 2050? 2020. Coalition for Sustainable Pharmaceuticals and Medical Devices . Care pathways guidance on appraising sustainability case study: Type 2 diabetes management care pathway. 2015. Fordham R, Dhatariya K, Stancliffe R, Lloyd A, Chatterjee M, Mathew M, et al. Effective diabetes complication management is a step toward a carbon-efficient planet: an economic modeling study. BMJ Open Diabetes Res Care. 2020; 8 (1):e001017. Marsh K, Ganz M, Nortoft E, Lund N, Graff-Zivin J . Incorporating Environmental Outcomes into a Health Economic Model. Int J Technol Assess Health Care. 2016; 32 (6):400-6. Sustainable Healthcare Coalition . Care Pathway Carbon Calculator. 2015. The Shift Project . Home page. Available from: https://theshiftproject.org/en/home/. Sustainable Healthcare Coalition . Who we are. Available from: https://shcoalition.org/who-we-are/. Descamps A, Lenzi N, Galtier F, Laine F, Lesieur Z, Vanhems P, et al. In-hospital and midterm post-discharge complications of adults hospitalised with respiratory syncytial virus infection in France, 2017-2019: an observational study. Eur Respir J. 2022; 59 (3):2100651. Tables Table 1. Annual health outcomes from substituting SD by HD in adults aged 65 years and above (15 words) VCR - 60% VCR - 75% approach: Influenza Cardio-respiratory Influenza Cardio-respiratory modelled outcome: Baseline (SD) HD HD - SD (i.e. avoided) Baseline (SD) HD HD – SD (i.e. avoided) Baseline (SD) HD HD – SD (i.e. avoided) Baseline (SD) HD HD – SD (i.e. avoided) Influenza disease outcome (in absolute numbers) Influenza cases 661,920 593,270 (68,650) 661,920 593,270 (68,650) 591,000 505,180 (85,820) 591,000 505,180 (85,820) General Practitioner visits 158,560 142,110 (16,450) 158,560 142,110 (16,450) 141,570 121,010 (20,560) 141,570 121,010 (20,560) Emergency Room visits 3,770 3,380 (390) 3,770 3,380 (390) 3,370 2,880 (490) 3,370 2,880 (490) Hospitalizations 20,000 17,920 (2,080) 206,790 187,630 (19,160) 17,850 15,260 (2,590) 196,160 172,200 (23,960) Hospitalization - bed days 174,990 156,840 (18,150) 1,791,730 1,625,690 (166,040) 156,240 133,560 (22,680) 1,699,580 1,492,030 (207,550) Deaths 8,840 7,920 (920) 8,840 7,920 (920) 7,890 6,750 (1,140) 7,890 6,750 (1,140) Note: HD, high dose; SD, standard dose; VCR, vaccine coverage rate Table 2. Unitary emissions factors applied as base case, upper bound and lower bound General practitioner visit (kg CO 2 eq / visit) Emergency room visit (kg CO 2 eq / visit) Hospitalization (kg CO 2 eq / bed day) Lower bound 55 69 105 Base case 95 117 184 Upper bound 129 157 249 Note: CO 2 eq: carbon dioxide equivalent; kg: kilogram Table 3. Equivalence in terms of carbon cost, value of averted carbon emissions and carbon reduction investment (15 words) VCR at 60% VCR at 75% Influenza Cardio-respiratory Influenza Cardio-respiratory approach Approach approach approach Equivalent carbon costs (€M) Equivalent carbon costs based on France carbon trading scheme €0.5 €3.0 €0.6 €3.7 Value of averted carbon emissions - lower range with 2030 reference €1.2 €8.0 €1.5 €10.0 Value of averted carbon emissions - upper range with 2050 reference €3.8 €24.9 €4.8 €31.1 Equivalent investment costs (€M) Equivalent investment in electric ambulances €9.1 €59.0 €11.4 €73.8 Note: CO 2 eq: carbon dioxide equivalent; M: million; t: tons; VCR: vaccine coverage rate Additional Declarations Competing interest reported. H.B., L.A. and P.D.T. are employees of Sanofi and may hold shares or stock options in the company. P.C. received consulting fees from Sanofi, and has participated in advisory boards conducted by Sanofi, Seqirus, and Pfizer. P.L. has received payment or honoraria for lectures, presentations, speakers bureau, manuscript writing or educational events from Astrazeneca, GlaxoSmithKline, Janssen, Moderna, Merck Sharp & Dohme, Pfizer, Sanofi Pasteur, Seqirus D.C. is part of CERES, which received fees from Sanofi to perform a critical review of this work. T.R.D.F. and S.D.M. are employees of CVA, which received consulting fees from Sanofi to conduct the research. The authors report no other conflicts of interest. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 12 Sep, 2025 Editor assigned by journal 01 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 27 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":129974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of vaccination journey and influenza patient care pathway for HD and SD vaccines\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7470484/v1/93928c4eefb09cfb7ede18b9.png"},{"id":94163808,"identity":"e559868a-2331-430d-b621-3e65d7f234ee","added_by":"auto","created_at":"2025-10-23 05:42:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTotal avoided GHG emissions from prevented health outcomes using HD vaccine vs SD vaccine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: results from sensitivity analysis in 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H.B., L.A. and P.D.T. are employees of Sanofi and may hold shares or stock options in the company.\nP.C. received consulting fees from Sanofi, and has participated in advisory boards conducted by Sanofi, Seqirus, and Pfizer. \nP.L. has received payment or honoraria for lectures, presentations, speakers bureau, manuscript writing or educational events from Astrazeneca, GlaxoSmithKline, Janssen, Moderna, Merck Sharp \u0026 Dohme, Pfizer, Sanofi Pasteur, Seqirus\nD.C. is part of CERES, which received fees from Sanofi to perform a critical review of this work.\nT.R.D.F. and S.D.M. are employees of CVA, which received consulting fees from Sanofi to conduct the research.\nThe authors report no other conflicts of interest.","formattedTitle":"\u003cp\u003eModelling the Impact on Greenhouse Gas Emissions From Using a High Dose Compared With a Standard Dose Influenza Vaccine in Adults Aged 65 Years and Older in France\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eFrance has pledged to reach carbon neutrality by 2050 as part of its National Low-Carbon Strategy (NLCS) (1). As a member of the Alliance for Action on Climate Change and Health (ATACH), a World Health Organization (WHO) initiative, France has committed to build low-carbon and climate-resilient health systems (2). Since the French healthcare system represents approximately 8% of total national annual carbon emissions, reducing its carbon footprint will be instrumental in reducing greenhouse gas emissions by 75% by 2050 to achieve the NLCS target (3). While only 11% and 2% of healthcare GHG emissions are related to direct sources from Scope 1 and Scope 2, respectively, the remaining 87% stem from indirect sources attributable to the pharmaceutical supply chain, delivery of healthcare services, and patient travel (scope 3 and beyond) (3). A detailed emissions breakdown for Scope 3 shows that 29% of total GHG emissions come from the purchase of drugs, 21% from the purchase of medical devices, 11% from alimentation, 9% from patient and visitor transport, 8% from buildings, 5% from waste and services, and 4% from professional transport (3). In addition to measuring the carbon footprint of the healthcare system in France, the Shift Project, a non-profit-organization, has drafted a low-carbon trajectory for the healthcare sector (3). Similarly, the Healthcare New Deal published by the Borne Commission in August 2023 presents a detailed outline of potential levers to reduce the carbon footprint of the healthcare system (4). From these analyses, reducing the carbon footprint of medicines is one of the key drivers to achieve an overall reduction in the carbon footprint of the healthcare system. In this context in France the Caisse Nationale d\u0026rsquo;Assurance Maladie (the French national insurance fund) is formalizing a methodological framework to measure emissions from the drug lifecycle, and the French health authorities are assessing how to take the carbon footprint of drugs into account in its future clinical and health economic evaluations (5).\u003c/p\u003e\u003cp\u003eHowever, reduction of GHG emissions from medicines will not be sufficient to achieve carbon neutrality by 2050. For this, paradigm shift is needed, with a greater focus on prevention that ultimately reduces health care energy and resource consumption (3). Of seven levers to reduce care pathway GHG emissions, the prevention of diseases and their complications (e.g. emergency room visits, hospitalizations) is highlighted by the Sustainable Markets Initiative (SMI) Health Task Force as being of particular importance (6). Although levers to decarbonize the healthcare system have been identified, few studies have assessed and quantified the impact of healthcare interventions on GHG emissions (7, 8). To effectively activate these decarbonization levers, there is a need for accurate and exhaustive measurement of the environmental impact of prevention interventions, which is currently lacking (9). The importance of decarbonization of healthcare systems is evidenced by the multi-factorial impact of climate change on public health, which disproportionately affects the elderly population (10). Changing weather patterns and temperature are affecting the circulation of infectious diseases and may affect the frequency, duration, and intensity of epidemics such as seasonal influenza (11).\u003c/p\u003e\u003cp\u003eOn average, influenza epidemics affect 2 to 6\u0026nbsp;million individuals each year in France (12). From 2011 to 2022, a yearly average of approximately 20,000 influenza-related hospitalizations and 9,000 deaths were attributed to influenza, of which 40% and 90% respectively were in adults aged 65 and over (13). Based on the most recent data, the 2022\u0026ndash;2023 influenza season resulted in approximately 2\u0026nbsp;million general practitioner (GP) consultations for influenza-like illness, about 110,000 emergency room (ER) visits, and more than 15,000 hospitalizations following ER visit for influenza or influenza-like illness contributing heavily to hospital saturation in a context of triple-demic (14). Influenza can trigger respiratory and cardiovascular complications outcomes and lead to loss of autonomy (15), and the elderly are disproportionately affected by the disease given their weaker immune system and increased risk of developing severe outcomes (10, 16). Seasonal influenza vaccination campaigns significantly contribute to reducing healthcare resource utilization (HCRU) and alleviation of the burden on healthcare systems during winter epidemics (13). Every year, influenza vaccination is recommended to the most vulnerable groups to reduce the public health burden of influenza, but vaccination coverage rates (VCR) remain far below the optimal level of 75% set by WHO (13, 17, 18). During three influenza seasons, from 2021 to 2024, a high-dose (HD) influenza vaccine with a good safety profile and proven superior vaccine efficacy compared to the standard dose (SD) vaccine was available in France for adults aged 65 and older (19, 20). The HD vaccine contains four times more antigen than the SD to address the immunosenescence observed in older population in whom protection following SD vaccination is suboptimal. The superiority of HD over SD vaccine has been systematically highlighted in the prevention of seasonal influenza and its complications in patients aged 60 years old and above, initially in randomized controlled trials (RCT) (20, 21), then confirmed in retrospective observational studies during different seasons (22). HD superiority was also confirmed in a recent meta-analysis of RCTs comparing HD with SD vaccines (23). This superiority is associated with a reduction in laboratory-confirmed cases of influenza, hospitalizations for influenza and/or pneumonia, hospitalizations for any cause, hospital admissions linked to respiratory complications or cardiorespiratory events. This demonstrated superiority has led the French NITAG (National Immunisation Technology Advisory Group) to preferentially recommend the use of improved influenza vaccines such as HD over SD (24).\u003c/p\u003e\u003cp\u003eThis study explores the impact of using the more efficacious HD vaccine against influenza in adults aged 65 and older in France on GHG emissions from the healthcare system perspective, spanning vaccine production to end of life.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study used a holistic approach to assess the carbon efficiency profile of substituting SD by HD vaccines in adults aged 65 and older in France for an average influenza season from the healthcare system perspective. The scope of the study spanned the entire product lifecycle from raw material extraction and production to end of life. For the use phase, an extended definition was applied in line with technical guidance from the Sustainable Healthcare Coalition (SHC) to measure GHG emissions in the patient care pathway (25). LifecycleGHGemissions for the production, distribution and disposal of both SD and HD influenza vaccines were documented in comparative lifecycle analyses, however, GHG emissions related to the use phase were not available in France (26). To address this gap, patient care pathway emissions associated with the use of the vaccine were mapped. The patient care mapping highlighted two pathways, the vaccination journey and the influenza patient care pathway, including its complications given their impact on healthcare resources utilization and associated GHG emissions.\u003c/p\u003e\n\u003cp\u003eEach year, the French health authorities send a voucher for a fully reimbursed vaccine—dispensed in a pharmacy and administered by a pharmacist, general practitioner or nurse—to all at-risk individuals eligible for influenza vaccination. For both HD and SD vaccines, vaccination takes place either directly in a one-step journey in a pharmacy, in a two-step journey at the GP practice after collection of the vaccine in a pharmacy, or by a nurse at home, in hospital, or at another healthcare setting (Figure 1).The COVID-19 pandemic has accelerated the adoption of pharmacy-based immunization, which now represents approximatively 50% of vaccinated individuals. No significant difference in the proportion between pharmacy-based and GP-based vaccination has been observed between HD and SD vaccinated individuals. The vaccination journey of HD and SD vaccines was therefore assumed to be similar in older adults, leading to no difference in GHG emissions by switching from SD to HD vaccines for this step of the care pathway (the use phase).\u003c/p\u003e\n\u003cp\u003eFor the influenza patient care pathway, the reduction in HCRU from vaccination was assessed via three events: GP consultations, ER visits, and hospitalizations.The influenza burden avoided by substituting SD with HD vaccine in the elderly population was obtained from a published health economic studybased on recent French data on the influenza burden (27). The model has been assessed and approved with no major limitations by the French Health Authorities in 2020 and in 2025 (28) and adapted in multiple other countries (29-34).In this study, the public health benefits as a result of the superior clinical efficacy of HD versus SD was modelled based on two VCR scenarios and two disease burden approaches. The base case scenario assumed a vaccination rate of 60%, close to the post-COVID-19 VCR measured by the French Public Health Agency, Santé publique France (59.9% for the 2020-2021 season, 56.8% for 2021-2022) (35). In a scenario analysis, a VCR of 75%, in line with the WHO and French national target for influenza VCR in older adults was explored (17). Given the difficulty in accurately measuring the influenza burden due to the large range of possible complications, two approaches were considered to best assess influenza-related hospitalizations (15). The ‘influenza-approach’ considered an average influenza-related excess respiratory hospitalization rate over 5 seasons. The ‘cardio-respiratory’ approach included hospitalizations possibly related to influenza; i.e. average cardio-respiratory hospitalizations rate over 5 seasons (36). In the ‘influenza’ approach, a relative vaccine efficacy of 24.2% of HD versus SD was used, based on the results of a published individually randomized clinical trial, while SD vaccine efficacy was assumed to be 50% (20). In the ‘cardio-respiratory’ approach, a relative vaccine efficacy (HD compared to SD) of 17.9% was used, as evidenced by a published meta-analysis including more than ten seasons of HD vs SD comparative clinical data (37).\u003c/p\u003e\n\u003cp\u003eAvoided health outcomes and associated HCRU were therefore modelled in 2 scenarios, for 60% and 75% VCR, using 2 approaches covering influenza hospitalizations and cardio-respiratory complications (Table 1). At 60% VCR, using HD instead of SD avoided approximately 68,650 influenza cases, 16,450 GP consultations, 390 ER visits and 2,080 hospitalizations (18,150 hospital bed days) in the influenza approach, while 19,160 hospitalizations (166,040 hospital bed days) are avoided in the cardio-respiratory approach. The hospital bed days result from applying an average hospital length of stay of approximately 8 days (16) . At 75% VCR, the study found that approximately 17,170 more influenza cases, 4,110 more GP consultations, 100 more ER visits, and 510 more hospitalizations (4,530 hospital bed days) would be avoided in the influenza approach, with a total of 23,960 hospitalizations (207,550 hospital bed days) avoided if cardio-respiratory outcomes are included (Table 1).\u003c/p\u003e\n\u003cp\u003eTo estimate GHG emissions avoided through reduced HCRU as a result of the superior clinical efficacy provided by HD vs SD vaccine, unitary emissions factors were applied to each of the avoided healthcare utilization events. In the absence of nationwide unitary emissions factors for GP consultations, ER visits, and hospitalizations, the study extrapolated data from the National Healthcare Service (NHS) England for GP consultations and ER visits (38) and used sub-national data for hospitalizations. In its sustainability report published in 2022, the Assistance Publique–Hôpitaux de Paris (AP-HP) provided a unitary emissions factor of 182 kilograms of carbon dioxide equivalent (kg CO2eq) per bed-day excluding patient and visitor travel (39). NHS England reported a corresponding factor of 117 kg CO2eq, and 125 kg CO2eq when adding patient and visitor travel. A ratio of 1.55 was found between the French and English healthcare systems which was then applied to NHS emissions factors for GP consultations and ER visits, 60 kg CO2eq and 71 kg CO2eq, respectively, excluding patient and visitor travel, resulting in an emission factor of 93 kg CO2eq for a GP consultation and 110 kg CO2eq for an ER visit.\u003c/p\u003e\n\u003cp\u003eTo best capture the impact on GHG emission factors on the care pathway, patient and visitor travel were measured in France and added to the unitary emissions factors. The distance, breakdown of mode of transportation for daily travel and travel emissions factors applied in the calculations were similar to those used by the Shift Project in its French healthcare system carbon footprint assessment (40). When adding patient travel and, for hospitalization, visitor travel, the emissions factors used as a base case amounted to 95 kg CO2eq per GP consultation, 117 kg CO2eq per ER visit, and 184 kg CO2eq per bed-day in hospital (Table 2).\u003c/p\u003e\n\u003cp\u003eGiven the limitations and uncertainties around the emissions factors used as the base case, a sensitivity analysis was performed using top-down and bottom-up values. For the upper bound, a similar bridging approach was used substituting bottom-up AP-HP hospitalization emissions with Montpellier Centre Hospitalier Universitaire (CHU) hospitalization emissions factor of 249 kg CO2eq per bed day (41). The respective associated ratio from NHS England data was applied to the two other emissions factors, resulting in 129 kg CO2eq per GP consultation, 157 kg CO2eq per ER visits, and 249 kg CO2eq per hospital bed-day when adding patient and visitor travel (Table 2). The lower bound relied solely on NHS England emissions factors but applied a ratio (France : United Kingdom) of carbon intensity of healthcare systems based on top-down values from Pichler \u003cem\u003eet al.\u003c/em\u003e, time-adjusted to account for the reduction of GHG emissions in France and the UK between the year of the Pichler \u003cem\u003eet al.\u003c/em\u003e study(2014), and the year of reference of the NHS England study (2019) (42). The ratio of 0.88 resulted in the following emissions factors: 55kg CO2eq per GP consultation, 69 kg CO2eq per ER visit, and 105 kg CO2eq per bed-day at the hospital (Table 2).\u003c/p\u003e\n\u003cp\u003eA model was used to apply the emissions factors to the avoided HCRU events for both scenarios and approaches. Once modelled, care pathway GHG emissions were consolidated with those from the lifecycle assessment (LCA) of SD and HD vaccines using an individual perspective as LCA data were produced per unit, for one manufactured vaccine. A comparative study conducted by Sanofi in 2019 compared the LCA data of its own SD and HD vaccines (produced France and the US, respectively), and found that an additional 0.6 kg CO2eq was emitted per dose to produce and distribute HD compared SD vaccines, while GHG emissions from end of life were similar. The higher carbon footprint of HD vaccines can be explained by their formulation, which contains four times more antigen than SD and therefore requires more raw materials and energy at the manufacturing phase than SD vaccines. The filling and packing manufacturing steps for the HD vaccine were recently relocated from the United States to France, which contributes to the reduction of the carbon footprint of HD vaccines. LCA data were not extrapolated at the population level as a possible production scale up was not covered in the Sanofi study.\u003c/p\u003e\n\u003cp\u003eGiven the uncertainties around carbon emissions input parameters and the attempt to model the full health care pathway in addition to product LCA, a panel of 7 experts was consulted during a scientific meeting to critically review and validate the objectives, scope, methodology, data inputs, and results of the study. The profiles of the experts ranged from carbon footprinting specialists to healthcare practitioners (infectiologist, cardiologist, pharmacist), health economist, and representatives from patients’ associations. In addition to endorsing the study design, several key strengths and limitations were noted and are reported in the present article. The experts also participated as either co-authors or contributors to this article.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe impact of seasonal influenza epidemics on environmental emissions ranged on average from 47.8 kilotonnes (kt) CO2eq in the influenza approach, to 345.1 kt CO2eq in the cardio-respiratory approach, assuming as a baseline of 60% of adults aged 65 years old and above vaccinated with SD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This means that, when divided by the total number of cases, which is the same for both approaches, an average influenza case has a global warming impact of 72.2 kg CO2eq, and 521.3 kg CO2eq if cardio-vascular complications are included.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo difference in GHG emissions was modelled for the vaccination journey, given the similarities in vaccination practices for SD and HD. For the influenza patient care pathway, GHG emissions were avoided through reduced HCRU attributable to the superior vaccine efficacy of HD versus SD. At 60% VCR, using HD instead of SD avoided 5.0 kt CO2eq (influenza hospitalization approach) to 32.2 kt CO2eq (cardio-respiratory approach).\u003c/p\u003e\u003cp\u003eIn the 75% VCR scenario, substituting SD with HD would avoid from 6.2 kt CO2eq (influenza hospitalization approach) to 40.2 kt CO2eq in the cardio-respiratory approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For each point of VCR, using HD instead of SD in the care pathway would avoid 82.6 t CO2eq (influenza hospitalization approach) to 535.9 t CO2eq (cardio-respiratory approach).\u003c/p\u003e\u003cp\u003eIn both scenarios and approaches, the main driver of avoided GHG emissions was hospitalizations, which represented up to 95% of all GHG emissions avoided in the cardio-respiratory approach. In the influenza approach, hospitalizations accounted for 67% of avoided GHG emissions, followed by GP consultations (32%) and ER visits (1%). In the cardio-respiratory approach, hospitalizations, GP consultations, and ER visits represented 96%, 4%, and 0.1% of avoided GHG emissions, respectively. In both approaches, regardless of the health outcomes considered, the four main sources of emissions avoided by using HD rather than SD vaccines were reduced use in pharmaceuticals, reduced use in medical devices, reduced building energy use, and averted patient travel.\u003c/p\u003e\u003cp\u003eThe sensitivity analysis provided estimates for the lower and upper bounds, respectively, of 2.8 and 6.7 kt CO\u003csub\u003e2\u003c/sub\u003eeq at 60% VCR for the influenza hospitalization approach, and 18.4 and 43.5 kt CO\u003csub\u003e2\u003c/sub\u003eeq when including cardio-respiratory complications. Overall, results were 43% lower than the base case in the lower bound and 35% higher than the base case in the upper bound.\u003c/p\u003e\u003cp\u003eThe carbon efficiency of the intervention was assessed at an individual level, comparing the incremental emissions of HD production with reduced GHG emissions in the care pathway per dose. Based on comparative LCA data, HD production emits 0.6 kg CO2eq more than SD per dose. When divided by the number of vaccinated individuals, using HD rather than SD avoids approximately 0.6 kg CO2eq during the use phase in the influenza hospitalization approach and 4.1 kg CO2eq in the cardio-respiratory approach per vaccinated individual. Although the production of HD vaccines emits more GHG emissions than SD vaccines, the upstream emissions are compensated for by avoided GHG emissions in the care pathways, as adopting HD breaks even in the influenza hospitalization approach and is carbon-saving when considering cardio-respiratory complications.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis first-of-a-kind study in France assessed the carbon-efficiency profile of using a more efficacious influenza vaccine in the elderly population. Depending on the approach used, avoided care pathway GHG emissions at 60% VCR could amount to 5.0 to 32.2 kt CO2eq. This is equivalent to approximately 1,370 to 8,890 round trips by plane from Paris to New York (40, 43). In the healthcare sector, the avoided carbon footprint can be compared to the total emissions of 280 to 1,840 trips around the world with thermal ambulances (44, 45). In a 60% VCR scenario in the influenza approach, the avoided carbon footprint is equivalent to ~€0.5M annual costs as per the French carbon trading scheme price for 2024 (46). According to guidance from French environmental authorities, this level of avoidance of carbon emissions can be projected to savings of €1.2 and €3.8M in 2030 and 2050, respectively (47).\u0026nbsp;When compared with other investments to reduce an equivalent amount of GHG emissions, this avoided carbon footprint is equivalent to an investment of ~€9.1M in converting ambulances from thermal to electric engines\u0026nbsp;(44). In the cardio-respiratory approach, these figures increase to ~€8.0-24.9M of projected savings in 2030 and 2050 respectively, and ~€59.0M of equivalent investment in ambulances (Table 3).\u003c/p\u003e\n\u003cp\u003eThe results show that the intervention is carbon neutral in the most conservative approach (i.e. excluding cardio-respiratory complications), and possibly with a net avoidance when including avoided cardio-respiratory outcomes. Capturing avoided disease complications when mapping the care pathway is thus critical to fully appreciate the avoided GHG emissions from a healthcare system perspective. This study provides additional evidence to support the hypothesis that prevention with vaccines may contribute to the decarbonization of healthcare systems through reduced HCRU, in addition to delivering tangible public health benefits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevention has been identified as a lever of decarbonization by both the SMI and the Shift Project (3, 6). Two other studies in the United Kingdom have demonstrated that immunization of infants and children against Respiratory Syncytial Virus (RSV) with nirsevimab and school-based seasonal influenza vaccination programs can significantly reduce GHG emissions from the healthcare system (7, 8). Beyond prevention, other decarbonization levers can be activated, such as optimizing disease management with better treatments or digital care as evidenced in chronic diseases such as diabetes (48, 49).\u003c/p\u003e\n\u003cp\u003eThis study highlights the importance of adopting a holistic perspective on GHG emissions, that considers the impact of medicines on the carbon footprint of healthcare systems, in addition to the product lifecycle GHG emissions, to be able to assess the carbon efficiency profile of the intervention in a comprehensive manner. As health authorities reflect on a common methodology for drug LCA, it is also important to include patient care pathway emissions when considering environmental factors in heath technology assessments (50). The SMI includes the evaluation of innovative approaches using common methodological frameworks and tools to produce robust evidence of the environmental benefits of certain drugs on the patient care pathway. While the therapeutical impact of drugs is measured and evaluated through health economic models, limited literature is available to explain how this impact on HCRU can be translated in terms of carbon footprint. The SHC sustainable care pathway method and health economic modelling are possible approaches to approximate the impact of GHG emissions on healthcare systems. A sensitivity analysis can be used to address some of the uncertainty associated with emission factors and provide realistic range of the expected impact (51). Given the novelty of the study, it is also recommended to gather multi-disciplinary perspectives, including epidemiology, health economics, and carbon footprinting aspects, through an expert scientific meeting to pressure-test the study method and interpretation of results.\u003c/p\u003e\n\u003cp\u003eAlthough there is a growing body of published evidence on unitary emissions factors in the field of healthcare, country-level data remains scarce, with nationwide data only available in England. In the absence of more granular data, emissions factors that have been considered are not specific to influenza nor to the older adult population. More precisely, the hospitalization emissions factor used as the base case is not representative of Metropolitan France but of hospitals in Paris only. Patient and visitor travel were added ad hoc, representing 5% and 2% of estimated GHG emissions, respectively. The extrapolation of GP consultation and ER visit emissions factors based on NHS England data, in the absence of accurate and representative equivalent data in France, also represents a limitation. This study is a first attempt in France to illustrate the broader opportunities presented by such data to assess the potential benefit of certain interventions. Beyond measuring the carbon footprint of the healthcare system, producing unitary emissions factors per HCRU outcome enables organizations and individuals to quantify the impact of interventions on the decarbonization of the care pathway. This calls for greater multi-stakeholder efforts, as exemplified through the SHC in the UK and spearheaded by the Shift Project in France (52, 53).\u003c/p\u003e\n\u003cp\u003eThis study focuses on the impact on GHG emissions from the healthcare system perspective and does not cover other environmental factors such as waste or water pollution, or the impact on biodiversity. The analysis was based on the results of a published health economic model which relies on a set of peer-reviewed assumptions and data, in terms of population size, VCR, quantification of the burden of a mean influenza season, and measurement of influenza vaccine efficacy. In particular, VCR in older adults is now below the 60% applied as the base case scenario in this study, based on the latest data on vaccine uptake. The study focused on three HCRU outcomes, with limitations on the estimates of ER visits avoided, and excluded influenza-related intensive care unit admissions and the possibility of re-hospitalization, which were evidenced in several studies (54). Deaths, as well as the impact of using a more effective vaccine on reducing the length of hospitalization and improving quality of life for patients were not included in our evaluation. These assumptions could mean that actual carbon emissions savings would be much more significant than presented due to additional averted HCRU. For the LCA carbon footprint, data from a comparative LCA performed by Sanofi were used (internal data), with Vaxigrip Tetra (SD) being manufactured in France and Efluelda (HD) partly produced in France. Other SD vaccines are used in France but produced in outside of France, and thus may have a different carbon footprint.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn France, the use of an HD vaccine, which is more effective than an SD vaccine against influenza and its complications in older adults, is carbon efficient as well as providing significant co-benefits by reducing the public health burden and economic burden of influenza and relieving the saturation of healthcare systems during winter epidemics. In addition to a consideration of drug lifecycle emissions, this study demonstrates the importance of considering care pathway emissions when assessing the decarbonization potential of an intervention, highlighting the importance of producing reliable unitary emissions factors as a prerequisite to such analyses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAP-HP, Assistance Publique–Hôpitaux de Paris; ATACH, Alliance for Action on Climate Change and Health; CDC, Centers for Disease Control and Prevention; CHU, Montpellier Centre Hospitalier Universitaire; CO2eq, carbon dioxide equivalent; ECDC, European Centre for Disease Prevention and Control; ER, emergency room; GHG, greenhouse gas; GP, general practitioner; GRADE, Grading of Recommendations Assessment, Development and Evaluation; HAS, French Health Authorities; HCRU, healthcare resource utilization; HD, high-dose; kg, kilogram; kt, kilotonne; LCA, lifecycle assessment; NCIRS, National Centre for Immunisation Research and Surveillance; NHS, National Healthcare Service; NLCS, National Low-Carbon Strategy; RSV, Respiratory Syncytial Virus; SD, standard dose; SHC, Sustainable Healthcare Coalition; SMI, Sustainable Markets Initiative; STIKO, Standing Committee on Vaccination; VCR, vaccine coverage rate; WHO, World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eHB, LA and PDT are employees of Sanofi and may hold shares or stock options in the company.\u003c/p\u003e\n\u003cp\u003ePC received consulting fees from Sanofi, and has participated in advisory boards conducted by Sanofi, Seqirus, and Pfizer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePL has received payment or honoraria for lectures, presentations, speakers bureau, manuscript writing or educational events from Astrazeneca, GlaxoSmithKline, Janssen, Moderna, Merck Sharp \u0026amp; Dohme, Pfizer, Sanofi Pasteur, Seqirus\u003c/p\u003e\n\u003cp\u003eDC is part of CERES, which received fees from Sanofi to perform a critical review of this work.\u003c/p\u003e\n\u003cp\u003eTRDF and SDM are employees of CVA, which received consulting fees from Sanofi to conduct the research.\u003c/p\u003e\n\u003cp\u003eThe authors report no other conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Sanofi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTRDF, SDM, and PDT: Structured the analysis, performed the modelling, contributed to the interpretation of data, critical review and approval of the manuscript.\u003c/p\u003e\n\u003cp\u003eLA, HB: Participated in data analysis, reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003ePC, JFT, PLo, DC, PLe: Contributed to the interpretation of data, critical review and approval of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors are accountable for the accuracy and integrity of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank additional experts involved in the consultation: Thomas Raimbaud, Baptiste Verneuil, and Mathias Egnell who critically reviewed the study results and commented on study limitations, and Charlotte Buxtorf and Leith Pic who helped with data collection and manuscript writing respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eMinist\u0026egrave;re de la Transition Ecologique et Solidaire\u003c/strong\u003e. 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BMJ Open Diabetes Res Care. 2020;\u003cstrong\u003e8\u003c/strong\u003e(1):e001017.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMarsh K, Ganz M, Nortoft E, Lund N, Graff-Zivin J\u003c/strong\u003e. Incorporating Environmental Outcomes into a Health Economic Model. Int J Technol Assess Health Care. 2016;\u003cstrong\u003e32\u003c/strong\u003e(6):400-6.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSustainable Healthcare Coalition\u003c/strong\u003e. Care Pathway Carbon Calculator. 2015.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe Shift Project\u003c/strong\u003e. Home page. Available from: https://theshiftproject.org/en/home/.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSustainable Healthcare Coalition\u003c/strong\u003e. Who we are. Available from: https://shcoalition.org/who-we-are/.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDescamps A, Lenzi N, Galtier F, Laine F, Lesieur Z, Vanhems P, et al.\u003c/strong\u003e In-hospital and midterm post-discharge complications of adults hospitalised with respiratory syncytial virus infection in France, 2017-2019: an observational study. Eur Respir J. 2022;\u003cstrong\u003e59\u003c/strong\u003e(3):2100651.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Annual health outcomes from substituting SD by HD in adults aged 65 years and above (15 words)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"979\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCR - 60%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCR - 75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003e\u003cem\u003eapproach:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 18.6735%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 19.0816%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardio-respiratory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 18.6735%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 19.0816%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardio-respiratory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003e\u003cem\u003emodelled outcome:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD - SD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(i.e. avoided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD \u0026ndash; SD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(i.e. avoided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD \u0026ndash; SD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(i.e. avoided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD \u0026ndash; SD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(i.e. avoided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza disease outcome (in absolute numbers)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eInfluenza cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e661,920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e593,270\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(68,650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e661,920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e593,270\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(68,650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e591,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e505,180\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(85,820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e591,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e505,180\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(85,820)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eGeneral Practitioner visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e158,560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e142,110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(16,450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e158,560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e142,110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(16,450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e141,570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e121,010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(20,560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e141,570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e121,010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(20,560)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eEmergency Room visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e3,770\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e3,380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e3,770\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e3,380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e3,370\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e2,880\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e3,370\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e2,880\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(490)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eHospitalizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e20,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e17,920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(2,080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e206,790\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e187,630\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(19,160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e17,850\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e15,260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(2,590)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e196,160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e172,200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(23,960)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eHospitalization - bed days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e174,990\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e156,840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(18,150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e1,791,730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e1,625,690\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(166,040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e156,240\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e133,560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(22,680)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e1,699,580\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e1,492,030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(207,550)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e8,840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e7,920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e8,840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e7,920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1.53061%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e7,890\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e6,750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(1,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e7,890\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.42857%;\"\u003e\n \u003cp\u003e6,750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.22449%;\"\u003e\n \u003cp\u003e(1,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: HD, high dose; SD, standard dose; VCR, vaccine coverage rate\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Unitary emissions factors applied as base case, upper bound and lower bound\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.0957%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral practitioner visit\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg CO\u003csub\u003e2\u003c/sub\u003eeq / visit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmergency room visit\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg CO\u003csub\u003e2\u003c/sub\u003eeq / visit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(kg CO\u003csub\u003e2\u003c/sub\u003eeq / bed day)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.0957%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.0957%;\"\u003e\n \u003cp\u003eLower bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e105\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.0957%;\"\u003e\n \u003cp\u003eBase case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e117\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.0957%;\"\u003e\n \u003cp\u003eUpper bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e129\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e157\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.6348%;\"\u003e\n \u003cp\u003e249\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNote: CO\u003csub\u003e2\u003c/sub\u003eeq: carbon dioxide equivalent; kg: kilogram\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Equivalence in terms of carbon cost, value of averted carbon emissions and carbon reduction investment (15 words)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"663\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCR at 60%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCR at 75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardio-respiratory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardio-respiratory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eapproach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApproach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eapproach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eapproach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquivalent carbon costs (\u0026euro;M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003eEquivalent carbon costs based on France carbon trading scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;0.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;3.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;0.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;3.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003eValue of averted carbon emissions - lower range with 2030 reference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;1.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;8.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;1.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;10.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003eValue of averted carbon emissions - upper range with 2050 reference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;3.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;24.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;4.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;31.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquivalent investment costs (\u0026euro;M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 316px;\"\u003e\n \u003cp\u003eEquivalent investment in electric ambulances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;9.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;59.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;11.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026euro;73.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: CO\u003csub\u003e2\u003c/sub\u003eeq: carbon dioxide equivalent; M: million; t: tons; VCR: vaccine coverage rate\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"carbon footprint, France, high dose influenza vaccine, influenza, patient care pathway, sustainability, vaccination","lastPublishedDoi":"10.21203/rs.3.rs-7470484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7470484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe healthcare system accounts for 8% of total carbon dioxide emissions in France. Vaccines are proven to mitigate the burden of infectious diseases and reduce healthcare utilization. This study aimed to assess the environmental impact on the French healthcare system of using a high-dose (HD) influenza vaccine instead of a standard-dose (SD), by modelling greenhouse gas (GHG) emissions in the care pathway for 65+ in France.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA model was developed applying GHG emission factors to avoided health outcomes through HD instead of SD vaccination, considering the increased GHG emissions from vaccine production. Outcomes from a health economic model populated with French epidemiological data were used, estimating avoided influenza-related primary care visits, emergency visits, and hospitalizations for the HD vaccine based on superior vaccine efficacy (24.2% higher for the HD vaccine than the SD vaccine). A vaccination coverage rate of 60% was used as a baseline and 75% as an exploratory scenario in line with World Health Organization (WHO) targets. Two hospitalization approaches were considered: 1/ based on influenza hospitalizations and 2/ including cardio-respiratory complications triggered by influenza. Emissions factors from France were used for hospitalizations, while UK data were adjusted and used for primary care visits and emergency visits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring an average season, if 60% of older adults were vaccinated with HD instead of SD, 5.0 kilotons of carbon dioxide equivalent (kt CO\u003csub\u003e2\u003c/sub\u003eeq) could be avoided per year considering hospitalizations for influenza, and 32.2 kt CO\u003csub\u003e2\u003c/sub\u003eeq when cardio-respiratory complications are included. At the 75% WHO target, from 6.2 to 40.2 kt CO\u003csub\u003e2\u003c/sub\u003eeq would be avoided. Avoided hospitalizations represented up to 95% of the avoided carbon emissions. The higher GHG emissions from HD vaccine production were compensated by avoided GHG emissions from prevented health outcomes in the approach for influenza hospitalizations, and the intervention exceeded the carbon-efficiency threshold when including cardio-respiratory complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite a higher carbon footprint from vaccine production, the use of an HD vaccine rather than an SD vaccine reduced GHG emissions throughout the care pathway and is therefore at least carbon efficient while delivering tangible public health benefits for 65+.\u003c/p\u003e","manuscriptTitle":"Modelling the Impact on Greenhouse Gas Emissions From Using a High Dose Compared With a Standard Dose Influenza Vaccine in Adults Aged 65 Years and Older in France","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 05:42:18","doi":"10.21203/rs.3.rs-7470484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-09T11:10:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-12T16:45:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T13:11:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T13:08:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-27T10:04:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ddf25657-c0af-4fa4-b7ef-4c940de97959","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T05:42:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 05:42:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7470484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7470484","identity":"rs-7470484","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}