Vaccine value profile for Klebsiella pneumoniae.

Klebsiella pneumoniae causes community- and healthcare-associated infections in children and adults. Globally in 2019, an estimated 1.27 million (95% Uncertainty Interval [UI]: 0.91-1.71) and 4.95 million (95% UI: 3.62-6.57) deaths were attributed to and associated with bacterial antimicrobial resistance (AMR), respectively. K. pneumoniae was the second leading pathogen in deaths attributed to AMR resistant bacteria. Furthermore, the rise of antimicrobial resistance in both community- and hospital-acquired infections is a concern for neonates and infants who are at high risk for invasive bacterial disease. There is a limited antibiotic pipeline for new antibiotics to treat multidrug resistant infections, and vaccines targeted against K. pneumoniae are considered to be of priority by the World Health Organization. Vaccination of pregnant women against K. pneumoniae could reduce the risk of invasive K.pneumoniae disease in their young offspring. In addition, vulnerable children, adolescents and adult populations at risk of K. pneumoniae disease with underlying diseases such as immunosuppression from underlying hematologic malignancy, chemotherapy, patients undergoing abdominal and/or urinary surgical procedures, or prolonged intensive care management are also potential target groups for a K. pneumoniae vaccine. A 'Vaccine Value Profile' (VVP) for K.pneumoniae, which contemplates vaccination of pregnant women to protect their babies from birth through to at least three months of age and other high-risk populations, provides a high-level, holistic assessment of the available information to inform the potential public health, economic and societal value of a pipeline of K. pneumoniae vaccines and other preventatives and therapeutics. This VVP was developed by a working group of subject matter experts from academia, non-profit organizations, public-private partnerships, and multi-lateral organizations, and in collaboration with stakeholders from the WHO. All contributors have extensive expertise on various elements of the K.pneumoniae VVP and collectively aimed to identify current research and knowledge gaps. The VVP was developed using only existing and publicly available information.

Costs-effectiveness and cost components of pharmaceutical and non-pharmaceutical interventions affecting antibiotic resistance outcomes in hospital patients: a systematic literature review.

INTRODUCTION: Limited information on costs and the cost-effectiveness of hospital interventions to reduce antibiotic resistance (ABR) hinder efficient resource allocation. METHODS: We conducted a systematic literature review for studies evaluating the costs and cost-effectiveness of pharmaceutical and non-pharmaceutical interventions aimed at reducing, monitoring and controlling ABR in patients. Articles published until 12 December 2023 were explored using EconLit, EMBASE and PubMed. We focused on critical or high-priority bacteria, as defined by the WHO, and intervention costs and incremental cost-effectiveness ratio (ICER). Following Preferred Reporting Items for Systematic review and Meta-Analysis guidelines, we extracted unit costs, ICERs and essential study information including country, intervention, bacteria-drug combination, discount rates, type of model and outcomes. Costs were reported in 2022 US dollars ($), adopting the healthcare system perspective. Country willingness-to-pay (WTP) thresholds from Woods et al 2016 guided cost-effectiveness assessments. We assessed the studies reporting checklist using Drummond's method. RESULTS: Among 20 958 articles, 59 (32 pharmaceutical and 27 non-pharmaceutical interventions) met the inclusion criteria. Non-pharmaceutical interventions, such as hygiene measures, had unit costs as low as $1 per patient, contrasting with generally higher pharmaceutical intervention costs. Several studies found that linezolid-based treatments for methicillin-resistant Staphylococcus aureus were cost-effective compared with vancomycin (ICER up to $21 488 per treatment success, all 16 studies' ICERs

The effectiveness and efficiency of asymptomatic SARS-CoV-2 testing strategies for patient and healthcare workers within acute NHS hospitals during an omicron-like period.

BACKGROUND: Asymptomatic SARS-CoV-2 testing of hospitalised patients began in April-2020, with twice weekly healthcare worker (HCW) testing introduced in November-2020. Guidance recommending asymptomatic testing was withdrawn in August-2022. Assessing the impact of this decision from data alone is challenging due to concurrent changes in infection prevention and control practices, community transmission rates, and a reduction in ascertainment rate from reduced testing. Computational modelling is an effective tool for estimating the impact of this change. METHODS: Using a computational model of SARS-CoV-2 transmission in an English hospital we estimate the effectiveness of several asymptomatic testing strategies, namely; (1) Symptomatic testing of patients and HCWs, (2) testing of all patients on admission with/without repeat testing on days 3 and 5-7, and (3) symptomatic testing plus twice weekly asymptomatic HCW testing with 70% compliance. We estimate the number of patient and HCW infections, HCW absences, number of tests, and tests per case averted or absence avoided, with differing community prevalence rates over a 12-week period. RESULTS: Testing asymptomatic patients on admission reduces the rate of nosocomial SARS-CoV-2 infection by 8.1-21.5%. Additional testing at days 3 and 5-7 post admission does not significantly reduce infection rates. Twice weekly asymptomatic HCW testing can reduce the proportion of HCWs infected by 1.0-4.4% and monthly absences by 0.4-0.8%. Testing asymptomatic patients repeatedly requires up to 5.5 million patient tests over the period, and twice weekly asymptomatic HCW testing increases the total tests to almost 30 million. The most efficient patient testing strategy (in terms of tests required to prevent a single patient infection) was testing asymptomatic patients on admission across all prevalence levels. The least efficient was repeated testing of patients with twice weekly asymptomatic HCW testing in a low prevalence scenario, and in all other prevalence levels symptomatic patient testing with regular HCW testing was least efficient. CONCLUSIONS: Testing patients on admission can reduce the rate of nosocomial SARS-CoV-2 infection but there is little benefit of additional post-admission testing. Asymptomatic HCW testing has little incremental benefit for reducing patient cases at low prevalence but has a potential role at higher prevalence or with low community transmission. A full health-economic evaluation is required to determine the cost-effectiveness of these strategies.

A systematic review on the excess health risk of antibiotic-resistant bloodstream infections for six key pathogens in Europe.

BACKGROUND: Antimicrobial resistance is a global threat, which requires novel intervention strategies, for which priority pathogens and settings need to be determined. OBJECTIVES: We evaluated pathogen-specific excess health burden of drug-resistant bloodstream infections (BSIs) in Europe. METHODS: A systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, and grey literature for the period January 1990 to May 2022. STUDY ELIGIBILITY CRITERIA: Studies that reported burden data for six key drug-resistant pathogens: carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, third-generation cephalosporin or CR Escherichia coli and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium. Excess health outcomes compared with drug-susceptible BSIs or uninfected patients. For MRSA and third-generation cephalosporin E. coli and K. pneumoniae BSIs, five or more European studies were identified. For all others, the search was extended to high-income countries. PARTICIPANTS: Paediatric and adult patients diagnosed with drug-resistant BSI. INTERVENTIONS: Not applicable. ASSESSMENT OF RISK OF BIAS: An adapted version of the Joanna-Briggs Institute assessment tool. METHODS OF DATA SYNTHESIS: Random-effect models were used to pool pathogen-specific burden estimates. RESULTS: We screened 7154 titles, 1078 full-texts and found 56 studies on BSIs. Most studies compared outcomes of drug-resistant to drug-susceptible BSIs (46/56, 82.1%), and reported mortality (55/56 studies, 98.6%). The pooled crude estimate for excess all-cause mortality of drug-resistant versus drug-susceptible BSIs ranged from OR 1.31 (95% CI 1.03-1.68) for CR P. aeruginosa to OR 3.44 (95% CI 1.62-7.32) for CR K. pneumoniae. Pooled crude estimates comparing mortality to uninfected patients were available for vancomycin-resistant Enterococcus and MRSA BSIs (OR of 11.19 [95% CI 6.92-18.09] and OR 6.18 [95% CI 2.10-18.17], respectively). CONCLUSIONS: Drug-resistant BSIs are associated with increased mortality, with the magnitude of the effect influenced by pathogen type and comparator. Future research should address crucial knowledge gaps in pathogen- and infection-specific burdens to guide development of novel interventions.

Excess resource use and cost of drug-resistant infections for six key pathogens in Europe: a systematic review and Bayesian meta-analysis.

BACKGROUND: Quantifying the resource use and cost of antimicrobial resistance establishes the magnitude of the problem and drives action. OBJECTIVES: Assessment of resource use and cost associated with infections with six key drug-resistant pathogens in Europe. METHODS: A systematic review and Bayesian meta-analysis. DATA SOURCES: MEDLINE (Ovid), Embase (Ovid), Econlit databases, and grey literature for the period 1 January 1990, to 21 June 2022. STUDY ELIGIBILITY CRITERIA: Resource use and cost outcomes (including excess length of stay, overall costs, and other excess in or outpatient costs) were compared between patients with defined antibiotic-resistant infections caused by carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, CR or third-generation cephalosporin Escherichia coli (3GCREC) and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium, and patients with drug-susceptible or no infection. PARTICIPANTS: All patients diagnosed with drug-resistant bloodstream infections (BSIs). INTERVENTIONS: NA. ASSESSMENT OF RISK OF BIAS: An adapted version of the Joanna Briggs Institute assessment tool, incorporating case-control, cohort, and economic assessment frameworks. METHODS OF DATA SYNTHESIS: Hierarchical Bayesian meta-analyses were used to assess pathogen-specific resource use estimates. RESULTS: Of 5969 screened publications, 37 were included in the review. Data were sparse and heterogeneous. Most studies estimated the attributable burden by, comparing resistant and susceptible pathogens (32/37). Four studies analysed the excess cost of hospitalization attributable to 3GCREC BSIs, ranging from -€ 2465.50 to € 6402.81. Eight studies presented adjusted excess length of hospital stay estimates for methicillin-resistant S. aureus and 3GCREC BSIs (4 each) allowing for Bayesian hierarchical analysis, estimating means of 1.26 (95% credible interval [CrI], -0.72 to 4.17) and 1.78 (95% CrI, -0.02 to 3.38) days, respectively. CONCLUSIONS: Evidence on most cost and resource use outcomes and across most pathogen-resistance combinations was severely lacking. Given the importance of this evidence for rational policymaking, further research is urgently needed.

AHHME: A model for estimating the holistic cost-effectiveness of antimicrobial resistance interventions in food animal production.

Antimicrobial resistance (AMR) is considered a global priority for human health, and reducing antimicrobial use in food animals has been suggested as a key area for interventions aiming to reduce resistant infections in humans. In addition to the effect on human health, such interventions may have effects across food animal productivity, healthcare sector costs, and the broader macroeconomy, but these effects are rarely captured in the AMR health economic literature. Without being able to estimate these effects, it is difficult to understand the true cost-effectiveness of antimicrobial stewardship interventions in food animal production, or to correctly design and prioritise such interventions. We explore and demonstrate the potential use of a novel compartment-based mathematical model to estimate the holistic cost-effectiveness of AMR-related interventions in food animal production from a One Health perspective. The Agriculture Human Health Micro-Economic model (AHHME) uses Markov state transition models to model the movement of humans and food animals between health states. It assigns values to these health states utilising empiric approaches, from the perspectives of human health, food animal productivity, labour productivity and healthcare sector costs. Providing AHHME open-source code and interactive online modelling tools allow for capacity building in AMR intervention modelling. This model represents a useful framework for capturing the cost-effectiveness of AMR-related interventions in food animal production in a more holistic way: it can allow us to capture the often-overlooked benefits of such interventions in like terms while considering distributional concerns. It also demonstrates that methodological assumptions such as willingness-to-pay thresholds and discount rates can be just as important to health decision models as epidemiological parameters, and allows these assumptions to be altered. We provide example outputs, and encourage researchers and policymakers to use and adapt our code to explore, design, and prioritise AMR-related interventions in their own country contexts.

The WHO costing and budgeting tool for national action plans on antimicrobial resistance-a practical addition to the WHOle toolkit.

Objectives: The development of national action plans (NAPs) for antimicrobial resistance (AMR) has been promoted and supported by the WHO, with recent support in the form of costing and budgeting tools to aid in finance-allocation decisions within governments. Methods: In this brief report we review this WHO costing and budgeting tool, discuss the strengths and weaknesses, and consider its place alongside other health economics and policy-support tools developed. Results: We call for future analyses of the costs of AMR NAPs to consider costs beyond that of only implementation, through use of other available, open-access data and tools. These include the Global Antimicrobial Resistance and Use Surveillance System (GLASS) data and One Health tools already within the existing 'WHO toolbox'. Conclusions: We suggest that future work on evaluating AMR along the impact pipeline use this toolbox where possible, ensuring empirical work is in turn open access.

Drivers of Antibiotic Use in Semi-Intensive Poultry Farms: Evidence from a Survey in Senegal.

Antimicrobial resistance (AMR), the capacity of microbial pathogens to survive in the presence of antimicrobials, is considered one of the greatest threats to human health worldwide and is growing rapidly in importance. AMR is thought to be driven in part by the use of antimicrobials (AMU) in livestock production. AMU reduction in agriculture is therefore important, but doing so may endanger farmers' livelihoods and hamper broader food security. Understanding the drivers for farmers' antibiotics use is essential for designing interventions which avoid harming agricultural output and to safeguard farmers' economic security. In this study, we analyse AMUSE survey data from poultry farmers in Senegal to explore the effects of vaccination, attitudes towards AMR, and biosecurity practices on: AMU, animal mortality, and farm productivity. We found that farmers with more "AMR-aware" attitudes may be less likely to use antibiotics in healthy birds. Stronger on-farm biosecurity was associated with less use of antibiotics in healthy birds, and in some specifications was linked to higher broiler productivity. Vaccination and AMU were both higher in farms with a higher disease prevalence, and both factors appeared conducive to higher broiler productivity. Overall, there is evidence that awareness raising and biosecurity improvements could encourage prudent use of antibiotics, and that biosecurity and vaccination could to some extent replace antibiotic use as productivity-enhancing and disease management tools in broiler farms. Finally, issues of farm antimicrobial stewardship must be considered at the structural level, with farm behaviours contingent on interaction with state and private stakeholders.

Extensions of Health Economic Evaluations in R for Microsoft Excel Users: A Tutorial for Incorporating Heterogeneity and Conducting Value of Information Analyses.

Advanced health economic analysis techniques currently performed in Microsoft Excel, such as incorporating heterogeneity, time-dependent transitions and a value of information analysis, can be easily transferred to R. Often the outputs of survival analyses (such as Weibull regression models) will estimate the impacts of correlated patient characteristics on patient outcomes, and are utilised directly as inputs for health economic decision models. This tutorial provides a step-by-step guide of how to conduct such analyses with a Markov model developed in R, and offers a comparison with established analyses performed in Microsoft Excel. This is done through the conversion of a previously published Microsoft Excel case study of a hip replacement surgery cost-effectiveness model. We hope that this paper can act as a facilitator in switching decision models from Microsoft Excel to R for complex health economic analyses, providing open-access code and data, suitable for future adaptation.

Health Economic Evaluation Using Markov Models in R for Microsoft Excel Users: A Tutorial.

A health economic evaluation (HEE) is a comparative analysis of alternative courses of action in terms of both costs and consequences. A cost-effectiveness analysis is a type of HEE that compares an intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. Cost-effectiveness analyses are commonly performed using Microsoft (MS) Excel. However, there is current interest in using other software that is better suited to more complex problems, methods, and data, as well as improved reproducibility and transparency. That is, it is increasingly important to be able to repeat an analysis of a particular data set and obtain the same results, and access the analysis and results in a clear and comprehensive openly available form. In this tutorial we provide a step-by-step guide on how to implement a mainstay model of HEE, namely a Markov model, in the statistical programming language R. The adoption of R for the purpose of cost-effectiveness analysis is highly dependent on the ability of the health economic modeller to understand, learn, and apply programming-type skills. R is likely to be less familiar than MS Excel for many modellers and so coding a cost-effectiveness model in R can be a large jump. We describe the technical details from the perspective of a MS Excel user to help bridge the gap between software and reduce the learning curve by providing for the first-time side-by-side comparisons of the Markov model example in MS Excel and R.

Changing food systems and infectious disease risks in low-income and middle-income countries.

The emergence of COVID-19 has drawn the attention of health researchers sharply back to the role that food systems can play in generating human disease burden. But emerging pandemic threats are just one dimension of the complex relationship between agriculture and infectious disease, which is evolving rapidly, particularly in low-income and middle-income countries (LMICs) that are undergoing rapid food system transformation. We examine this changing relationship through four current disease issues. The first is that greater investment in irrigation to improve national food security raises risks of vector-borne disease, which we illustrate with the case of malaria and rice in Africa. The second is that the intensification of livestock production in LMICs brings risks of zoonotic diseases like cysticercosis, which need to be managed as consumer demand grows. The third is that the nutritional benefits of increasing supply of fresh vegetables, fruit, and animal-sourced foods in markets in LMICs pose new food-borne disease risks, which might undermine supply. The fourth issue is that the potential human health risks of antimicrobial resistance from agriculture are intensified by changing livestock production. For each disease issue, we explore how food system transition is creating unintentional infectious disease risks, and what solutions might exist for these problems. We show that successfully addressing all of these challenges requires a coordinated approach between public health and agricultural sectors, recognising the costs and benefits of disease-reducing interventions to both, and seeking win-win solutions that are most likely to attract broad policy support and uptake by food systems.

Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England.

Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the "primary effects" of AMR. Previous estimates of the burden of AMR have largely ignored the potential "secondary effects," such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.

Quantifying the Relationship between Antibiotic Use in Food-Producing Animals and Antibiotic Resistance in Humans.

It is commonly asserted that agricultural production systems must use fewer antibiotics in food-producing animals in order to mitigate the global spread of antimicrobial resistance (AMR). In order to assess the cost-effectiveness of such interventions, especially given the potential trade-off with rural livelihoods, we must quantify more precisely the relationship between food-producing animal antimicrobial use and AMR in humans. Here, we outline and compare methods that can be used to estimate this relationship, calling on key literature in this area. Mechanistic mathematical models have the advantage of being rooted in epidemiological theory, but may struggle to capture relevant non-epidemiological covariates which have an uncertain relationship with human AMR. We advocate greater use of panel regression models which can incorporate these factors in a flexible way, capturing both shape and scale variation. We provide recommendations for future panel regression studies to follow in order to inform cost-effectiveness analyses of AMR containment interventions across the One Health spectrum, which will be key in the age of increasing AMR.

Stark choices: exploring health sector costs of policy responses to COVID-19 in low-income and middle-income countries.

OBJECTIVES: COVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios. METHODS: We used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs. RESULTS: COVID-19 clinical management costs vary greatly by country, ranging between <0.1%-12% of GDP and 0.4%-223% of total annual health expenditure (excluding out-of-pocket payments). Without mitigation policies, COVID-19 clinical management costs per capita range from US$43.39 to US$75.57; in 22 of 76 LMICs, these costs would surpass total annual health expenditure. In a scenario of stringent social distancing, costs per capita fall to US$1.10-US$1.32. CONCLUSIONS: We present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed.

Exploring equity in health and poverty impacts of control measures for SARS-CoV-2 in six countries.

BACKGROUND: Policy makers need to be rapidly informed about the potential equity consequences of different COVID-19 strategies, alongside their broader health and economic impacts. While there are complex models to inform both potential health and macro-economic impact, there are few tools available to rapidly assess potential equity impacts of interventions. METHODS: We created an economic model to simulate the impact of lockdown measures in Pakistan, Georgia, Chile, UK, the Philippines and South Africa. We consider impact of lockdown in terms of ability to socially distance, and income loss during lockdown, and tested the impact of assumptions on social protection coverage in a scenario analysis. RESULTS: In all examined countries, socioeconomic status (SES) quintiles 1-3 were disproportionately more likely to experience income loss (70% of people) and inability to socially distance (68% of people) than higher SES quintiles. Improving social protection increased the percentage of the workforce able to socially distance from 48% (33%-60%) to 66% (44%-71%). We estimate the cost of this social protection would be equivalent to an average of 0.6% gross domestic product (0.1% Pakistan-1.1% Chile). CONCLUSIONS: We illustrate the potential for using publicly available data to rapidly assess the equity implications of social protection and non-pharmaceutical intervention policy. Social protection is likely to mitigate inequitable health and economic impacts of lockdown. Although social protection is usually targeted to the poorest, middle quintiles will likely also need support as they are most likely to suffer income losses and are disproportionately more exposed.

The effectiveness of biosecurity interventions in reducing the transmission of bacteria from livestock to humans at the farm level: A systematic literature review.

Zoonotic bacterial infections are a health hazard for people who are in regular contact with livestock at the farm level. Improved biosecurity can limit zoonotic pathogen transmission within farms. The aim of this review was to summarize the effectiveness of farm-level biosecurity interventions in reducing bacterial transmission from animals to people who lived, worked in or visited farms. A systematic literature review was conducted using Embase, Ovid Medline and Agris databases, which were searched on 7th of July 2019, limited to English language papers but with no time exclusion criteria. A narrative synthesis was undertaken utilizing the Centre for Reviews and Dissemination approach, reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias within and across the included studies was performed using established checklists. Out of 869 studies retrieved through database searches, 11 studies were selected. In addition, three studies were found through study reference lists. Fourteen studies were therefore included in this review. Biosecurity interventions were grouped into five categories: hand washing, sanitization and hygienic measures (six studies); personal protective equipment (five studies); vaccination (two studies); other interventions (e.g. air ventilation flap) (four studies); and routine farm activities (two studies). Across studies that investigated odds of human colonization or infection (three studies), odds were seen to both be increased and decreased through use of tested biosecurity measures. Large confidence intervals that often crossed the threshold of an odds ratio equal to 1 were found. Most of the studies' overall risk of bias was 'medium risk' (11 studies), with selection bias domains generally being scored 'medium risk.' Biosecurity interventions are potentially beneficial in reducing bacterial transmission from animals to humans. However, more high-quality evidence is needed to increase certainty in which interventions, in which contexts, are most effective from the human health perspective.

Quantitatively evaluating the cross-sectoral and One Health impact of interventions: A scoping review and case study of antimicrobial resistance.

BACKGROUND: Current frameworks evaluating One Health (OH) interventions focus on intervention-design and -implementation. Cross-sectoral impact evaluations are needed to more effectively tackle OH-issues, such as antimicrobial resistance (AMR). We aimed to describe quantitative evaluation methods for interventions related to OH and cross-sectoral issues, to propose an explicit approach for evaluating such interventions, and to apply this approach to AMR. METHODS: A scoping review was performed using WebofScience, EconLit, PubMed and gray literature. Quantitative evaluations of interventions that had an impact across two or more of the human, animal and environment sectors were included. Information on the interventions, methods and outcome measures found was narratively summarised. The information from this review informed the construction of a new approach to OH-related intervention evaluation, which then was applied to the field of AMR. RESULTS: The review included 90 studies: 73 individual evaluations (from 72 papers) and 18 reviews, with a range of statistical modelling (n = 13 studies), mathematical modelling (n = 53) and index-creation/preference-ranking (n = 14) methods discussed. The literature highlighted the need to (I) establish stakeholder objectives, (II) establish quantifiable outcomes that feed into those objectives, (III) establish agents and compartments that affect these outcomes and (IV) select appropriate methods (described in this review) accordingly. Based on this, an evaluation model for AMR was conceptualised; a decision-tree of intervention options, a compartmental-microeconomic model across sectors and a general-equilibrium (macroeconomic) model are linked. The outcomes of this multi-level model (including cost-utility and Gross Domestic Product impact) can then feed into multi-criteria-decision analyses that weigh respective impact estimates alongside other chosen outcome estimates (for example equity or uncertainty). CONCLUSION: In conclusion, stakeholder objectives are key in establishing which evaluation methods (and associated outcome measures) should be used for OH-related interventions. The stated multi-level approach also allows for sub-systems to be modelled in succession, where resources are constrained.

Code-Sharing in Cost-of-Illness Calculations: An Application to Antibiotic-Resistant Bloodstream Infections.

Background: More data-driven evidence is needed on the cost of antibiotic resistance. Both Japan and England have large surveillance and administrative datasets. Code sharing of costing models enables reduced duplication of effort in research. Objective: To estimate the burden of antibiotic-resistant Staphylococcus aureus bloodstream infections (BSIs) in Japan, utilizing code that was written to estimate the hospital burden of antibiotic-resistant Escherichia coli BSIs in England. Additionally, the process in which the code-sharing and application was performed is detailed, to aid future such use of code-sharing in health economics. Methods: National administrative data sources were linked with voluntary surveillance data within the Japan case study. R software code, which created multistate models to estimate the excess length of stay associated with different exposures of interest, was adapted from previous use and run on this dataset. Unit costs were applied to estimate healthcare system burden in 2017 international dollars (I$). Results: Clear supporting documentation alongside open-access code, licensing, and formal communication channels, helped the re-application of costing code from the English setting within the Japanese setting. From the Japanese healthcare system perspective, it was estimated that there was an excess cost of I$6,392 per S. aureus BSI, whilst oxacillin resistance was associated with an additional I$8,155. Conclusions:S. aureus resistance profiles other than methicillin may substantially impact hospital costs. The sharing of costing models within the field of antibiotic resistance is a feasible way to increase burden evidence efficiently, allowing for decision makers (with appropriate data available) to gain rapid cost-of-illness estimates.

The health and cost burden of antibiotic resistant and susceptible Escherichia coli bacteraemia in the English hospital setting: A national retrospective cohort study.

INTRODUCTION: Antibiotic resistance poses a threat to public health and healthcare systems. Escherichia coli causes more bacteraemia episodes in England than any other bacterial species. This study aimed to estimate the burden of E. coli bacteraemia and associated antibiotic resistance in the secondary care setting. MATERIALS AND METHODS: This was a retrospective cohort study, with E. coli bacteraemia as the main exposure of interest. Adult hospital in-patients, admitted to acute NHS hospitals between July 2011 and June 2012 were included. English national surveillance and administrative datasets were utilised. Cox proportional hazard, subdistribution hazard and multistate models were constructed to estimate rate of discharge, rate of in-hospital death and excess length of stay, with a unit bed day cost applied to the latter to estimate cost burden from the healthcare system perspective. RESULTS: 14,042 E. coli bacteraemia and 8,919,284 non-infected inpatient observations were included. E. coli bacteraemia was associated with an increased rate of in-hospital death across all models, with an adjusted subdistribution hazard ratio of 5.88 (95% CI: 5.62-6.15). Resistance was not found to be associated with in-hospital mortality once adjusting for patient and hospital covariates. However, resistance was found to be associated with an increased excess length of stay. This was especially true for third generation cephalosporin (1.58 days excess length of stay, 95% CI: 0.84-2.31) and piperacillin/tazobactam resistance (1.23 days (95% CI: 0.50-1.95)). The annual cost of E. coli bacteraemia was estimated to be £14,346,400 (2012 £), with third-generation cephalosporin resistance associated with excess costs per infection of £420 (95% CI: 220-630). CONCLUSIONS: E. coli bacteraemia places a statistically significant burden on patient health and the hospital sector in England. Resistance to front-line antibiotics increases length of stay; increasing the cost burden of such infections in the secondary care setting.

Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets.

Background: Antibiotics are most often prescribed empirically, meaning that they are used to treat infection syndromes prior to identification of the causative bacteria and their susceptibility to antibiotics. The effectiveness of antibiotic therapies is now compromised by the emergence and spread of antibiotic-resistant bacteria. Guidelines on empiric antibiotic therapy are a key component of effective clinical care for infection syndromes, as treatment needs to be informed by knowledge of likely aetiology and bacterial resistance patterns. Methods: We used open-access antimicrobial resistance (AMR) surveillance datasets, including the newly available ATLAS dataset from Pfizer, to derive a composite index of antibiotic resistance for common infection syndromes. Results: We developed a framework that integrated data on antibiotic prescribing guidelines, aetiology of infections, access to and cost of antibiotics, with antibiotic susceptibilities from global AMR surveillance datasets to create an empirical prescribing index. The results are presented in an interactive web app to allow users to visualise underlying resistance rates to first-line empiric antibiotics for their infection syndromes and countries of interest. Conclusions: We found that whilst an index for empiric antibiotic therapy based on resistance data can technically be created, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets (ECDC Surveillance Atlas, CDDEP ResistanceMap and WHO GLASS datasets) are largely limited to bacteraemia-derived specimens and cannot directly inform treatment of other infection syndromes. With improving data availability on international rates of AMR and better understanding of infection aetiology, our approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data. Syndrome-level resistance could be a more clinically relevant measure of resistance to inform on the appropriateness of empiric antibiotic therapies at the country-level.