Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--5

The economic rationale for cell-based influenza vaccines in children and adults: A review of cost-effectiveness analyses

Seasonal influenza significantly affects both health and economic costs in children and adults. This narrative review summarizes published cost-effectiveness analyses (CEAs) of cell-based influenza vaccines in children and adults <65 years of age, critically assesses the assumptions and approaches used in these analyses, and considers the role of cell-based influenza vaccines for children and adults. CEAs from multiple countries demonstrated the cost-effectiveness of cell-based quadrivalent influenza vaccines (QIVc) compared with egg-based trivalent/quadrivalent influenza vaccines (TIVe/QIVe). CEA findings were consistent across models relying on different relative vaccine effectiveness (rVE) estimate inputs, with the rVE of QIVc versus QIVe ranging from 8.1% to 36.2% in favor of QIVc. Across multiple scenarios and types of analyses, QIVc was consistently cost-effective compared with QIVe, including in children and adults across different regions of the world.

Projecting the Potential Clinical and Economic Impact of Human Immunodeficiency Virus Prevention Resource Reallocation in Tennessee

BACKGROUND

In 2023, Tennessee replaced $6.2 M in US Centers for Disease Control and Prevention (CDC) human immunodeficiency virus (HIV) prevention funding with state funds to redirect support away from men who have sex with men (MSM), transgender women (TGW), and heterosexual Black women (HSBW) and to prioritize instead first responders (FR), pregnant people (PP), and survivors of sex trafficking (SST).

METHODS

We used a simulation model of HIV disease to compare the clinical impact of Current, the present allocation of condoms, preexposure prophylaxis (PrEP), and HIV testing to CDC priority risk groups (MSM/TGW/HSBW); with Reallocation, funding instead increased HIV testing and linkage of Tennessee-determined priority populations (FR/PP/SST). Key model inputs included baseline condom use (45%-49%), PrEP provision (0.1%-8%), HIV testing frequency (every 2.5-4.8 years), and 30-day HIV care linkage (57%-65%). We assumed Reallocation would reduce condom use (-4%), PrEP provision (-26%), and HIV testing (-47%) in MSM/TGW/HSBW, whereas it would increase HIV testing among FR (+47%) and HIV care linkage (to 100%/90%) among PP/SST.

RESULTS

Reallocation would lead to 166 additional HIV transmissions, 190 additional deaths, and 843 life-years lost over 10 years. HIV testing reductions were most influential in sensitivity analysis; even a 24% reduction would result in 287 more deaths compared to Current. With pessimistic assumptions, we projected 1359 additional HIV transmissions, 712 additional deaths, and 2778 life-years lost over 10 years.

CONCLUSIONS

Redirecting HIV prevention funding in Tennessee would greatly harm CDC priority populations while conferring minimal benefits to new priority populations.

Masks and respirators for prevention of respiratory infections: a state of the science review

SUMMARYThis narrative review and meta-analysis summarizes a broad evidence base on the benefits-and also the practicalities, disbenefits, harms and personal, sociocultural and environmental impacts-of masks and masking. Our synthesis of evidence from over 100 published reviews and selected primary studies, including re-analyzing contested meta-analyses of key clinical trials, produced seven key findings. First, there is strong and consistent evidence for airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory pathogens. Second, masks are, if correctly and consistently worn, effective in reducing transmission of respiratory diseases and show a dose-response effect. Third, respirators are significantly more effective than medical or cloth masks. Fourth, mask mandates are, overall, effective in reducing community transmission of respiratory pathogens. Fifth, masks are important sociocultural symbols; non-adherence to masking is sometimes linked to political and ideological beliefs and to widely circulated mis- or disinformation. Sixth, while there is much evidence that masks are not generally harmful to the general population, masking may be relatively contraindicated in individuals with certain medical conditions, who may require exemption. Furthermore, certain groups (notably D/deaf people) are disadvantaged when others are masked. Finally, there are risks to the environment from single-use masks and respirators. We propose an agenda for future research, including improved characterization of the situations in which masking should be recommended or mandated; attention to comfort and acceptability; generalized and disability-focused communication support in settings where masks are worn; and development and testing of novel materials and designs for improved filtration, breathability, and environmental impact.

Use of Adjuvanted Quadrivalent Influenza Vaccine in Older-Age Adults: A Systematic Review of Economic Evidence

Influenza vaccination is an important public health measure that can reduce disease burden, especially among older persons (those aged 65 and over) who have weaker immune systems. Evidence suggests enhanced vaccines, including adjuvanted quadrivalent vaccines (aQIV), may be particularly effective in this group. This study reports the results of a systematic review of the cost-effectiveness of aQIV in this population. The review was undertaken and reported in accordance with good practice guidelines. Medline and EMBASE were searched from 2013 to the present. Pre-selected eligibility criteria were employed and quality assessment undertaken using the Consensus Health Economic Criteria (CHEC-extended) checklist and Consolidated Health Economic Evaluation Reporting Standard (CHEERS) 2022 checklists. A total of 124 records were returned, with 10 full text papers retained. All were modelling studies and exhibited heterogeneity in approach, perspective, and parameter estimation. Nine papers reported cost-effectiveness ranging from EUR 6694/QALY to EUR 20,000/QALY in evaluations employing a payer perspective and from EUR 3936/QALY to EUR 17,200/QALY in those using a societal perspective. Results remained robust to a range of sensitivity analyses. One paper that reported contrary findings adopted a distinct modelling approach. It is reasonable to conclude that there is a broad consensus as to the cost-effectiveness of aQIV in this population group.

Structured Literature Review to Identify Human Papillomavirus's Natural History Parameters for Dynamic Population Models of Vaccine Impacts

Human papillomavirus (HPV) is a common sexually transmitted virus that can cause cervical cancer and other diseases. Dynamic transmission models (DTMs) have been developed to evaluate the health and economic impacts of HPV vaccination. These models typically include many parameters, such as natural history of the disease, transmission, demographic, behavioral, and screening. To ensure the accuracy of DTM projections, it is important to parameterize them with the best available evidence. This study aimed to identify and synthesize data needed to parametrize DTMs on the natural history of HPV infection and related diseases. Parameters describing data of interest were grouped by their anatomical location (genital warts, recurrent respiratory papillomatosis, and cervical, anal, vaginal, vulvar, head and neck, and penile cancers), and natural history (progression, regression, death, cure, recurrence, detection), and were identified through a systematic literature review (SLR) and complementary targeted literature reviews (TLRs). The extracted data were then synthesized by pooling parameter values across publications, and summarized using the range of values across studies reporting each parameter and the median value from the most relevant study. Data were extracted and synthesized from 223 studies identified in the SLR and TLRs. Parameters frequently reported pertained to cervical cancer outcomes, while data for other anatomical locations were less available. The synthesis of the data provides a large volume of parameter values to inform HPV DTMs, such as annual progression rates from cervical intraepithelial neoplasia (CIN) 1 to CIN 2+ (median of highest quality estimate 0.0836), CIN 2 to CIN 3+ (0.0418), carcinoma in situ (CIS) 2 to local cancer+ (0.0396), and regional to distant cancer (0.0474). Our findings suggest that while there is a large body of evidence on cervical cancer, parameter values featured substantial heterogeneity across studies, and further studies are needed to better parametrize the non-cervical components of HPV DTMs.

A Practical Guide to Full Value of Vaccine Assessments

Articulating the wide range of health, social and economic benefits that vaccines offer may help to overcome obstacles in the vaccine development pipeline. A framework to guide the assessment and communication of the value of a vaccine-the Full Value of Vaccine Assessment (FVVA)-has been developed by the WHO. The FVVA framework offers a holistic assessment of the value of vaccines, providing a synthesis of evidence to inform the public health need of a vaccine, describing the supply and demand aspects, its market and its impact from a health, financial and economic perspective. This paper provides a practical guide to how FVVAs are developed and used to support investment in vaccines, ultimately leading to sustained implementation in countries. The FVVA includes a range of elements that can be broadly categorised as synthesis, vaccine development narrative and defining vaccine impact and value. Depending on the features of the disease/vaccine in question, different elements may be emphasised; however, a standardised set of elements is recommended for each FVVA. The FVVA should be developed by an expert group who represent a range of stakeholders, perspectives and geographies and ensure a fair, coherent and evidence-based assessment of vaccine value.

The economic burden of Ebola virus disease: a review and recommendations for analysis

BACKGROUND

Ebola virus disease (EVD) continues to be a major public health threat globally, particularly in the low-and-middle-income countries (LMICs) of Africa. The social and economic burdens of EVD are substantial and have triggered extensive research into prevention and control. We aim to highlight the impact and economic implications, identify research gaps, and offer recommendations for future economic studies pertaining to EVD.

METHOD

We conducted a comprehensive librarian-led search in PubMed/Medline, Embase, Google Scholar, EconLit and Scopus for economic evaluations of EVD. After study selection and data extraction, findings on the impact and economics of EVD were synthesized using a narrative approach, while identifying gaps, and recommending critical areas for future EVD economic studies.

RESULTS

The economic evaluations focused on the burden of illness, vaccine cost-effectiveness, willingness-to-pay for a vaccine, EVD funding, and preparedness costs. The estimated economic impact of the 2014 EVD outbreak in Guinea, Liberia, and Sierra Leone across studies ranged from $30 billion to $50 billion. Facility construction and modification emerged as significant cost drivers for preparedness. The EVD vaccine demonstrated cost-effectiveness in a dynamic transmission model; resulting in an incremental cost-effectiveness ratio of about $96 per additional disability adjusted life year averted. Individuals exhibited greater willingness to be vaccinated if it incurred no personal cost, with a minority willing to pay about $1 for the vaccine.

CONCLUSIONS

The severe impact of EVD puts pressure on governments and the international community for better resource utilization and re-allocation. Several technical and methodological issues related to economic evaluation of EVD remain to be addressed, especially for LMICs. We recommend conducting cost-of-sequelae and cost-of-distribution analyses in addition to adapting existing economic analytical methods to EVD. Characteristics of the affected regions should be considered to provide evidence-based economic plans and economic-evaluation of mitigations that enhance resource allocation for prevention and treatment.

Effective contact tracing for COVID-19: A systematic review

Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. Following PRISMA guidelines, we searched four databases using a range of terms related to contact tracing effectiveness for COVID-19. We found 343 papers; 32 were included. All were observational or modelling studies. Observational studies (n = 14) provided consistent, very-low certainty evidence that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19 (e.g. in Hong Kong, only 1084 cases and four deaths were recorded in the first 4.5 months of the pandemic). Modelling studies (n = 18) provided consistent, high-certainty evidence that under assumptions of prompt and thorough tracing with effective quarantines, contact tracing could stop the spread of COVID-19 (e.g. by reducing the reproduction number from 2.2 to 0.57). A cautious interpretation indicates that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts.

Cost-effectiveness analysis of HIV pre-exposure prophylaxis in Japan

BACKGROUND

While global efforts have been made to prevent transmission of HIV, the epidemic persists. Men who have sex with men (MSM) are at high risk of infection. Despite evidence of its cost-effectiveness in other jurisdictions, pre-exposure prophylaxis (PrEP) for MSM is neither approved nor reimbursed in Japan.

METHOD

The cost-effectiveness analysis compared the use of once daily PrEP versus no PrEP among MSM over a 30-year time horizon from a national healthcare perspective. Epidemiological estimates for each of the 47 prefectures informed the model. Costs included HIV/AIDS treatment, HIV and testing for sexually transmitted infections, monitoring tests and consults, and hospitalization costs. Analyses included health and cost outcomes, as well as the incremental cost-effectiveness ratio (ICER) reported as the cost per quality-adjusted life year (QALY) for all of Japan and each prefecture. Sensitivity analyses were performed.

FINDINGS

The estimated proportion of HIV infections prevented with the use of PrEP ranged from 48% to 69% across Japan, over the time horizon. Cost savings due to lower monitoring costs and general medical costs were observed. Assuming 100% coverage, for Japan overall, daily use of PrEP costs less and was more effective; daily use of PrEP was cost-effective at a willingness to pay threshold of ¥5,000,000 per QALY in 32 of the 47 prefectures. Sensitivity analyses found that the ICER was most sensitive to the cost of PrEP.

INTERPRETATION

Compared to no PrEP use, once daily PrEP is a cost-effective strategy in Japanese MSM, reducing the clinical and economic burden associated with HIV.

Estimating the Cost-Effectiveness of Switching to Higher-Valency Pediatric Pneumococcal Conjugate Vaccines in the United Kingdom

Currently, the 13-valent pneumococcal conjugate vaccine (PCV13) is administered under a 1+1 (1 primary dose) pediatric schedule in the United Kingdom (UK). Higher-valency PCVs, 15-valent PCV (PCV15), or 20-valent PCV (PCV20) might be considered to expand serotype coverage. We evaluated the cost-effectiveness of PCV20 or PCV15 using either a 2+1 (2 primary doses) or 1+1 schedule for pediatric immunization in the UK. Using a dynamic transmission model, we simulated future disease incidence and costs under PCV13 1+1, PCV20 2+1, PCV20 1+1, PCV15 2+1, and PCV15 1+1 schedules from the UK National Health Service perspective. We prospectively estimated disease cases, direct costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio. Scenario analyses were performed to estimate the impact of model assumptions and parameter uncertainty. Over a five-year period, PCV20 2+1 averted the most disease cases and gained the most additional QALYs. PCV20 2+1 and 1+1 were dominant (cost-saving and more QALYs gained) compared with PCV15 (2+1 or 1+1) and PCV13 1+1. PCV20 2+1 was cost-effective (GBP 8110/QALY) compared with PCV20 1+1. PCV20 was found cost-saving compared with PCV13 1+1, and PCV20 2+1 was cost-effective compared with PCV20 1+1. Policymakers should consider the reduction in disease cases with PCV20, which may offset vaccination costs.

Infectious Disease Modelling of HIV Prevention Interventions: A Systematic Review and Narrative Synthesis of Compartmental Models

BACKGROUND

The HIV epidemic remains a major public health problem. Critical to transmission control are HIV prevention strategies with new interventions continuing to be developed. Mathematical models are important for understanding the potential impact of these interventions and supporting policy decisions. This systematic review aims to answer the following question: when a new HIV prevention intervention is being considered or designed, what information regarding it is necessary to include in a compartmental model to provide useful insights to policy makers? The primary objective of this review is therefore to assess suitability of current compartmental HIV prevention models for informing policy development.

METHODS

Articles published in EMBASE, Medline, Econlit, and Global Health were screened. Included studies were identified using permutations of (i) HIV, (ii) pre-exposure prophylaxis (PrEP), circumcision (both voluntary male circumcision [VMMC] and early-infant male circumcision [EIMC]), and vaccination, and (iii) modelling. Data extraction focused on study design, model structure, and intervention incorporation into models. Article quality was assessed using the TRACE (TRAnsparent and Comprehensive Ecological modelling documentation) criteria for mathematical models.

RESULTS

Of 837 articles screened, 48 articles were included in the review, with 32 unique mathematical models identified. The substantial majority of studies included PrEP (83%), whilst fewer modelled circumcision (54%), and only a few focussed on vaccination (10%). Data evaluation, implementation verification, and model output corroboration were identified as areas of poorer model quality. Parameters commonly included in the mathematical models were intervention uptake and effectiveness, with additional intervention-specific common parameters identified. We identified key modelling gaps; critically, models insufficiently incorporate multiple interventions acting simultaneously. Additionally, population subgroups were generally poorly represented-with future models requiring improved incorporation of ethnicity and sexual risk group stratification-and many models contained inappropriate data in parameterisation which will affect output accuracy.

CONCLUSIONS

This review identified gaps in compartmental models to date and suggests areas of improvement for models focusing on new prevention interventions. Resolution of such gaps within future models will ensure greater robustness and transparency, and enable more accurate assessment of the impact that new interventions may have, thereby providing more meaningful guidance to policy makers.

Estimated cost-effectiveness of point-of-care testing in community pharmacies vs. self-testing and standard laboratory testing for HIV

OBJECTIVE

Point-of-care-testing (POCT) for HIV at community pharmacies can enhance care linkage compared with self-tests and increase testing uptake relative to standard lab testing. While the higher test uptake may increase testing costs, timely diagnosis and treatment can reduce downstream HIV treatment costs and improve health outcomes. This study provides the first evidence on the cost-effectiveness of pharmacist-led POCT vs. HIV self-testing and standard lab testing.

DESIGN

Dynamic transmission model.

METHODS

We compared three HIV testing strategies: POCT at community pharmacies; self-testing using HIV self-test kits; and standard lab testing. Analyses were conducted from the Canadian health system perspective over a 30-year time horizon for all individuals aged 15-64 years in Canada. Costs were measured in 2021 Canadian dollars and effectiveness was captured using quality-adjusted life-years (QALYs).

RESULTS

Compared with standard lab testing, POCT at community pharmacies would save $885 million in testing costs over 30 years. Though antiretroviral treatment costs would increase by $190 million with POCT as more persons living with HIV are identified and treated, these additional costs would be partly offset by their lower downstream healthcare utilization (savings of $150 million). POCT at community pharmacies would also yield over 5000 additional QALYs. Compared with HIV self-testing, POCT at community pharmacies would generate both higher costs and higher QALYs and would be cost-effective with an incremental cost-effectiveness ratio of $47 475 per QALY gained.

CONCLUSIONS

Offering POCT at community pharmacies can generate substantial cost savings and improve health outcomes compared with standard lab testing. It would also be cost-effective vs. HIV self-testing.

Systematic review of applied mathematical models for the control of Schistosoma japonicum

BACKGROUND

Schistosoma japonicum remains endemic in China and the Philippines. Substantial progress has been made in the control of Japonicum in both China and the Philippines. China is reaching elimination thanks to a concerted effort of control strategies. Mathematical modelling has been a key tool in the design of control strategies, in place of expensive randomised-controlled trials. We conducted a systematic review to investigate mathematical models of Japonicum control strategies in China and the Philippines.

METHODS

We conducted a systematic review on July 5, 2020, in four electronic bibliographic databases - PubMed, Web of Science, SCOPUS and Embase. Articles were screened for relevance and for meeting the inclusion criteria. Data extracted included authors, year of publication, year of data collection, setting and ecological context, objectives, control strategies, main findings, the form and content of the model including its background, type, representation of population dynamics, heterogeneity of hosts, simulation period, source of parameters, model validation and sensitivity analysis. Results After screening, 19 eligible papers were included in the systematic review. Seventeen considered control strategies in China and two in the Philippines. Two frameworks were identified; the mean-worm burden framework and the prevalence-based framework, the latter of which increasingly common. Most models considered human and bovine definitive hosts. There were mixed additional elements included in the models, such as alternative definitive hosts and the role of seasonality and weather. Models generally agreed upon the need for an integrated control strategy rather than reliance on mass drug administration alone to sustain reductions in prevalence.

CONCLUSIONS

Mathematical modelling of Japonicum has converged from multiple approaches to modelling using the prevalence-based framework with human and bovine definitive hosts and find integrated control strategies to be most effective. Further research could investigate the role of other definitive hosts and model the effect of seasonal fluctuations in transmission.

Health economic analyses of latent tuberculosis infection screening and preventive treatment among people living with HIV in lower tuberculosis incidence settings: a systematic review

Introduction: In lower tuberculosis (TB) incidence countries (<100 cases/100,000/year), screening and preventive treatment (PT) for latent TB infection (LTBI) among people living with HIV (PLWH) is often recommended, yet guidelines advising which groups to prioritise for screening can be contradictory and implementation patchy. Evidence of LTBI screening cost-effectiveness may improve uptake and health outcomes at reasonable cost. Methods: Our systematic review assessed cost-effectiveness estimates of LTBI screening/PT strategies among PLWH in lower TB incidence countries to identify model-driving inputs and methodological differences. Databases were searched 1980-2020. Studies including health economic evaluation of LTBI screening of PLWH in lower TB incidence countries (<100 cases/100,000/year) were included. Results: Of 2,644 articles screened, nine studies were included. Cost-effectiveness estimates of LTBI screening/PT for PLWH varied widely, with universal screening/PT found highly cost-effective by some studies, while only targeting to high-risk groups (such as those from mid/high TB incidence countries) deemed cost-effective by others. Cost-effectiveness of strategies screening all PLWH from studies published in the past five years varied from US$2828 to US$144,929/quality-adjusted life-year gained (2018 prices). Study quality varied, with inconsistent reporting of methods and results limiting comparability of studies. Cost-effectiveness varied markedly by screening guideline, with British HIV Association guidelines more cost-effective than NICE guidelines in the UK. Discussion: Cost-effectiveness studies of LTBI screening/PT for PLWH in lower TB incidence settings are scarce, with large variations in methods and assumptions used, target populations and screening/PT strategies evaluated. The limited evidence suggests LTBI screening/PT may be cost-effective for some PLWH groups but further research is required, particularly on strategies targeting screening/PT to PLWH at higher risk. Standardisation of model descriptions and results reporting could facilitate reliable comparisons between studies, particularly to identify those factors driving the wide disparity between cost-effectiveness estimates. Registration: PROSPERO CRD42020166338 (18/03/2020).

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Objectives

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.

Methods

To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.

Results

Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.

Conclusions

Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Cost-utility analysis of the universal pneumococcal vaccination programme for older adults in Norway

The aim of this study was to establish whether the universal pneumococcal vaccination for older adults in Norway is likely to be cost-effective from the perspective of the health care provider. A decision tree model developed by the Public Health Agency of Sweden was adapted to the Norwegian setting. Two cohorts, consisting of 65-year-olds and 75-year-olds grouped into vaccinated and unvaccinated, were followed over a 5-year time horizon. In the base case, the 23-valent polysaccharide vaccine (PPV23) was used while the 13-valent pneumococcal conjugate vaccine (PCV13) was included in scenario analyses only. The costs and health benefits (measured in quality adjusted life years (QALY) gained) were compared in the two cohorts between the vaccinated and unvaccinated groups. The impact of indirect effects of the vaccine, such as herd immunity and serotype replacement, were not investigated. The relative importance of change in price was assessed by performing one-way sensitivity analyses. Under base-case assumptions, the programme for the 75-year-old cohort is expected to be dominant (cost-effective) from the health care perspective at the current maximal pharmacy retail price and at 75% vaccination coverage. In comparison, for the 65-year-old cohort the cost per QALY gained is approximately NOK 601,784 (EUR 61,281) under the base-case assumptions. A reduction in the cost of the vaccine to one quarter of its current level also brings the cost per QALY gained within the acceptable ranges in a Norwegian context for both the 65- and 75-year-old cohorts. There is no exact cost-effectiveness threshold in Norway. However, introducing a vaccination programme against pneumococcal disease for 65-year-olds in Norway is likely to fall within the acceptable range while for the 75-year-old cohort the universal programme appears to be dominant (cost-effective).

Assessing the Cost-Utility of Universal Hepatitis B Vaccination Among Adults

BACKGROUND

Although effective against hepatitis B virus (HBV) infection, hepatitis B (HepB) vaccination is only recommended for infants, children, and adults at higher risk. We conducted an economic evaluation of universal HepB vaccination among US adults.

METHODS

Using a decision analytic model with Markov disease progression, we compared current vaccination recommendations (baseline) with either 3-dose or 2-dose universal HepB vaccination (intervention strategies). In simulated modeling of 1 million adults distributed by age and risk groups, we quantified health benefits (quality-adjusted life years, QALYs) and costs for each strategy. Multivariable probabilistic sensitivity analyses identified key inputs. All costs reported in 2019 US dollars.

RESULTS

With incremental base-case vaccination coverage up to 50% among persons at lower risk and 0% increment among persons at higher risk, each of 2 intervention strategies averted nearly one-quarter of acute HBV infections (3-dose strategy, 24.8%; 2-dose strategy, 24.6%). Societal incremental cost per QALY gained of $152 722 (interquartile range, $119 113-$235 086) and $155 429 (interquartile range, $120 302-$242 226) were estimated for 3-dose and 2-dose strategies, respectively. Risk of acute HBV infection showed the strongest influence.

CONCLUSIONS

Universal adult vaccination against HBV may be an appropriate strategy for reducing HBV incidence and improving resulting health outcomes.

Ku-gaa-gii pimitizi-win, the COVID-19 cohort study of people experiencing homelessness in Toronto, Canada: a study protocol

Introduction

Initial reports suggest people experiencing homelessness (PEH) are at high risk for SARS-CoV-2 infection and associated morbidity and mortality. However, there have been few longitudinal evaluations of the spread and impact of COVID-19 among PEH. This study will estimate the prevalence and incidence of COVID-19 infections in a cohort of PEH followed prospectively in Toronto, Canada. It will also examine associations between individual-level and shelter-level characteristics with COVID-19 infection, adverse health outcomes related to infection and vaccination. Finally, the data will be used to develop and parameterise a mathematical model to characterise SARS-CoV-2 transmission dynamics, and the transmission impact of interventions serving PEH.

Design, methods and analysis

Ku-gaa-gii pimitizi-win will follow a random sample of PEH from across Toronto (Canada) for 12 months. 736 participants were enrolled between June and September 2021, and will be followed up at 3-month intervals. At each interval, specimens (saliva, capillary blood) will be collected to determine active SARS-CoV-2 infection and serologic evidence of past infection and/or vaccination, and a detailed survey will gather self-reported information, including a detailed housing history. To examine the association between individual-level and shelter-level characteristics on COVID-19-related infection, adverse outcomes, and vaccination, shelter and healthcare administrative data will be linked to participant study data. Healthcare administrative data will also be used to examine long-term (up to 5 years) COVID-19-related outcomes among participants.

Ethics and dissemination

Ethical approval was obtained from the Unity Health Toronto and University of Toronto Health Sciences Research Ethics Boards (# 20-272). Ku-gaa-gii pimitizi-win was designed in collaboration with community and service provider partners and people having lived experience of homelessness. Findings will be reported to groups supporting Ku-gaa-gii pimitizi-win, Indigenous and other community partners and service providers, funding bodies, public health agencies and all levels of government to inform policy and public health programs.

Data-Related Challenges in Cost-Effectiveness Analyses of Vaccines

Cost-effectiveness analyses (CEAs) are often prepared to quantify the expected economic value of potential vaccination strategies. Estimated outcomes and costs of vaccination strategies depend on numerous data inputs or assumptions, including estimates of vaccine efficacy and disease incidence in the absence of vaccination. Limitations in epidemiologic data can meaningfully affect both CEA estimates and the interpretation of those results by groups involved in vaccination policy decisions. Developers of CEAs should be transparent with regard to the ambiguity and uncertainty associated with epidemiologic information that is incorporated into their models. We describe selected data-related challenges to conducting CEAs for vaccination strategies, including generalizability of estimates of vaccine effectiveness, duration and functional form of vaccine protection that can change over time, indirect (herd) protection, and serotype replacement. We illustrate how CEA estimates can be sensitive to variations in specific epidemiologic assumptions, with examples from CEAs conducted for the USA that assessed vaccinations against human papillomavirus and pneumococcal disease. These challenges are certainly not limited to these two case studies and may be relevant to other vaccines.

Finding and treating early-stage HIV infections: A cost-effectiveness analysis of the Sabes study in Lima, Peru

Background

Sabes, a treatment-as-prevention intervention among men who have sex with men and transgender women in Lima, Peru, was developed to identify HIV during early primary infection (<3 months from acquisition) through monthly serologic assays and HIV RNA tests. Newly diagnosed individuals were rapidly linked to care and offered to initiate ART. In this study we sought to study the cost-effectiveness of Sabes compared to the standard of care (SOC) for HIV testing and initiation of treatment.

Methods

We adapted a compartmental model of HIV transmission to evaluate the cost-effectiveness of the Sabes approach compared to the SOC using a government health care perspective, 20-year time horizon, and 3% annual discounting. We estimated the proportion of cases of HIV detected during early primary infection, reduction in HIV incidence and prevalence, incremental cost-effectiveness ratio (ICER), and net monetary benefit. We analyzed costs using data from the Sabes study, the Peruvian Ministry of Health, published literature, and expert consultation.

Findings

The Sabes intervention is projected to identify 9294 early primary HIV infections in Lima, Peru over 20 years. The intervention costs $6,896 per early primary infection diagnosed and by 2038 is expected to decrease the fraction of early infections among prevalent infections by 62%. Sabes is expected to improve health, resulting in greater total discounted QALYs per person than the SOC (16·7 vs 16·4, respectively). Sabes had an ICER of $1431 (22% per capita GDP in Peru) per QALY compared to SOC.

Interpretation

Our analysis suggests that in Lima, Peru the Sabes intervention could be a cost-effective approach to reduce the burden of HIV even under stringent cost-effectiveness criteria. This finding suggests that programs that use frequent HIV testing, rapid linkage to care and initiation of ART should be considered as part of a comprehensive HIV prevention strategy.

Funding

National Institutes of Health.

M2HepPrEP: study protocol for a multi-site multi-setting randomized controlled trial of integrated HIV prevention and HCV care for PWID

Background

Opioid use is escalating in North America and comes with a multitude of health consequences, including HIV and hepatitis C virus (HCV) outbreaks among persons who inject drugs (PWID). HIV pre-exposure prophylaxis (PrEP) and HCV treatment regimens have transformative potential to address these co-occurring epidemics. Evaluation of innovative multi-modal approaches, integrating harm reduction, opioid agonist therapy (OAT), PrEP, and HCV treatment is required. The aim of this study is to assess the effectiveness of an on-site integrated care model where delivery of PrEP and HCV treatment for PWID takes places at syringe service programs (SSP) and OAT programs compared with referring PWID to clinical services in the community through a patient navigation model and to examine how structural factors interact with HIV prevention adherence and HCV treatment outcomes.

Methods

The Miami-Montreal Hepatitis C and Pre-Exposure Prophylaxis trial (M²HepPrEP) is an open-label, multi-site, multi-center, randomized, controlled, superiority trial with two parallel treatment arms. A total of 500 persons who injected drugs in the prior 6 months and are eligible for PrEP will be recruited in OAT clinics and SSP in Miami, FL, and Montréal, Québec. Participants will be randomized to either on-site care, with adherence counseling, or referral to off-site clinics assisted by a patient navigator. PrEP will be offered to all participants and HCV treatment to those HCV-infected. Co-primary endpoints will be (1) adherence to pre-exposure prophylaxis medication at 6 months post-randomization and (2) HCV sustained virological response (SVR) 12 weeks post-treatment completion among participants who were randomized within the HCV stratum. Up to 100 participants will be invited to participate in a semi-structured interview regarding perceptions of adherence barriers and facilitators, after their 6-month assessment. A simulation model-based cost-effectiveness analysis will be performed to determine the comparative value of the strategies being evaluated.

Discussion

The results of this study have the potential to demonstrate the effectiveness and cost-effectiveness of offering PrEP and HCV treatment in healthcare venues frequently attended by PWID. Testing the intervention in two urban centers with high disease burden among PWID, but with different healthcare system dynamics, will increase generalizability of findings.

Trial registration

Clinicaltrials.gov NCT03981445. Trial registry name: Integrated HIV Prevention and HCV Care for PWID (M2HepPrEP). Registration date: June 10, 201.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06085-3.

Evaluating the cost-effectiveness of a pre-exposure prophylaxis program for HIV prevention for men who have sex with men in Japan

Men who have sex with men (MSM) have been disproportionally affected by the HIV epidemic in many countries, including Japan. Although pre-exposure prophylaxis (PrEP) is a strong prevention tool, it is not yet approved in Japan. A Markov model was developed to describe HIV infection and disease progression in an MSM cohort (N = 1000) in Japan receiving a PrEP program. The model was used to evaluate the cost-effectiveness of a PrEP program. HIV/AIDS treatment, screening, hospitalization due to AIDS, and PrEP were considered as costs and quality-adjusted life-years (QALYs) gained as utilities. Cost-effectiveness was assessed by comparing the incremental cost-effectiveness ratio (ICER) over a 30-year period against the willingness to pay (WTP) threshold. One-way sensitivity and probabilistic sensitivity analyses were performed. With 50% PrEP coverage, the PrEP program became dominant against the program without PrEP, using a threshold of 5.0 million JPY/QALY (45,455 USD). The probabilistic sensitivity analysis revealed that the PrEP program was dominant or at least cost-effective in most cases of 10,000 simulations. Therefore, preparing cheaper PrEP pills, which results in PrEP being dominant or ICER being lower than the WTP threshold, is important to make the program cost-effective. Introduction of PrEP to an MSM cohort in Japan would be cost-effective over a 30-year time horizon.

A review of epidemiological models of Clostridioides difficile transmission and control (2009-2021)

Clostridioides difficile is the leading cause of infectious diarrhea and one of the most common healthcare-acquired infections worldwide. We performed a systematic search and a bibliometric analysis of mathematical and computational models for Clostridioides difficile transmission. We identified 33 publications from 2009 to 2021. Models have underscored the importance of asymptomatic colonized patients in maintaining transmission in health-care settings. Infection control, antimicrobial stewardship, active testing, and vaccination have often been evaluated in models. Despite active testing and vaccination being not currently implemented, they are the most commonly evaluated interventions. Some aspects of C. difficile transmission, such community transmission and interventions in health-care settings other than in acute-care hospitals, remained less evaluated through modeling.

Preventing COVID-19 outbreaks through surveillance testing in healthcare facilities: a modelling study

BACKGROUND

Surveillance testing within healthcare facilities provides an opportunity to prevent severe outbreaks of coronavirus disease 2019 (COVID-19). However, the quantitative impact of different available surveillance strategies and their potential to decrease the frequency of outbreaks are not well-understood.

METHODS

We establish an individual-based model representative of a mental health hospital yielding generalizable results. Attributes and features of this facility were derived from a prototypical hospital, which provides psychiatric, psychosomatic and psychotherapeutic treatment. We estimate the relative reduction of outbreak probability for three test strategies (entry test, once-weekly test and twice-weekly test) relative to a symptom-based baseline strategy. Based on our findings, we propose determinants of successful surveillance measures.

RESULTS

Entry Testing reduced the outbreak probability by 26%, additionally testing once or twice weekly reduced the outbreak probability by 49% or 67% respectively. We found that fast diagnostic test results and adequate compliance of the clinic population are mandatory for conducting effective surveillance. The robustness of these results towards uncertainties is demonstrated via comprehensive sensitivity analyses.

CONCLUSIONS

We conclude that active testing in mental health hospitals and similar facilities considerably reduces the number of COVID-19 outbreaks compared to symptom-based surveillance only.

Sound Decision Making in Uncertain Times: Can Systems Modelling Be Useful for Informing Policy and Planning for Suicide Prevention?

The COVID-19 pandemic demonstrated the significant value of systems modelling in supporting proactive and effective public health decision making despite the complexities and uncertainties that characterise an evolving crisis. The same approach is possible in the field of mental health. However, a commonly levelled (but misguided) criticism prevents systems modelling from being more routinely adopted, namely, that the presence of uncertainty around key model input parameters renders a model useless. This study explored whether radically different simulated trajectories of suicide would result in different advice to decision makers regarding the optimal strategy to mitigate the impacts of the pandemic on mental health. Using an existing system dynamics model developed in August 2020 for a regional catchment of Western Australia, four scenarios were simulated to model the possible effect of the COVID-19 pandemic on levels of psychological distress. The scenarios produced a range of projected impacts on suicide deaths, ranging from a relatively small to a dramatic increase. Discordance in the sets of best-performing intervention scenarios across the divergent COVID-mental health trajectories was assessed by comparing differences in projected numbers of suicides between the baseline scenario and each of 286 possible intervention scenarios calculated for two time horizons; 2026 and 2041. The best performing intervention combinations over the period 2021–2041 (i.e., post-suicide attempt assertive aftercare, community support programs to increase community connectedness, and technology enabled care coordination) were highly consistent across all four COVID-19 mental health trajectories, reducing suicide deaths by between 23.9–24.6% against the baseline. However, the ranking of best performing intervention combinations does alter depending on the time horizon under consideration due to non-linear intervention impacts. These findings suggest that systems models can retain value in informing robust decision making despite uncertainty in the trajectories of population mental health outcomes. It is recommended that the time horizon under consideration be sufficiently long to capture the full effects of interventions, and efforts should be made to achieve more timely tracking and access to key population mental health indicators to inform model refinements over time and reduce uncertainty in mental health policy and planning decisions.

Development and application of a dynamic transmission model of health systems' preparedness and response to COVID-19 in twenty-six Latin American and Caribbean countries

The global impact of COVID-19 has challenged health systems across the world. This situation highlighted the need to develop policies based on scientific evidence to prepare the health systems and mitigate the pandemic. In this scenario, governments were urged to predict the impact of the measures they were implementing, how they related to the population's behavior, and the capacity of health systems to respond to the pandemic. The overarching aim of this research was to develop a customizable and open-source tool to predict the impact of the expansion of COVID-19 on the level of preparedness of the health systems of different Latin American and the Caribbean countries, with two main objectives. Firstly, to estimate the transmission dynamics of COVID-19 and the preparedness and response capacity of health systems in those countries, based on different scenarios and public policies implemented to control, mitigate, or suppress the spread of the epidemic. Secondly, to facilitate policy makers' decisions by allowing the model to adjust its parameters according to the specific pandemic trajectory and policy context. How many infections and deaths are estimated per day?; When are the peaks of cases and deaths expected, according to the different scenarios?; Which occupancy rate will ICU services have along the epidemiological curve?; When is the optimal time increase restrictions in order to prevent saturation of ICU beds?, are some of the key questions that the model can respond, and is publicly accessible through the following link: http://shinyapps.iecs.org.ar/modelo-covid19/. This open-access and open code tool is based on a SEIR model (Susceptible, Exposed, Infected and Recovered). Using a deterministic epidemiological model, it allows to frame potential scenarios for long periods, providing valuable information on the dynamics of transmission and how it could impact on health systems through multiple customized configurations adapted to specific characteristics of each country.

A Dynamic Approach to Economic Priority Setting to Invest in Youth Mental Health and Guide Local Implementation: Economic Protocol for Eight System Dynamics Policy Models

BACKGROUND

Mental illness costs the world economy over US2.5 Bn each year, including premature mortality, morbidity, and productivity losses. Multisector approaches are required to address the systemic drivers of mental health and ensure adequate service provision. There is an important role for economics to support priority setting, identify best value investments and inform optimal implementation. Mental health can be defined as a complex dynamic system where decision makers are challenged to prospectively manage the system over time. This protocol describes the approach to equip eight system dynamics (SD) models across Australia to support priority setting and guide portfolio investment decisions, tailored to local implementation context.

METHODS

As part of a multidisciplinary team, three interlinked protocols are developed; (i) the participatory process to codesign the models with local stakeholders and identify interventions for implementation, (ii) the technical protocol to develop the SD models to simulate the dynamics of the local population, drivers of mental health, the service system and clinical outcomes, and (iii) the economic protocol to detail how the SD models will be equipped to undertake a suite of economic analysis, incorporating health and societal perspectives. Models will estimate the cost of mental illness, inclusive of service costs (health and other sectors, where necessary), quality-adjusted life years (QALYs) lost, productivity costs and carer costs. To assess the value of investing (disinvesting) in interventions, economic analysis will include return-on-investment, cost-utility, cost benefit, and budget impact to inform affordability. Economic metrics are expected to be dynamic, conditional upon changing population demographics, service system capacities and the mix of interventions when synergetic or antagonistic interactions. To support priority setting, a portfolio approach will identify best value combinations of interventions, relative to a defined budget(s). User friendly dashboards will guide decision makers to use the SD models to inform resource allocation and generate business cases for funding.

DISCUSSION

Equipping SD models to undertake economic analysis is intended to support local priority setting and help optimise implementation regarding the best value mix of investments, timing and scale. The objectives are to improve allocative efficiency, increase mental health and economic productivity.

Inclusion of Additional Unintended Consequences in Economic Evaluation: A Systematic Review of Immunization and Tuberculosis Cost-Effectiveness Analyses

OBJECTIVE

Our objective was to review economic evaluations of immunization and tuberculosis to determine the extent to which additional unintended consequences were taken into account in the analysis and to describe the methodological approaches used to estimate these, where possible.

METHODS

We sourced the vaccine economic evaluations from a previous systematic review by Nymark et al. (2009-2015) and searched PubMed/MEDLINE and Embase from 2015 to 2019 using the same search strategy. For tuberculosis economic evaluations, we extracted studies from 2009 to 2019 that were published in a previous review by Siapka et al. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance. Studies were classified according to the categories and subcategories (e.g., herd immunity, non-specific effects, and labor productivity) defined in a framework identifying additional unintended consequences by Nymark and Vassall. Where possible, methods for estimating the additional unintended consequences categories and subcategories were described. We evaluated the reporting quality of included studies according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) extraction guideline.

RESULTS

We identified 177 vaccine cost-effectiveness analyses (CEAs) between 2009 and 2019 that met the inclusion criteria. Of these, 98 included unintended consequences. Of the total 98 CEAs, overall health consequence categories were included 73 times; biological categories: herd immunity 43 times; pathogen response: resistance 15 times; and cross-protection 15 times. For health consequences pertaining to the supply-side (health systems) categories, side effects were included five times. On the nonhealth demand side (intrahousehold), labor productivity was included 60 times. We identified 29 tuberculosis CEAs from 2009 to 2019 that met the inclusion criteria. Of these, six articles included labor productivity, four included indirect transmission effects, and one included resistance. Between 2009 and 2019, only 34% of tuberculosis CEAs included additional unintended consequences, compared with 55% of vaccine CEAs.

CONCLUSIONS

The inclusion of additional unintended consequences in economic evaluations of immunization and tuberculosis continues to be limited. Additional unintended consequences of economic benefits, such as those examined in this review and especially those that occur outside the health system, offer valuable information to analysts. Further work on appropriate ways to value these additional unintended consequences is still warranted.

Economic evaluation using dynamic transition modeling of ebola virus vaccination in lower-and-middle-income countries

BACKGROUND

With the increasing occurrence of infectious diseases in lower-and-middle-income countries (LMICs), emergency preparedness is essential for rapid response and mitigation. Economic evaluations of mitigation technologies and strategies have been recommended for inclusion in emergency preparedness plans. We aimed to perform an economic evaluation using dynamic transition modeling of ebola virus disease (EVD) vaccination in a hypothetical community of 1,000 persons in the Democratic Republic of Congo (DRC).

METHOD

Using a modified SEIR (Susceptible, Exposed, Infectious, Recovered, with Death added [SEIR-D]) model that accounted for death and epidemiological data from an EVD outbreak in the DRC, we modeled the transmission of EVD in a hypothetical population of 1,000. With our model, we estimated the cost-effectiveness of an EVD vaccine and an EVD vaccination intervention.

RESULTS

The results showed vaccinating 50% of the population at risk prevented 670 cases, 538 deaths, and 22,022 disability-adjusted life years (DALYs). The vaccine was found to be cost-effective with an incremental cost-effectiveness ratio (ICER) of $95.63 per DALY averted. We also determined the minimum required vaccination coverage for cost-effectiveness to be 40%. Sensitivity analysis showed our model to be fairly robust, assuring relatively consistent results even with variations in such input parameters as cost of screening, as well as transmission, infection, incubation, and case fatality rates.

CONCLUSION

EVD vaccination in our hypothetical population was found to be cost-effective from the payer perspective. Our model presents an efficient and reliable approach for conducting economic evaluations of infectious disease interventions as part of an emergency preparedness plan.

How to Prevent Vaccines Falling Victim to Their Own Success: Intertemporal Dependency of Incidence Levels on Indirect Effects in Economic Reevaluations

OBJECTIVES

Incremental cost-effectiveness analyses may inform the optimal choice of healthcare interventions. Nevertheless, for many vaccines, benefits fluctuate with incidence levels over time. Reevaluating a vaccine after it has successfully decreased incidences may eventually cause a disease resurgence if switching to a vaccine with lower indirect benefits. Decisions may successively alternate between vaccines alongside repeated rises and falls in incidence and when indirect effects from historic use are ignored. Our suggested proposal aims to prevent suboptimal decision making.

METHODS

We used a conceptual model of demand to illustrate alternating decisions between vaccines because of time-varying levels of indirect effects. Similar to the concept of subsidies, we propose internalizing the indirect effects achievable with vaccines. In a case study over 60 years, we simulated a hypothetical 10-year reevaluation of 2 oncogenic human papillomavirus vaccines, of which only 1 protects additionally against anogenital warts.

RESULTS

Our case study showed that the vaccine with additional warts protection is initially valued higher than the vaccine without additional warts protection. After 10 years, this differential decreases because of declines in warts incidence, which supports switching to the nonwarts vaccine that causes a warts resurgence eventually. Instead, pricing the indirect effects separately supports continuing with the warts vaccine.

CONCLUSIONS

Ignoring how the observed incidences depend on the indirect effects achieved with a particular vaccine may lead to repeated changes in vaccines at successive reevaluations, with unintended resurgences, economic inefficiencies, and eroding vaccine confidence. We propose internalizing indirect effects to prevent vaccines falling victim to their own success.

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

BACKGROUND

The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research.

METHODS AND FINDINGS

We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies.

CONCLUSIONS

These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

Protocol for the economic evaluation of COVID-19 pandemic response policies

INTRODUCTION

Several treatment options are available for COVID-19 to date. However, the use of a combination of non-pharmaceutical interventions (NPIs) is necessary for jurisdictions to contain its spread. Although the implementation cost of NPIs may be low from the healthcare system perspective, it can be costly when considering the indirect costs from the societal perspective. COVID-19 vaccination campaigns have begun in several countries worldwide. Nonetheless, the quantity of vaccines available remain limited over the next 1 to 2 years. A tool for informing vaccine prioritisation that considers both cost and effectiveness will be highly useful. This study aims to identify the most cost-effective combination of COVID-19 response policies, using Singapore as an example.

METHODS AND ANALYSIS

An age-stratified Susceptible-Exposed-Infectious-Recovered model will be used to generate the number of infections stratified by disease severity under different intervention scenarios. Polices of interest include test-trace-isolate, travel restriction, compulsory face mask and hygiene practices, social distancing, dexamethasone/remdesivir therapy and vaccination. The latest phase 3 trial results and the WHO Target Product Profiles for COVID-19 vaccines will be used to model vaccine characteristics. A cost (expected resource utilisation and productivity losses) and quality-adjusted life years (QALYs) will be attached to these outputs for a cost-utility analysis. The primary outcome measure will be the incremental cost-effectiveness ratio generated from the incremental cost of policy alternatives expressed as a ratio of the incremental benefits (QALYs gained). Efficacy of policy options will be gathered from literature review and from its observed impacts in Singapore. Cost data will be gathered from healthcare institutions, Ministry of Health and published data. Sensitivity analysis such as threshold analysis and scenario analysis will be conducted.

ETHICS AND DISSEMINATION

Ethics approval was not required for this study. The study findings will be disseminated through peer-reviewed journals.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

Advancing sustainable development goals through immunization: a literature review

BACKGROUND

Immunization directly impacts health (SDG3) and brings a contribution to 14 out of the 17 Sustainable Development Goals (SDGs), such as ending poverty, reducing hunger, and reducing inequalities. Therefore, immunization is recognized to play a central role in reaching the SDGs, especially in low- and middle-income countries (LMICs). Despite continuous interventions to strengthen immunization systems and to adequately respond to emergency immunization during epidemics, the immunization-related indicators for SDG3 lag behind in sub-Saharan Africa. Especially taking into account the current Covid19 pandemic, the current performance on the connected SDGs is both a cause and a result of this.

METHODS

We conduct a literature review through a keyword search strategy complemented with handpicking and snowballing from earlier reviews. After title and abstract screening, we conducted a qualitative analysis of key insights and categorized them according to showing the impact of immunization on SDGs, sustainability challenges, and model-based solutions to these challenges.

RESULTS

We reveal the leveraging mechanisms triggered by immunization and position them vis-à-vis the SDGs, within the framework of Public Health and Planetary Health. Several challenges for sustainable control of vaccine-preventable diseases are identified: access to immunization services, global vaccine availability to LMICs, context-dependent vaccine effectiveness, safe and affordable vaccines, local/regional vaccine production, public-private partnerships, and immunization capacity/capability building. Model-based approaches that support SDG-promoting interventions concerning immunization systems are analyzed in light of the strategic priorities of the Immunization Agenda 2030.

CONCLUSIONS

In general terms, it can be concluded that relevant future research requires (i) design for system resilience, (ii) transdisciplinary modeling, (iii) connecting interventions in immunization with SDG outcomes, (iv) designing interventions and their implementation simultaneously, (v) offering tailored solutions, and (vi) model coordination and integration of services and partnerships. The research and health community is called upon to join forces to activate existing knowledge, generate new insights and develop decision-supporting tools for Low-and Middle-Income Countries' health authorities and communities to leverage immunization in its transformational role toward successfully meeting the SDGs in 2030.

Users' Guide to Medical Decision Analysis

Clinicians regularly have to trade benefits and harms to choose between testing and treatment strategies. This process is often done by making global and implicit judgments. A decision analysis is an analytic method that makes this process more explicit, reproducible, and evidence-based. While clinicians are unlikely to conduct their own decision analysis, they will read publications of such analyses or use guidelines based on them. This review outlines the anatomy of a decision tree and provides clinicians with the tools to critically appraise a decision analysis and apply its results to medical decision making. Clinicians reading about a decision analysis can make two judgments. The first judgment is about the credibility of the methods, such as whether the decision analysis addressed a relevant clinical question, included all important outcomes, used the current best evidence to derive variables in the model, and adopted the appropriate time horizon. The second judgment is about rating confidence in the preferred course of action by determining the certainty in the model variables, whether the results are robust in sensitivity analyses and if the results are applicable to a specific patient. Results from a valid and robust decision analysis can inform both guideline panels and the patient-clinician dyad engaged in shared decision-making.

Patient Flows, Patient Distribution Computations and Medicines Accounting in the Pharmacoeconomic Models Through Procurement Perspective

Purpose

Stimulating cost reduction of pharmaceutical companies to optimize the structure of distribution of patients by the level of treatment costs in various programs.

Patients and Methods

In this article, we rise up the issues of pharmacoeconomic modeling related to the description of the patient flows in the pharmacoeconomic model and methods to determining the course dose of drugs under the restriction of integer computations. We established two possible ways of distributing patients through treatment regimens in pharmacoeconomic models, also analyzed the effects of simultaneous and uniform entry of patients into the model. Also, we considered the limitations and possibilities of calculations based on the active substance and packaging, as well as the transition factor of the remainder of the drug in the next time period.

Results

A mathematical model of the analysis of the system assessment of patients by the level of risk of abandoning a healthy lifestyle in connection with the growing problems of the difficult-to-control process is developed. The use of a rational data convolution mode allowed us to obtain a criterion for the optimality of the process and a logical point of stability of the pharmaceutical company by rationally applying treatment methods according to established standards (percentage base). This approach makes it possible to influence the management of private clinics through clear ideas on the algorithms for prescribing drugs in each group of patients and their zoning in the vector recovery mode.

Conclusion

Initial data and sample size: 552 measurements of the intervals of changes in the subject's indicators in seconds (smoothing and scaling the data to the level of the base (analytical) period or the final (barrier) period). Regular use of this approach makes it possible to reserve the resources of the body of a healthy and physically active person in a timely manner for a very reliable functioning of all body systems, taking into account the dosed intake of prescribed drugs and the conditions of comfortable (decent) maintenance of patients during the course of treatment according to the method chosen by the doctor.

Where Do We Go From Here? A Framework for Using Susceptible-Infectious-Recovered Models for Policy Making in Emerging Infectious Diseases

OBJECTIVES

Throughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making.

METHODS

We identify and describe 5 broad standards: transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards.

RESULTS

We graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent.

CONCLUSIONS

There is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.

Consultations for Influenza-Like Illness in Primary Care in The Netherlands: A Regression Approach

OBJECTIVES

To estimate the general practitioner (GP) consultation rate attributable to influenza in The Netherlands.

METHODS

Regression analysis was performed on the weekly numbers of influenza-like illness (ILI) GP consultations and laboratory reports for influenza virus types A and B and 8 other pathogens over the period 2003-2014 (11 influenza seasons; week 40-20 of the following year).

RESULTS

In an average influenza season, 27% and 11% of ILI GP consultations were attributed to infection by influenza virus types A and B, respectively. Influenza is therefore responsible for approximately 107 000 GP consultations (651/100 000) each year in The Netherlands. GP consultation rates associated with influenza infection were highest in children under 5 years of age, at 667 of 100 000 for influenza A and 258 of 100 000 for influenza B. Influenza virus infection was found to be the predominant cause of ILI-related GP visits in all age groups except children under 5, in which respiratory syncytial virus (RSV) infection was found to be the main contributor.

CONCLUSIONS

The burden of influenza in terms of GP consultations is considerable. Overall, influenza is the main contributor to ILI. Although ILI symptoms in children under 5 years of age are most often associated with RSV infection, the majority of visits related to influenza occur among children under 5 years of age.

Cost-Effectiveness of Pediatric Influenza Vaccination in The Netherlands

OBJECTIVE

This study evaluates the cost-effectiveness of extending the Dutch influenza vaccination program for elderly and medical high-risk groups to include pediatric influenza vaccination, taking indirect protection into account.

METHODS

An age-structured dynamic transmission model was used that was calibrated to influenza-associated GP visits over 4 seasons (2010-2011 to 2013-2014). The clinical and economic impact of different pediatric vaccination strategies were compared over 20 years, varying the targeted age range, the vaccine type for children or elderly and high-risk groups. Outcome measures include averted symptomatic infections and deaths, societal costs and quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Costs and QALYs were discounted at 4% and 1.5% annually.

RESULTS

At an assumed coverage of 50%, adding pediatric vaccination for 2- to 17-year-olds with quadrivalent live-attenuated vaccine to the current vaccination program for elderly and medical high-groups with quadrivalent inactivated vaccine was estimated to avert, on average, 401 820 symptomatic cases and 72 deaths per year. Approximately half of averted symptomatic cases and 99% of averted deaths were prevented in other age groups than 2- to 17-year-olds due to herd immunity. The cumulative discounted 20-year economic impact was 35 068 QALYs gained and €1687 million saved, that is, the intervention was cost-saving. This vaccination strategy had the highest probability of being the most cost-effective strategy considered, dominating pediatric strategies targeting 2- to 6-year-olds or 2- to 12-year-olds or strategies with trivalent inactivated vaccine.

CONCLUSION

Modeling indicates that introducing pediatric influenza vaccination in The Netherlands is cost-saving, reducing the influenza-related disease burden substantially.

Costs and benefits of interventions aimed at major infectious disease threats: lessons from the literature

Pandemics and major outbreaks have the potential to cause large health losses and major economic costs. To prioritize between preventive and responsive interventions, it is important to understand the costs and health losses interventions may prevent. We review the literature, investigating the type of studies performed, the costs and benefits included, and the methods employed against perceived major outbreak threats. We searched PubMed and SCOPUS for studies concerning the outbreaks of SARS in 2003, H5N1 in 2003, H1N1 in 2009, Cholera in Haiti in 2010, MERS-CoV in 2013, H7N9 in 2013, and Ebola in West-Africa in 2014. We screened titles and abstracts of papers, and subsequently examined remaining full-text papers. Data were extracted according to a pre-constructed protocol. We included 34 studies of which the majority evaluated interventions related to the H1N1 outbreak in a high-income setting. Most interventions concerned pharmaceuticals. Included costs and benefits, as well as the methods applied, varied substantially between studies. Most studies used a short time horizon and did not include future costs and benefits. We found substantial variation in the included elements and methods used. Policymakers need to be aware of this and the bias toward high-income countries and pharmaceutical interventions, which hampers generalizability. More standardization of included elements, methodology, and reporting would improve economic evaluations and their usefulness for policy.

Benefit-Risk Assessment of Vaccines. Part II: Proposal Towards Consolidated Standards of Reporting Quantitative Benefit-Risk Models Applied to Vaccines (BRIVAC)

INTRODUCTION

Quantitative benefit-risk models (qBRm) applied to vaccines are increasingly used by public health authorities and pharmaceutical companies as an important tool to help decision makers with supporting benefit-risk assessment (BRA). However, many publications on vaccine qBRm provide insufficient details on the methodological approaches used. Incomplete and/or inadequate qBRm reporting may affect result interpretation and confidence in BRA, highlighting a need for the development of standard reporting guidance.

OBJECTIVES

Our objective was to provide an operational checklist for improved reporting of vaccine qBRm.

METHODS

The consolidated standards of reporting quantitative Benefit-RIsk models applied to VACcines (BRIVAC) were designed as a checklist of key information to report in qBRm scientific publications regarding the assessed vaccines, the methodological considerations and the results and their interpretation.

RESULTS

In total, 22 items and accompanying definitions, recommendations, explanations and examples were provided and divided into six main sections corresponding to the classic subdivisions of a scientific publication: title and abstract (items 1-2), introduction (items 3-4), methods (items 5-15), results (items 16-17), discussion (items 18-20) and other (items 21-22).

CONCLUSIONS

The BRIVAC checklist is the first initiative providing an operational checklist for improved reporting of qBRm applied to vaccines in scientific articles. It is intended to assist authors, peer-reviewers, editors and readers in their critical appraisal. Future initiatives are needed to provide methodological guidance to perform qBRm while taking into account the vaccine specificities.

Evidence-Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions

OBJECTIVE

To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices.

DATA SOURCES

Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions.

REVIEW METHODS

Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations.

CONCLUSIONS

Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care.

IMPLICATIONS FOR PRACTICE

Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

Seasonal influenza vaccination in Kenya: an economic evaluation using dynamic transmission modelling

BACKGROUND

There is substantial burden of seasonal influenza in Kenya, which led the government to consider introducing a national influenza vaccination programme. Given the cost implications of a nationwide programme, local economic evaluation data are needed to inform policy on the design and benefits of influenza vaccination. We set out to estimate the cost-effectiveness of seasonal influenza vaccination in Kenya.

METHODS

We fitted an age-stratified dynamic transmission model to active surveillance data from patients with influenza from 2010 to 2018. Using a societal perspective, we developed a decision tree cost-effectiveness model and estimated the incremental cost-effectiveness ratio (ICER) per disability-adjusted life year (DALY) averted for three vaccine target groups: children 6-23 months (strategy I), 2-5 years (strategy II) and 6-14 years (strategy III) with either the Southern Hemisphere influenza vaccine (Strategy A) or Northern Hemisphere vaccine (Strategy B) or both (Strategy C: twice yearly vaccination campaigns, or Strategy D: year-round vaccination campaigns). We assessed cost-effectiveness by calculating incremental net monetary benefits (INMB) using a willingness-to-pay (WTP) threshold of 1-51% of the annual gross domestic product per capita ($17-$872).

RESULTS

The mean number of infections across all ages was 2-15 million per year. When vaccination was well timed to influenza activity, the annual mean ICER per DALY averted for vaccinating children 6-23 months ranged between $749 and $1385 for strategy IA, $442 and $1877 for strategy IB, $678 and $4106 for strategy IC and $1147 and $7933 for strategy ID. For children 2-5 years, it ranged between $945 and $1573 for strategy IIA, $563 and $1869 for strategy IIB, $662 and $4085 for strategy IIC, and $1169 and $7897 for strategy IID. For children 6-14 years, it ranged between $923 and $3116 for strategy IIIA, $1005 and $2223 for strategy IIIB, $883 and $4727 for strategy IIIC and $1467 and $6813 for strategy IIID. Overall, no vaccination strategy was cost-effective at the minimum ($17) and median ($445) WTP thresholds. Vaccinating children 6-23 months once a year had the highest mean INMB value at $872 (WTP threshold upper limit); however, this strategy had very low probability of the highest net benefit.

CONCLUSION

Vaccinating children 6-23 months once a year was the most favourable vaccination option; however, the strategy is unlikely to be cost-effective given the current WTP thresholds.

Digital contact tracing technologies in epidemics: a rapid review

BACKGROUND

Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in contact with an infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks.

OBJECTIVES

To assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease.

SEARCH METHODS

An information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID-19 Study Register.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), cluster-RCTs, quasi-RCTs, cohort studies, cross-sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID-19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence. The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self-reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records).

DATA COLLECTION AND ANALYSIS

Two review authors independently screened records and all potentially relevant full-text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE-CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies.

MAIN RESULTS

We identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non-outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID-19 scenarios, while two simulated close contacts in non-specific outbreak settings. Modelling studies Two modelling studies provided low-certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case - effective reproductive number (R _eff)). One study estimated an 18% reduction in R _eff with digital contact tracing compared to self-isolation alone, and a 35% reduction with manual contact-tracing. Another found a reduction in R _eff for digital contact tracing compared to self-isolation alone (26% reduction) and a reduction in R _eff for manual contact tracing compared to self-isolation alone (53% reduction). However, the certainty of evidence was reduced by unclear specifications of their models, and assumptions about the effectiveness of manual contact tracing (assumed 95% to 100% of contacts traced), and the proportion of the population who would have the app (53%). Cohort studies Two cohort studies provided very low-certainty evidence of a benefit of digital over manual contact tracing. During an Ebola outbreak, contact tracers using an app found twice as many close contacts per case on average than those using paper forms. Similarly, after a pertussis outbreak in a US hospital, researchers found that radio-frequency identification identified 45 close contacts but searches of electronic medical records found 13. The certainty of evidence was reduced by concerns about imprecision, and serious risk of bias due to the inability of contact tracing study designs to identify the true number of close contacts. One cohort study provided very low-certainty evidence that an app could reduce the time to complete a set of close contacts. The certainty of evidence for this outcome was affected by imprecision and serious risk of bias. Contact tracing teams reported that digital data entry and management systems were faster to use than paper systems and possibly less prone to data loss. Two studies from lower- or middle-income countries, reported that contact tracing teams found digital systems simpler to use and generally preferred them over paper systems; they saved personnel time, reportedly improved accuracy with large data sets, and were easier to transport compared with paper forms. However, personnel faced increased costs and internet access problems with digital compared to paper systems. Devices in the cohort studies appeared to have privacy from contacts regarding the exposed or diagnosed users. However, there were risks of privacy breaches from snoopers if linkage attacks occurred, particularly for wearable devices.

AUTHORS' CONCLUSIONS

The effectiveness of digital solutions is largely unproven as there are very few published data in real-world outbreak settings. Modelling studies provide low-certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self-isolation. Cohort studies provide very low-certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at-risk populations with poor internet access and poor access to digital technology. Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real-world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies.

A comprehensive framework for considering additional unintended consequences in economic evaluation

Background

In recent years there has been a growth in economic evaluations that consider indirect health benefits to populations due to advances in mathematical modeling. In addition, economic evaluations guidelines have suggested the inclusion of impact inventories to include non-health direct and indirect consequences. We aim to bring together this literature, together with the broader literature on internalities and externalities to propose a comprehensive approach for analysts to identify and characterize all unintended consequences in economic evaluations.

Methods

We present a framework to assist analysts identify and characterize additional costs and effects beyond that of direct health impact primarily intended to be influenced by the intervention/technology. We build on previous checklists to provide analysts with a comprehensive framework to justify the inclusion or exclusion of effects, supporting the use of current guidelines, to ensure any unintended effects are considered. We illustrate this framework with examples from immunization. These were identified from a previous systematic review, PhD thesis work, and general search scoping in PubMed databases.

Results

We present a comprehensive framework to consider additional consequences, exemplified by types and categories. We bring this and other guidance together to assist analysts identify possible unintended consequences whether taking a provider or societal perspective.

Conclusions

Although there are many challenges ahead to standardize the inclusion of additional consequences in economic evaluation, we hope by moving beyond generic statements to reporting against a comprehensive framework of additional effects we can support further consistency in this aspect of cost-effectiveness analysis going forward.

Estimating the Value of New Antimicrobials in the Context of Antimicrobial Resistance: Development and Application of a Dynamic Disease Transmission Model

OBJECTIVES

Antimicrobial resistance (AMR) represents a significant threat to patient and population health. The study aim was to develop and validate a model of AMR that defines and quantifies the value of new antibiotics.

METHODS

A dynamic disease transmission and cost-effectiveness model of AMR consisting of three components (disease transmission, treatment pathway and optimisation) was developed to evaluate the health economic value of new antibiotics. The model is based on the relationship between AMR, antimicrobial availability and consumption. Model analysis explored the impact of different antibiotic treatment strategies on the development of AMR, patient and population estimates of health benefit, across three common treatment indications and pathogens in the UK.

RESULTS

Population-level resistance to existing antimicrobials was estimated to increase from 10.3 to 16.1% over 10 years based on current antibiotic availability and consumption. In comparison, the diversified use of a new antibiotic was associated with significant reduction in AMR (12.8% vs. 16.1%) and quality-adjusted life year (QALY) gains at a patient (7.7-10.3, dependent on antimicrobial efficacy) and population level (3657-8197, dependent on antimicrobial efficacy and the prevalence of AMR). Validation across several real-world data sources showed that the model output does not tend to systematically under- or over-estimate observed data.

CONCLUSIONS

The development of new antibiotics and the appropriate use of existing antibiotics are key to addressing the threat of AMR. This study presents a validated model that quantifies the value of new antibiotics through clinical and economic outcomes of relevance, and accounts for disease transmission of infection and development of AMR. In this context, the model may be a useful tool that could contribute to the decision-making process alongside other potential models and expert advice.

The cost-effectiveness of conducting three versus two reverse transcription-polymerase chain reaction tests for diagnosing and discharging people with COVID-19: evidence from the epidemic in Wuhan, China

OBJECTIVES

The objectives were to evaluate the effectiveness of conducting three versus two reverse transcription-PCR (RT-PCR) tests for diagnosing and discharging people with COVID-19 with regard to public health and clinical impacts by incorporating asymptomatic and presymptomatic infection and to compare the medical costs associated with the two strategies.

METHODS

A model that consisted of six compartments was built. The compartments were the susceptible (S), the asymptomatic infective (A), the presymptomatic infective (L), the symptomatic infective (I), the recovered (R), and the deceased (D). The A, L and I classes were infective states. To construct the model, several parameters were set as fixed using existing evidence and the rest of the parameters were estimated by fitting the model to a smoothed curve of the cumulative confirmed cases in Wuhan from 24 January 2020 to 6 March 2020. Input data about the cost-effectiveness analysis were retrieved from the literature.

RESULTS

Conducting RT-PCR tests three times for diagnosing and discharging people with COVID-19 reduced the estimated total number of symptomatic cases to 45‌ 013 from 51 144 in the two-test strategy over 43 days. The former strategy also led to 850.1 quality-adjusted life years (QALYs) of health gain and a net healthcare expenditure saving of CN¥49.1 million. About 100.7 QALYs of the health gain were attributable to quality-adjusted life day difference between the strategies during the analytic period and 749.4 QALYs were attributable to years of life saved.

CONCLUSIONS

More accurate strategies and methods of testing for the control of COVID-19 may reduce both the number of infections and the total medical costs. Increasing the number of tests should be considered in regions with relatively severe epidemics when existing tests have moderate sensitivity.