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

Critical assessment of uncertainty in economic evaluations on influenza vaccines for the elderly population in Spain

BACKGROUND

Influenza is a seasonal infection with a huge impact on morbidity and mortality in older adults, for whom vaccination is recommended. New influenza vaccines for this population have been introduced in Spain in the past 5 years, and a number of cost-effectiveness analyses (CEA) have been published to aid healthcare decision-making. The objective of this study was to assess possible sources of uncertainty in the CEAs of influenza vaccines for the older adults in Spain.

METHODS

A systematic review was performed to identify Spanish CEAs published since 2016. Potential sources of structural, methodologic and parametric uncertainty in CEA results were systematically analysed using the TRansparent Uncertainty ASsessmenT (TRUST) Tool, quality assessment checklists, and the WHO guidance on economic evaluations of influenza vaccine strategies. The primary sources of efficacy/effectiveness were analysed in depth to ascertain whether they could support the conclusions of the respective CEAs.

RESULTS

Seven CEAs were included. Overall, they were designed and performed in accordance with the applicable guidelines; however, some critical sources of uncertainty were detected, mainly: (1) the choice and use of efficacy/effectiveness data (real-world single season studies, meta-analyses including studies with high risk of bias and/or high heterogeneity with biased interpretation); (2) use of fewer than 5 seasons to estimate influenza burden; (3) generalized use of influenza-like illness data to estimate effectiveness and burden, among others.

CONCLUSIONS

Seemingly well-designed studies may conceal important sources of uncertainty that affect the results. This must be taken into account when interpreting results to support decision-making.

The role of economic evaluation in modelling public health and social measures for pandemic policy: a systematic review

BACKGROUND

Dynamic transmission models are often used to provide epidemiological guidance for pandemic policy decisions. However, how economic evaluation is typically incorporated into this technique to generate cost-effectiveness estimates of pandemic policy responses has not previously been reviewed.

METHODS

We systematically searched the Embase, PubMed and Scopus databases for dynamic epidemiological modelling studies that incorporated economic evaluation of public health and social measures (PHSMs), with no date restrictions, on 7 July 2024.

RESULTS

Of the 2,719 screened studies, 51 met the inclusion criteria. Most studies (n = 42, 82%) modelled SARS-CoV-2. A range of PHSMs were examined, including school closures, testing/screening, social distancing and mask use. Half of the studies utilised an extension of a Susceptible-Exposed-Infectious-Recovered (SEIR) compartmental model. The most common type of economic evaluation was cost-effectiveness analysis (n = 24, 47%), followed by cost-utility analysis (n = 17, 33%) and cost-benefit analysis (n = 17, 33%).

CONCLUSIONS

Economic evaluation is infrequently incorporated into dynamic epidemiological modelling studies of PHSMs. The scope of this research should be expanded, given the substantial cost implications of pandemic PHSM policy responses.

The Health and Economic Benefits of United States Investments in Measles and Rubella Control and Elimination

Background: Prior to measles vaccine introduction in 1963, measles virus caused hundreds of thousands of annual reported cases, which led to substantial US morbidity, mortality, and costs. Similarly, congenital rubella syndrome (CRS) led to highly visible and tragic lifelong disability for thousands of Americans, before rubella vaccine introduction in 1969. The US certified national virus transmission elimination of indigenous measles in 2000 and rubella in 2004. Methods: Applying an existing integrated transmission and economic model, this analysis characterizes the net benefits of US investments in measles (1963-2030) and rubella (1969-2030) immunization assuming continued high routine immunization coverage. Due to importation risks, the US maintains two doses of both vaccines in its routine immunization schedule. Results: This analysis estimates total US costs of 8.1 billion (economics reported in 2023 US dollars) for measles immunization for 1963-2023 and 14.1 billion for rubella immunization for 1969-2023. The analysis estimates an additional approximately 1.2 billion for measles immunization and 1.5 billion for rubella immunization expected for 2024-2030. Historical and future US investments prevented an estimated approximately 237 million measles infections, 228,000 measles deaths, 193 million rubella infections, and 166,000 CRS cases. These investments imply net benefits (from avoided treatment costs minus immunization costs) of approximately 310 billion for measles and 430 billion for rubella and CRS, even without incorporating avoided productivity losses and intangible costs. Conclusions: US investments in measles and rubella immunization continue to provide enormous savings of human and financial costs and to prevent substantial mortality and morbidity.

Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review

Objectives

To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).

Methods

We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.

Results

83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.

Conclusion

Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.

Lessons Learned from Model-based Economic Evaluations of COVID-19 Drug Treatments Under Pandemic Circumstances: Results from a Systematic Review

BACKGROUND

Following clinical research of potential coronavirus disease 2019 (COVID-19) treatments, numerous decision-analytic models have been developed. Due to pandemic circumstances, clinical evidence was limited and modelling choices were made under great uncertainty. This study aimed to analyse key methodological characteristics of model-based economic evaluations of COVID-19 drug treatments, and specifically focused on modelling choices which pertain to disease severity levels during hospitalisation, model structure, sources of effectiveness and quality of life and long-term sequelae.

METHODS

We conducted a systematic literature review and searched key databases (including MEDLINE, EMBASE, Web of Science, Scopus) for original articles on model-based full economic evaluations of COVID-19 drug treatments. Studies focussing on vaccines, diagnostic techniques and non-pharmaceutical interventions were excluded. The search was last rerun on 22 July 2023. Results were narratively synthesised in tabular form. Several aspects were categorised into rubrics to enable comparison across studies.

RESULTS

Of the 1047 records identified, 27 were included, and 23 studies (85.2%) differentiated patients by disease severity in the hospitalisation phase. Patients were differentiated by type of respiratory support, level of care management, a combination of both or symptoms. A Markov model was applied in 16 studies (59.3%), whether or not preceded by a decision tree or an epidemiological model. Most cost-utility analyses lacked the incorporation of COVID-19-specific health utility values. Of ten studies with a lifetime horizon, seven adjusted general population estimates to account for long-term sequelae (i.e. mortality, quality of life and costs), lasting for 1 year, 5 years, or a patient's lifetime. The most often reported parameter influencing the outcome of the analysis was related to treatment effectiveness.

CONCLUSION

The results illustrate the variety in modelling approaches of COVID-19 drug treatments and address the need for a more standardized approach in model-based economic evaluations of infectious diseases such as COVID-19.

TRIAL REGISTRY

Protocol registered in PROSPERO under CRD42023407646.

Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models

Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and ≥15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models.

The Full Value of Vaccine Assessments (FVVA): a framework for assessing and communicating the value of vaccines for investment and introduction decision-making

BACKGROUND

Several economic obstacles can deter the development and use of vaccines. This can lead to limited product options for some diseases, delays in new product development, and inequitable access to vaccines. Although seemingly distinct, these obstacles are actually interrelated and therefore need to be addressed through a single over-arching strategy encompassing all stakeholders.

METHODS

To help overcome these obstacles, we propose a new approach, the Full Value of Vaccines Assessments (FVVA) framework, to guide the assessment and communication of the value of a vaccine. The FVVA framework is designed to facilitate alignment across key stakeholders and to enhance decision-making around investment in vaccine development, policy-making, procurement, and introduction, particularly for vaccines intended for use in low- and middle-income countries.

RESULTS

The FVVA framework has three key elements. First, to enhance assessment, existing value-assessment methods and tools are adapted to include broader benefits of vaccines as well as opportunity costs borne by stakeholders. Second, to improve decision-making, a deliberative process is required to recognize the agency of stakeholders and to ensure country ownership of decision-making and priority setting. Third, the FVVA framework provides a consistent and evidence-based approach that facilitates communication about the full value of vaccines, helping to enhance alignment and coordination across diverse stakeholders.

CONCLUSIONS

The FVVA framework provides guidance for stakeholders organizing global-level efforts to promote investment in vaccines that are priorities for LMICs. By providing a more holistic view of the benefits of vaccines, its application also has the potential to encourage greater take-up by countries, thereby leading to more sustainable and equitable impacts of vaccines and immunization programmes.

Cost-Effectiveness of Vaccination of Older Adults with an MF59®-Adjuvanted Quadrivalent Influenza Vaccine Compared to Standard-Dose and High-Dose Vaccines in Denmark, Norway, and Sweden

Individuals aged 65 years and above are at increased risk of complications and death from influenza compared with any other age group. Enhanced vaccines, as the MF59®-adjuvanted quadrivalent influenza vaccine (aQIV) and the high-dose quadrivalent influenza vaccine (HD-QIV), provide increased protection for older adults in comparison to the traditional standard-dose quadrivalent influenza vaccines (SD-QIV). This study aimed to assess the cost-effectiveness of aQIV compared to SD-QIV and HD-QIV in Denmark, Norway, and Sweden for adults aged ≥65 years. A static decision tree model was used to evaluate costs and outcomes of different vaccination strategies from healthcare payer and societal perspectives. This model projects that compared to SD-QIV, vaccination with aQIV could prevent a combined total of 18,772 symptomatic influenza infections, 925 hospitalizations, and 161 deaths in one influenza season across the three countries. From a healthcare payer perspective, the incremental costs per quality adjusted life year (QALY) gained with aQIV versus SD-QIV were EUR 10,170/QALY in Denmark, EUR 12,515/QALY in Norway, and EUR 9894/QALY in Sweden. The aQIV was cost saving compared with HD-QIV. This study found that introducing aQIV to the entire population aged ≥65 years may contribute to reducing the disease and economic burden associated with influenza in these countries.

Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.

[Cost-effectiveness of universal childhood vaccination against hepatitis A in Spain: a dynamic approach]

OBJECTIVE

To evaluate the cost-effectiveness of infant universal vaccination against hepatitis A in Spain.

METHOD

Using a dynamic model and decision tree model, a cost-effectiveness analysis was performed to compare three vaccination strategies against hepatitis A: non-vaccination strategy versus universal childhood vaccination of hepatitis A with one or two doses. The perspective of the study was that of the National Health System (NHS) and a lifetime horizon was considered. Both costs and effects were discounted at 3% per year. Health outcomes were measured in terms of quality adjusted life years (QALY) and the cost-effectiveness measure used was the incremental cost-effectiveness ratio (ICER). In addition, deterministic sensitivity analysis by scenarios was performed.

RESULTS

In the particular case of Spain, with low endemicity for hepatitis A, the difference in health outcomes between vaccination strategies (with 1 or 2 doses) and non-vaccination are practically non-existent, terms of QALY. In addition, the ICER obtained is high, exceeding the limits of willingness to pay from Spain (€22,000-25,000/QALY). The deterministic sensitivity analysis showed that the results are sensitive to the variations of the key parameters, although in no case the vaccination strategies are cost-effective.

CONCLUSIONS

Universal infant vaccination strategy against hepatitis A would not be a cost-effective option from the NHS perspective in Spain.

Direct cost of cochlear implants in Germany - a strategic simulation

BACKGROUND

Despite the current undersupply of cochlear implants (CIs) with simultaneously increasing indication, CI implantation numbers in Germany still are at a relatively low level.

METHODS

As there are hardly any solid forecasts available in the literature, we develop a System Dynamics model that forecasts the number and costs of CI implantations in adults for 40 years from a social health insurance (SHI) perspective.

RESULTS

CI demand will grow marginally by demographic changes causing average annual costs of about 538 million €. Medical-technical progress with following relaxed indication criteria and patients' increasing willingness for implantation will increase implantation numbers significantly with average annual costs of 765 million €.

CONCLUSION

CI demand by adults will increase in the future, thus will the costs for CI supply. Continuous research and development in CI technology and supply is crucial to ensure long-term financing of the growing CI demand through cost-reducing innovations.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review

Objective

The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques.

Methods

For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary.

Results

Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data.

Conclusion

Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

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.

Economic Evaluation of Individualized Nutritional Support for Hospitalized Patients with Chronic Heart Failure

Background Malnutrition is a highly prevalent risk factor in hospitalized patients with chronic heart failure (CHF). A recent randomized trial found lower mortality and improved health outcomes when CHF patients with nutritional risk received individualized nutritional treatment. Objective To estimate the cost-effectiveness of individualized nutritional support in hospitalized patients with CHF. Methods This analysis used data from CHF patients at risk of malnutrition (N = 645) who were part of the Effect of Early Nutritional Therapy on Frailty, Functional Outcomes and Recovery of Undernourished Medical Inpatients Trial (EFFORT). Study patients with CHF were randomized into (i) an intervention group (individualized nutritional support to reach energy, protein, and micronutrient goals) or (ii) a control group (receiving standard hospital food). We used a Markov model with daily cycles (over a 6-month interval) to estimate hospital costs and health outcomes in the comparator groups, thus modeling cost-effectiveness ratios of nutritional interventions. Results With nutritional support, the modeled total additional cost over the 6-month interval was 15,159 Swiss Francs (SF). With an additional 5.77 life days, the overall incremental cost-effectiveness ratio for nutritional support vs. no nutritional support was 2625 SF per life day gained. In terms of complications, patients receiving nutritional support had a cost savings of 6214 SF and an additional 4.11 life days without complications, yielding an incremental cost-effectiveness ratio for avoided complications of 1513 SF per life day gained. Conclusions On the basis of a Markov model, this economic analysis found that in-hospital nutritional support for CHF patients increased life expectancy at an acceptable incremental cost-effectiveness ratio.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Cost-effectiveness of human papillomavirus vaccine in China: a systematic review of modelling studies

OBJECTIVES

China suffers from high burdens of human papillomavirus (HPV) and cervical cancer, whereas the uptake of HPV vaccine remains low. The first Chinese domestic HPV vaccine was released in 2019. However, collective evidence on cost-effectiveness of HPV vaccination in China has yet to be established. We summarised evidence on the cost-effectiveness of HPV vaccine in China.

DESIGN

Systematic review and narrative synthesis DATA SOURCES: PubMed, EMBASE, China National Knowledge Infrastructure and Wanfang Data were searched through 2 January 2021 ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Cost-effectiveness studies using a modelling approach focusing on HPV vaccination interventions in the setting of China were included for review.

DATA EXTRACTION AND SYNTHESIS

We extracted information from the selected studies focusing on cost-effectiveness results of various vaccination programmes, key contextual and methodological factors influencing cost-effectiveness estimates and an assessment of study quality.

RESULTS

A total of 14 studies were included for review. Considerable heterogeneity was found in terms of the methodologies used, HPV vaccination strategies evaluated and study quality. The reviewed studies generally supported the cost-effectiveness of HPV vaccine in China, although some reached alternative conclusions, particularly when assessed incremental to cervical cancer screening. Cost of vaccination was consistently identified as a key determinant for the cost-effectiveness of HPV vaccination programmes.

CONCLUSIONS

Implementing HPV vaccination programmes should be complemented with expanded cervical cancer screening, while the release of lower-priced domestic vaccine offers more promising potential for initiating public HPV vaccination programmes. Findings of this study contributes important evidence for policies for cervical cancer prevention in China and methodological implications for future modelling efforts.

Cost-Effectiveness of Molecular Method Diagnostic for Rapid Detection of Antibiotic-Resistant Bacteria

OBJECTIVES

This study aimed to perform a cost-effectiveness analysis (CEA) of the molecular diagnostic method (MM) associated with conventional diagnostic method (CM) compared with the CM alone, for the detection of resistant profile in bacteremia, from the perspective of the Brazilian Public Health System, in intensive care units setting.

METHODS

The clinical parameters regarding methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Gram-negative bacteria (CRGNB), and vancomycin-resistant Enterococcus spp. (VRE) infections were collected from searches on PubMed, Scopus, and SciELO, using specific keywords. Data on direct medical costs to treat these infections were collected according to Brazilian Public Health System perspective from Brazilian databases, in tables of 2018 to 2019. CEA was performed after building a dynamic model, which was calibrated and validated according to international recommendations. The incremental cost-effectiveness ratio of the MM + CM compared with the CM was calculated using the outcomes "avoided death" and "avoided resistant infections." One-way sensitivity analyses were performed.

RESULTS

This CEA demonstrated that the MM + CM was dominant in all scenarios. Estimates showed that for MRSA, CRGNB, and VRE infections, every avoided death would lead to savings of Brazilian real (R$) 4.9 million ($937 301), R$2.2 million ($419 899), and R$1.3 million ($248 919), respectively. The same infections assessed by avoided resistant infections savings were projected to be R$24 964 ($4686), R$40 260 ($7558), and R$23 867 ($4480).

CONCLUSIONS

MM leads to cost reduction and increased benefits, optimizing the use of financial resources on the health system in the intensive care unit setting, in bacteremia caused by MRSA, CRGNB, and VRE.

Informing decision makers seeking to improve vaccination programs: case-study Serbia

Background:The optimisation of vaccine policies before their implementation is beholden upon public health decision makers, seeking to maximise population health. In this case study in Serbia, the childhood vaccines under consideration included pneumococcal conjugate vaccination (PCV), rotavirus (RV) vaccination and varicella zoster virus (VZV) vaccination. Objective: The objective of this study is to define the optimal order of introduction of vaccines to minimise deaths, quality adjusted life years (QALYs) lost, or hospitalisation days, under budget and vaccine coverage constraints. Methods: A constrained optimisation model was developed including a static multi-cohort decision-tree model for the three infectious diseases. Budget and vaccine coverage were constrained, and to rank the vaccines, the optimal solution to the linear programming problem was based upon the ratio of the outcome (deaths, QALYs or hospitalisation days) per unit of budget. A probabilistic decision analysis Monte Carlo simulation technique was used to test the robustness of the rankings. Results: PCV was the vaccine ranked first to minimise deaths, VZV vaccination for QALY loss minimisation and RV vaccination for hospitalisation day reduction. Sensitivity analysis demonstrated the most robust ranking was that for PCV minimizing deaths. Conclusion: Constrained optimisation modelling, whilst considering all potential interventions currently, provided a comprehensive and rational approach to decision making.

Are COVID-19 models blind to the social determinants of health? A systematic review protocol

INTRODUCTION

Infectious disease models are important tools to inform public health policy decisions. These models are primarily based on an average population approach and often ignore the role of social determinants in predicting the course of a pandemic and the impact of policy interventions. Ignoring social determinants in models may cause or exacerbate inequalities. This limitation has not been previously explored in the context of the current pandemic, where COVID-19 has been found to disproportionately affect marginalised racial, ethnic and socioeconomic groups. Therefore, our primary goal is to identify the extent to which COVID-19 models incorporate the social determinants of health in predicting outcomes of the pandemic.

METHODS AND ANALYSIS

We will search MEDLINE, EMBASE, Cochrane Library and Web of Science databases from December 2019 to August 2020. We will assess all infectious disease modelling studies for inclusion of social factors that meet the following criteria: (a) focused on human spread of SARS-CoV-2; (b) modelling studies; (c) interventional or non-interventional studies; and (d) focused on one of the following outcomes: COVID-19-related outcomes (eg, cases, deaths), non-COVID-19-related outcomes (ie, impacts of the pandemic or control policies on other health conditions or health services), or impact of the pandemic or control policies on economic outcomes. Data will only be extracted from models incorporating social factors. We will report the percentage of models that considered social factors, indicate which social factors were considered, and describe how social factors were incorporated into the conceptualisation and implementation of the infectious disease models. The extracted data will also be used to create a narrative synthesis of the results.

ETHICS AND DISSEMINATION

Ethics approval is not required as only secondary data will be collected. The results of this systematic review will be disseminated through peer-reviewed publication and conference proceedings.

PROSPERO REGISTRATION NUMBER

CRD42020207706.

Is modelling complexity always needed? Insights from modelling PrEP introduction in South Africa

BACKGROUND

Mathematical models can be powerful policymaking tools. Simple, static models are user-friendly for policymakers. More complex, dynamic models account for time-dependent changes but are complicated to understand and produce. Under which conditions are static models adequate? We compare static and dynamic model predictions of whether behavioural disinhibition could undermine the impact of HIV pre-exposure prophylaxis (PrEP) provision to female sex workers in South Africa.

METHODS

A static model of HIV risk was developed and adapted into a dynamic model. Both models were used to estimate the possible reduction in condom use, following PrEP introduction, without increasing HIV risk. The results were compared over a 20-year time horizon, in two contexts: at epidemic equilibrium and during an increasing epidemic.

RESULTS

Over time horizons of up to 5 years, the models are consistent. Over longer timeframes, the static model overstates the tolerated reduction in condom use where initial condom use is reasonably high ($\ge$50%) and/or PrEP effectiveness is low ($\le$45%), especially during an increasing epidemic.

CONCLUSIONS

Static models can provide useful deductions to guide policymaking around the introduction of a new HIV intervention over short-medium time horizons of up to 5 years. Over longer timeframes, static models may not sufficiently emphasise situations of programmatic importance, especially where underlying epidemics are still increasing.

Systematic review of reporting quality of economic evaluations in plastic surgery based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement

BACKGROUND

Economic evaluations in healthcare are designed to inform decisions by the estimation of cost and effect trade-off of two or more interventions. This review identified and appraised the quality of reporting of economic evaluations in plastic surgery based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement.

METHODS

Electronic databases were searched: MEDLINE, EMBASE, The Cochrane Library, Ovid Health Star, and Business Source Complete from January 1, 2012 to November 30, 2019. Data extracted included: the type of economic evaluation (i.e., cost-utility analysis (CUA), cost-effectiveness analysis (CEA), cost-benefit analysis (CBA), cost-minimization analysis (CMA)), domain of plastic surgery, journal, year, and country of publication. The CHEERS checklist (with 24 items) was used to appraise the quality of reporting.

RESULTS

Ninety-two economic evaluations were identified; CUA (10%), CEA (31%), CBA (4%), and CMA (50%). Breast surgery was the top domain (48%). Most were conducted in the USA (61%) and published in Plastic and Reconstructive Surgery journal (28%). One-third were published in the last two years. The average CHEERS checklist compliance score was 15 (63%). The average CHEERS checklist compliance score per type of evaluation was 19 (77%) for CUA, 17 (70%) for CEA, 13 (52%) for CBA, and 14 (57%) for CMA. The least reported CHEERS checklist items included: time horizon (15%), discount rate (18%), and assessment of heterogeneity (15%). Thirty-two percent of studies were inappropriately titled (i.e., methodologically incorrect).

CONCLUSION

Quality of reporting of economic evaluations is suboptimal. The CHEERS checklist should be consulted when performing and reporting economic evaluations in plastic surgery.

Evaluation of Contact-Tracing Policies against the Spread of SARS-CoV-2 in Austria: An Agent-Based Simulation

BACKGROUND

Many countries have already gone through several infection waves and mostly managed to successfully stop the exponential spread of SARS-CoV-2 through bundles of restrictive measures. Still, the danger of further waves of infections is omnipresent, and it is apparent that every containment policy must be carefully evaluated and possibly replaced by a different, less restrictive policy before it can be lifted. Tracing of contacts and consequential breaking of infection chains is a promising strategy to help contain the disease, although its precise impact on the epidemic is unknown.

OBJECTIVE

In this work, we aim to quantify the impact of tracing on the containment of the disease and investigate the dynamic effects involved.

DESIGN

We developed an agent-based model that validly depicts the spread of the disease and allows for exploratory analysis of containment policies. We applied this model to quantify the impact of different approaches of contact tracing in Austria to derive general conclusions on contract tracing.

RESULTS

The study displays that strict tracing complements other intervention strategies. For the containment of the disease, the number of secondary infections must be reduced by about 75%. Implementing the proposed tracing strategy supplements measures worth about 5%. Evaluation of the number of preventively quarantined persons shows that household quarantine is the most effective in terms of avoided cases per quarantined person.

LIMITATIONS

The results are limited by the validity of the modeling assumptions, model parameter estimates, and the quality of the parametrization data.

CONCLUSIONS

The study shows that tracing is indeed an efficient measure to keep case numbers low but comes at a high price if the disease is not well contained. Therefore, contact tracing must be executed strictly, and adherence within the population must be held up to prevent uncontrolled outbreaks of the disease.

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.

Cost-effectiveness of maternal pertussis immunization: Implications of a dynamic transmission model for low- and middle-income countries

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(84) Low- and middle-income countries (LMICs) have experienced a resurgence of pertussis.

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(74) Maternal aP immunization could prevent pertussis among very young infants.

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(84) A dynamic transmission model was used to evaluate maternal aP immunization in LMICs.

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(82) Maternal aP is cost-effective when infant vaccination coverage is moderate or low.

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(85) Maternal aP immunization is not cost-effective in LMICs with infant coverage 90-95%.

Objective

This study evaluates the cost-effectiveness of maternal acellular pertussis (aP) immunization in low- and middle-income countries using a dynamic transmission model.

Methods

We developed a dynamic transmission model to simulate the impact of infant vaccination with whole-cell pertussis (wP) vaccine with and without maternal aP immunization. The model was calibrated to Brazilian surveillance data and then used to project health outcomes and costs under alternative strategies in Brazil, and, after adjusting model parameter values to reflect their conditions, in Nigeria and Bangladesh. The primary measure of cost-effectiveness is incremental cost (2014 USD) per disability-adjusted life-year (DALY).

Results

The dynamic model shows that maternal aP immunization would be cost-effective in Brazil, a middle-income country, under the base-case assumptions, but would be very expensive at infant vaccination coverage in and above the threshold range necessary to eliminate the disease (90–95%). At 2007 infant coverage (DTP1 90%, DTP3 61% at 1 year of age), maternal immunization would cost < $4,000 per DALY averted. At high infant coverage, such as Brazil in 1996 (DTP1 94%, DTP3 74% at 1 year), cost/DALY increases to $1.27 million. When the model’s time horizon was extended from 2030 to 2100, cost/DALY increased under both infant coverage levels, but more steeply with high coverage. The results were moderately sensitive to discount rate, maternal vaccine price, and maternal aP coverage and were robust using the 100 best-fitting parameter sets. Scenarios representing low-income countries showed that maternal aP immunization could be cost-saving in countries with low infant coverage, such as Nigeria, but very expensive in countries, such as Bangladesh, with high infant coverage.

Conclusion

A dynamic model, which captures the herd immunity benefits of pertussis vaccination, shows that, in low- and middle-income countries, maternal aP immunization is cost-effective when infant vaccination coverage is moderate, even cost-saving when it is low, but not cost-effective when coverage levels pass 90–95%.

Comparison of static and dynamic models of maternal immunization to prevent infant pertussis in Brazil

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Dynamic transmission models of infectious disease capture the herd immunity effects of vaccination.

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We compared dynamic and static models of maternal acellular pertussis (aP) immunization built with Brazilian data.

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At infant vaccine coverage < 90–95%, both models estimate that maternal immunization is cost-effective.

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Only the dynamic model shows that maternal immunization is not cost-effective at infant coverage > 90–95%.

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The background effect of routine infant vaccination is critical to the cost-effectiveness of maternal aP immunization.

Background

This paper compares cost-effectiveness results from two models of maternal immunization to prevent pertussis in infants in Brazil, one static, one dynamic, to explore when static models are adequate for public health decisions and when the extra effort required by dynamic models is worthwhile.

Methods

We defined two scenarios to explore key differences between static and dynamic models, herd immunity and time horizon. Scenario 1 evaluates the incremental cost/DALY of maternal acellular pertussis (aP) immunization as routine infant vaccination coverage ranges from low/moderate up to, and above, the threshold at which herd immunity begins to eliminate pertussis. Scenario 2 compares cost-effectiveness estimates over the models’ different time horizons. Maternal vaccine prices of $9.55/dose (base case) and $1/dose were evaluated.

Results

The dynamic model shows that maternal immunization could be cost-saving as well as life-saving at low levels of infant vaccination coverage. When infant coverage reaches the threshold range (90–95%), it is expensive: the dynamic model estimates that maternal immunization costs $2 million/DALY at infant coverage > 95% and maternal vaccine price of $9.55/dose; at $1/dose, cost/DALY is $200,000. By contrast, the static model estimates costs/DALY only modestly higher at high than at low infant coverage. When the models’ estimates over their different time horizons are compared at infant coverage < 90–95%, their projections fall in the same range.

Conclusions

Static models may serve to explore an intervention’s cost-effectiveness against infectious disease: the direction and principal drivers of change were the same in both models. When, however, an intervention too small to have significant herd immunity effects itself, such as maternal aP immunization, takes place against a background of vaccination in the rest of the population, a dynamic model is crucial to accurate estimates of cost-effectiveness. This finding is particularly important in the context of widely varying routine infant vaccination rates globally.

Clinical Trial registry

Clinical Trial registry name and registration number: Not applicable.

COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

Economic evaluation of meningococcal serogroup B (MenB) vaccines: A systematic review

BACKGROUND

Meningococcal serogroup B (MenB) has emerged as the leading cause of invasive meningococcal disease (IMD) in several countries following the release of effective vaccines against serogroups A, C, W, and Y. In 2013, however, the first multicomponent MenB vaccine (Bexsero®) was licensed in Europe.

AIM

To review the evidence on the cost-effectiveness of vaccination against MenB.

METHODS

Searches were performed in MEDLINE, EMBASE, Web of Science, NHS EED, Econlit, Tufts CEA registry, and HTA. Three reviewers independently screened and selected studies. Using a narrative synthesis, studies were categorized by vaccination strategies. The quality of included studies was assessed using the Comparative Health Economics Evaluation Reporting Standards (CHEERS) checklist.

RESULTS

13 studies were included. Ten studies were conducted in the European region and three in the Americas. None of the vaccination strategies were considered cost-effective. Including herd effects improved value for money for MenB vaccines. Routine infant vaccination was the most effective short-term strategy, however, adolescent strategies offered the best value for money. Without herd immunity, routine infant vaccination had the lowest incremental cost-effectiveness ratio estimates.

CONCLUSION

Routine MenB vaccination does not offer substantial value for money, mainly due to high vaccine costs and low disease incidence.

Evidence of the effectiveness of travel-related measures during the early phase of the COVID-19 pandemic: a rapid systematic review

OBJECTIVE

To review the effectiveness of travel measures implemented during the early stages of the COVID-19 pandemic to inform changes on how evidence is incorporated in the International Health Regulations (2005) (IHR).

DESIGN

We used an abbreviated Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols to identify studies that investigated the effectiveness of travel-related measures preprinted or published by 1 June 2020.

RESULTS

We identified 29 studies, of which 26 were modelled. Thirteen studies investigated international measures, while 17 investigated domestic measures (one investigated both). There was a high level of agreement that the adoption of travel measures led to important changes in the dynamics of the early phases of the COVID-19 pandemic: the Wuhan measures reduced the number of cases exported internationally by 70%-80% and led to important reductions in transmission within Mainland China. Additional travel measures, including flight restrictions to and from China, may have led to additional reductions in the number of exported cases. Few studies investigated the effectiveness of measures implemented in other contexts. Early implementation was identified as a determinant of effectiveness. Most studies of international travel measures did not account for domestic travel measures thus likely leading to biased estimates.

CONCLUSION

Travel measures played an important role in shaping the early transmission dynamics of the COVID-19 pandemic. There is an urgent need to address important evidence gaps and also a need to review how evidence is incorporated in the IHR in the early phases of a novel infectious disease outbreak.

Modelling a cost-effective vaccination strategy for the prevention of varicella and herpes zoster infection: A systematic review

BACKGROUND

Varicella zoster virus (VZV) and its re-emergence as herpes zoster (HZ) is associated with significant morbidity and mortality. While studies show that VZV vaccination is effective in reducing VZV incidence, many decision makers have not added VZV to their vaccination schedule, largely due to uncertainty surrounding the effect of VZV vaccination on HZ incidence (exogenous boosting, EB), and the cost-effectiveness (CE) of vaccination.

METHODS

A systematic review was conducted to identify the current published evidence of CE of VZV vaccination strategies where both VZV and HZ incidence were modelled.

RESULTS

Six studies (one published in 2003 and five between 2010 and 2019), were identified with all conducting cost-utility analysis using a dynamic transmission modelling approach and assuming EB. All predicted that mass infant VZV vaccination would rapidly reduce VZV incidence, but HZ incidence would increase. Compared with no-vaccination, the CE of VZV vaccination strategies ranged from higher costs and poorer outcomes (dominated), towards CE (incremental cost-effectiveness ratios of between $7,000 to $61,000 USD), or lower cost and better outcomes (dominant). However, without EB, HZ incidence immediately dropped below pre-vaccination levels making VZV vaccination quickly CE and/or dominant to a no vaccination strategy.

CONCLUSIONS

Current models are sensitive to assumptions of EB suggesting that future studies consider an agent-based modelling approach to address the individual nature of variables that determine the infectiousness of VZV.

Cost-effectiveness of the long-acting regimen cabotegravir plus rilpivirine for the treatment of HIV-1 and its potential impact on adherence and viral transmission: A modelling study

Introduction

Combination antiretroviral therapy (cART) improves outcomes for people living with HIV (PLWH) but requires adherence to daily dosing. Suboptimal adherence results in reduced treatment effectiveness, increased costs, and greater risk of resistance and onwards transmission. Treatment with long-acting (LA), injection-based ART administered by healthcare professionals (directly observed therapy (DOT)) eliminates the need for adherence to daily dosing and may improve clinical outcomes. This study reports the cost-effectiveness of the cabotegravir plus rilpivirine LA regimen (CAB+RPV LA) and models the potential impact of LA DOT therapies.

Methods

Parameterisation was performed using pooled data from recent CAB+RPV LA Phase III trials. The analysis was conducted using a cohort-level hybrid decision-tree and state-transition model, with states defined by viral load and CD4 cell count. The efficacy of oral cART was adjusted to reflect adherence to daily regimens from published data. A Canadian health service perspective was adopted.

Results

CAB+RPV LA is predicted to be the dominant intervention when compared to oral cART, generating, per 1,000 patients treated, lifetime cost-savings of $1.5 million, QALY and life-year gains of 107 and 138 respectively with three new HIV cases averted.

Conclusions

Economic evaluations of LA DOTs need to account for the impact of adherence and HIV transmission. This study adds to the existing literature by incorporating transmission and using clinical data from the first LA DOT regimen. Providing PLWH and healthcare providers with novel modes of ART administration, enhancing individualisation of treatment, may facilitate the achievement of UNAIDS 95-95-95 objectives.

Modeling the cost-effectiveness of maternal acellular pertussis immunization (aP) in different socioeconomic settings: A dynamic transmission model of pertussis in three Brazilian states

OBJECTIVES

Using dynamic transmission models we evaluated the health and cost outcomes of adding acellular pertussis (aP) vaccination of pregnant women to infant vaccination in three Brazilian states that represent different socioeconomic conditions. The primary objective was to determine whether the same model structure could be used to represent pertussis disease dynamics in differing socioeconomic conditions.

METHODS

We tested three model structures (SIR, SIRS, SIRSIs) to represent population-level transmission in three socio-demographically distinct Brazilian states: São Paulo, Paraná and Bahia. Two strategies were evaluated: infant wP vaccination alone versus maternal aP immunization plus infant wP vaccination. Model projections for 2014-2029 include outpatient and inpatient pertussis cases, pertussis deaths, years of life lost, disability-adjusted life-years (DALYs) lost, and costs (in 2014 USD) of maternal aP vaccination, infant vaccination, and pertussis medical treatment. Incremental cost per DALY averted is presented from the perspective of the Brazilian National Health System.

RESULTS

Based on goodness-of-fit statistics, the SIRSIs model fit best, although it had only a modest improvement in statistical quantitative assessments relative to the SIRS model. For all three Brazilian states, maternal aP immunization led to higher costs but also saved infant lives and averted DALYs. The 2014 USD cost/DALY averted was $3068 in Sao Paulo, $2962 in Parana, and $2022 in Bahia. These results were robust in sensitivity analyses with the incremental cost-effectiveness ratios exceeding per capita gross regional product only when the probability that a pertussis case is reported was assumed higher than base case implying more overt cases and deaths and therefore more medical costs.

CONCLUSIONS

The same model structure fit all three states best, supporting the idea that the disease behaves similarly across different socioeconomic conditions. We also found that immunization of pregnant women with aP is cost-effective in diverse Brazilian states.

Cost-Effectiveness of Quadrivalent Versus Trivalent Influenza Vaccination in the Dutch National Influenza Prevention Program

OBJECTIVES

As of 2019, quadrivalent influenza vaccine (QIV) has replaced trivalent influenza vaccine (TIV) in the national immunization program in The Netherlands. Target groups are individuals of 60+ years of age and those with chronic diseases. The objective was to estimate the incremental break-even price of QIV over TIV at a threshold of €20 000 per quality-adjusted life-year (QALY).

METHODS

An age-structured compartmental dynamic model was adapted for The Netherlands to assess health outcomes and associated costs of vaccinating all individuals at higher risk for influenza with QIV instead of TIV over the seasons 2010 to 2018. Influenza incidence rates were derived from a global database. Other parameters (probabilities, QALYs and costs) were extracted from the literature and applied according to Dutch guidelines. A threshold of €20 000 per QALY was applied to estimate the incremental break-even prices of QIV versus TIV. Sensitivity analyses were performed to test the robustness of the model outcomes.

RESULTS

Retrospectively, vaccination with QIV instead of TIV could have prevented on average 9500 symptomatic influenza cases, 2130 outpatient visits, 84 hospitalizations, and 38 deaths per year over the seasons 2010 to 2018. This translates into 385 QALYs and 398 life-years potentially gained. On average, totals of €431 527 direct and €2 388 810 indirect costs could have been saved each year.

CONCLUSION

Using QIV over TIV during the influenza seasons 2010 to 2018 would have been cost-effective at an incremental price of maximally €3.81 (95% confidence interval, €3.26-4.31). Sensitivity analysis showed consistent findings on the incremental break-even price in the same range.

Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action

INTRODUCTION

High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications.

METHODS

We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors.

RESULTS

A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies.

CONCLUSIONS

This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making

OBJECTIVES

The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs).

STUDY DESIGN AND SETTING

Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach.

RESULTS

Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either "off-the-shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines.

CONCLUSION

This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a rapidly emerging disease classified as a pandemic by the World Health Organization (WHO). To support the WHO with their recommendations on quarantine, we conducted a rapid review on the effectiveness of quarantine during severe coronavirus outbreaks.

OBJECTIVES

To assess the effects of quarantine (alone or in combination with other measures) of individuals who had contact with confirmed or suspected cases of COVID-19, who travelled from countries with a declared outbreak, or who live in regions with high disease transmission.

SEARCH METHODS

An information specialist searched the Cochrane COVID-19 Study Register, and updated the search in PubMed, Ovid MEDLINE, WHO Global Index Medicus, Embase, and CINAHL on 23 June 2020.

SELECTION CRITERIA

Cohort studies, case-control studies, time series, interrupted time series, case series, and mathematical modelling studies that assessed the effect of any type of quarantine to control COVID-19. We also included studies on SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) as indirect evidence for the current coronavirus outbreak.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened abstracts and titles in duplicate. Two review authors then independently screened all potentially relevant full-text publications. One review author extracted data, assessed the risk of bias and assessed the certainty of evidence with GRADE and a second review author checked the assessment. We used three different tools to assess risk of bias, depending on the study design: ROBINS-I for non-randomised studies of interventions, a tool provided by Cochrane Childhood Cancer for non-randomised, non-controlled studies, and recommendations from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) for modelling studies. We rated the certainty of evidence for the four primary outcomes: incidence, onward transmission, mortality, and costs.

MAIN RESULTS

We included 51 studies; 4 observational studies and 28 modelling studies on COVID-19, one observational and one modelling study on MERS, three observational and 11 modelling studies on SARS, and three modelling studies on SARS and other infectious diseases. Because of the diverse methods of measurement and analysis across the outcomes of interest, we could not conduct a meta-analysis and undertook a narrative synthesis. We judged risk of bias to be moderate for 2/3 non-randomized studies of interventions (NRSIs) and serious for 1/3 NRSI. We rated risk of bias moderate for 4/5 non-controlled cohort studies, and serious for 1/5. We rated modelling studies as having no concerns for 13 studies, moderate concerns for 17 studies and major concerns for 13 studies. Quarantine for individuals who were in contact with a confirmed/suspected COVID-19 case in comparison to no quarantine Modelling studies consistently reported a benefit of the simulated quarantine measures, for example, quarantine of people exposed to confirmed or suspected cases may have averted 44% to 96% of incident cases and 31% to 76% of deaths compared to no measures based on different scenarios (incident cases: 6 modelling studies on COVID-19, 1 on SARS; mortality: 2 modelling studies on COVID-19, 1 on SARS, low-certainty evidence). Studies also indicated that there may be a reduction in the basic reproduction number ranging from 37% to 88% due to the implementation of quarantine (5 modelling studies on COVID-19, low-certainty evidence). Very low-certainty evidence suggests that the earlier quarantine measures are implemented, the greater the cost savings may be (2 modelling studies on SARS). Quarantine in combination with other measures to contain COVID-19 in comparison to other measures without quarantine or no measures When the models combined quarantine with other prevention and control measures, such as school closures, travel restrictions and social distancing, the models demonstrated that there may be a larger effect on the reduction of new cases, transmissions and deaths than measures without quarantine or no interventions (incident cases: 9 modelling studies on COVID-19; onward transmission: 5 modelling studies on COVID-19; mortality: 5 modelling studies on COVID-19, low-certainty evidence). Studies on SARS and MERS were consistent with findings from the studies on COVID-19. Quarantine for individuals travelling from a country with a declared COVID-19 outbreak compared to no quarantine Very low-certainty evidence indicated that the effect of quarantine of travellers from a country with a declared outbreak on reducing incidence and deaths may be small for SARS, but might be larger for COVID-19 (2 observational studies on COVID-19 and 2 observational studies on SARS).

AUTHORS' CONCLUSIONS

The current evidence is limited because most studies on COVID-19 are mathematical modelling studies that make different assumptions on important model parameters. Findings consistently indicate that quarantine is important in reducing incidence and mortality during the COVID-19 pandemic, although there is uncertainty over the magnitude of the effect. Early implementation of quarantine and combining quarantine with other public health measures is important to ensure effectiveness. In order to maintain the best possible balance of measures, decision makers must constantly monitor the outbreak and the impact of the measures implemented. This review was originally commissioned by the WHO and supported by Danube-University-Krems. The update was self-initiated by the review authors.

Simulating the End of AIDS in New York: Using Participatory Dynamic Modeling to Improve Implementation of the Ending the Epidemic Initiative

OBJECTIVES

In 2014, the governor of New York announced the Ending the Epidemic (ETE) plan to reduce annual new HIV infections from 3000 to 750, achieve a first-ever decrease in HIV prevalence, and reduce AIDS progression by the end of 2020. The state health department undertook participatory simulation modeling to develop a baseline for comparing epidemic trends and feedback on ETE strategies.

METHODS

A dynamic compartmental model projected the individual and combined effects of 3 ETE initiatives: enhanced linkage to and retention in HIV treatment, increased preexposure prophylaxis (PrEP) among men who have sex with men, and expanded housing assistance. Data inputs for model calibration and low-, medium-, and high-implementation scenarios (stakeholders' rollout predictions, and lower and upper bounds) came from surveillance and program data through 2014, the literature, and expert judgment.

RESULTS

Without ETE (baseline scenario), new HIV infections would decline but remain >750, and HIV prevalence would continue to increase by 2020. Concurrently implementing the 3 programs would lower annual new HIV infections by 16.0%, 28.1%, and 45.7% compared with baseline in the low-, medium-, and high-implementation scenarios, respectively. In all concurrent implementation scenarios, although annual new HIV infections would remain >750, there would be fewer new HIV infections than deaths, yielding the first-ever decrease in HIV prevalence. PrEP and enhanced linkage and retention would confer the largest population-level changes.

CONCLUSIONS

New York State will achieve 1 ETE benchmark under the most realistic (medium) implementation scenario. Findings facilitated framing of ETE goals and underscored the need to prioritize men who have sex with men and maintain ETE's multipronged approach, including other programs not modeled here.

Transitions: Novel Study Methods to Understand Early HIV Risk Among Adolescent Girls and Young Women in Mombasa, Kenya, and Dnipro, Ukraine

Transitions aims to understand the human immunodeficiency virus (HIV) risk at critical transition points in the sexual life course of adolescent girls and young women (AGYW) who engage in casual sex, transactional sex, and sex work. In this article, we present the Transitions study methods. The Transitions study has the following objectives: (1) to describe how the characteristics and length of the transition period and access gap vary across two epidemiological contexts (Mombasa, Kenya, and Dnipro, Ukraine); (2) to understand how the risk of HIV varies by length and characteristics of the transition period and access gap across epidemiologic contexts; and (3) to assess the extent to which HIV infections acquired during the transition period and access gap could mitigate the population-level impact of focused interventions for female sex workers and explore the potential marginal benefit of expanding programs to reach AGYW during the transition period and access gap. Cross-sectional biobehavioral data were collected from young women aged 14 to 24 years who were recruited from locations in Mombasa County, Kenya, and Dnipro, Ukraine, where sex work took place. Data are available for 1,299 Kenyan and 1,818 Ukrainian participants. The survey addressed the following areas: timing of transition events (first sex, first exchange of sex for money or other resources, self-identification as sex workers, entry into formal sex work, access to prevention program services); sexual behaviors (condom use, anal sex, sex under the influence of drugs or alcohol); partnerships (regular and first-time clients, regular and first-time transactional sex partners, and husbands and boyfriends); alcohol use; injection and non-injection illicit drug use; experience of violence; access to HIV prevention and treatment program; testing for sexually transmitted and blood-borne infections and HIV; and reproductive health (pregnancies, abortions, contraceptives). HIV and hepatitis C virus prevalence data were based on rapid test results. Mathematical modeling will be used to generate projections of onward HIV transmission at specific transition points in the sexual life course of AGYW. Taken together, these data form a novel data resource providing comprehensive behavioral, structural, and biological data on a high-risk group of AGYW in two distinct sociocultural and epidemiologic contexts.

Cost-effectiveness of the HepCATT intervention in specialist drug clinics to improve case-finding and engagement with HCV treatment for people who inject drugs in England

BACKGROUND AND AIMS

People who inject drugs (PWID) are at high risk of hepatitis C virus (HCV) infection; however, ~50% are undiagnosed in England and linkage-to-care is poor. This study investigated the cost-effectiveness of an intervention (HepCATT) to improve case-finding and referral to HCV treatment compared with standard-of-care pathways in drug treatment centres in England.

DESIGN

HCV transmission and disease progression model with cost-effectiveness analysis using a health-care perspective. Primary outcome and cost data from the HepCATT study parameterized the intervention, suggesting that HepCATT increased HCV testing in drug treatment centres 2.5-fold and engagement onto the HCV treatment pathway 10-fold. A model was used to estimate the decrease in HCV infections and HCV-related deaths from 2016, with costs and health benefits (quality-adjusted life-years or QALYs) tracked over 50 years. Univariable and probabilistic sensitivity analyses (PSA) were undertaken.

SETTING

England-specific epidemic with 40% prevalence of chronic HCV among PWID.

PARTICIPANTS

PWID attending drug treatment centres.

INTERVENTION

Nurse facilitator in drug treatment centres to improve the HCV care pathway from HCV case-finding to referral and linkage to specialist care. Comparator was the standard-of-care HCV care pathway.

MEASUREMENTS

Incremental cost-effectiveness ratio (ICER) in terms of cost per QALY gained through improved case-finding.

FINDINGS

Over 50 years per 1000 PWID, the HepCATT intervention could prevent 75 (95% central interval 37-129) deaths and 1330 (827-2040) or 51% (30-67%) of all new infections. The mean ICER was £7986 per QALY gained, with all PSA simulations being cost-effective at a £20 000 per QALY willingness-to-pay threshold. Univariable sensitivity analyses suggest the intervention would become cost-saving if the cost of HCV treatment reduces to £3900. If scaled up to all PWID in England, the intervention would cost £8.8 million and decrease incidence by 56% (33-70%) by 2030.

CONCLUSIONS

Increasing hepatitis C virus infection case-finding and treatment referral in drug treatment centres could be a highly cost-effective strategy for decreasing hepatitis C virus incidence among people who inject drugs.

A novel method to estimate the indirect community benefit of HIV interventions using a microsimulation model of HIV disease

BACKGROUND

Microsimulation models of human immunodeficiency virus (HIV) disease that simulate individual patients one at a time and assess clinical and economic outcomes of HIV interventions often provide key details regarding direct individual clinical benefits ("individual benefit"), but they may lack detail on transmissions, and thus may underestimate an intervention's indirect benefits ("community benefit"). Dynamic transmission models can be used to simulate HIV transmissions, but they may do so at the expense of the clinical detail of microsimulations. We sought to develop, validate, and demonstrate a practical, novel method that can be integrated into existing HIV microsimulation models to capture this community benefit, integrating the effects of reduced transmission while keeping the clinical detail of microsimulations.

METHODS

We developed a new method to capture the community benefit of HIV interventions by estimating HIV transmissions from the primary cohort of interest. The method captures the benefit of averting infections within the cohort of interest by estimating a corresponding gradual decline in incidence within the cohort. For infections averted outside the cohort of interest, our method estimates transmissions averted based on reductions in HIV viral load within the cohort, and the benefit (life-years gained and cost savings) of averting those infections based on the time they were averted. To assess the validity of our method, we paired it with the Cost-effectiveness of Preventing AIDS Complications (CEPAC) Model - a validated and widely-published microsimulation model of HIV disease. We then compared the consistency of model-estimated outcomes against outcomes of a widely-validated dynamic compartmental transmission model of HIV disease, the HIV Optimization and Prevention Economics (HOPE) model, using the intraclass correlation coefficient (ICC) with a two-way mixed effects model. Replicating an analysis done with HOPE, validation endpoints were number of HIV transmissions averted by offering pre-exposure prophylaxis (PrEP) to men who have sex with men (MSM) and people who inject drugs (PWID) in the US at various uptake and efficacy levels. Finally, we demonstrated an application of our method in a different setting by evaluating the clinical and economic outcomes of a PrEP program for MSM in India, a country currently considering PrEP rollout for this high-risk group.

RESULTS

The new method paired with CEPAC demonstrated excellent consistency with the HOPE model (ICC = 0.98 for MSM and 0.99 for PWID). With only the individual benefit of the intervention incorporated, a PrEP program for MSM in India averted 43,000 transmissions over a 5-year period and resulted in a lifetime incremental cost-effectiveness ratio (ICER) of US$2,300/year-of-life saved (YLS) compared to the status quo. After applying both the direct (individual) and indirect (community) benefits, PrEP averted 86,000 transmissions over the same period and resulted in an ICER of US$600/YLS.

CONCLUSIONS

Our method enables HIV microsimulation models that evaluate clinical and economic outcomes of HIV interventions to estimate the community benefit of these interventions (in terms of survival gains and cost savings) efficiently and without sacrificing clinical detail. This method addresses an important methodological gap in health economics microsimulation modeling and allows decision scientists to make more accurate policy recommendations.

VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota

MOTIVATION

The resistance of bacterial pathogens to antibiotics is one of the most important issues of modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microbiota by means of horizontal gene transfer. Thus, it is important to develop methods of prediction and monitoring of antibiotics resistance in human populations.

RESULTS

We present the agent-based VERA model, which allows simulation of the spread of pathogens, including the possible horizontal transfer of resistance determinants from a commensal microbiota community. The model considers the opportunity of residents to stay in the town or in a medical institution, have incorrect self-treatment, treatment with several antibiotics types and transfer and accumulation of resistance determinants from commensal microorganism to a pathogen. In this model, we have also created an assessment of optimum observation frequency of infection spread among the population. Investigating model behavior, we show a number of non-linear dependencies, including the exponential nature of the dependence of the total number of those infected on the average resistance of a pathogen. As the model infection, we chose infection with Shigella spp., though it could be applied to a wide range of other pathogens.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a rapidly emerging disease that has been classified a pandemic by the World Health Organization (WHO). To support WHO with their recommendations on quarantine, we conducted a rapid review on the effectiveness of quarantine during severe coronavirus outbreaks.

OBJECTIVES

We conducted a rapid review to assess the effects of quarantine (alone or in combination with other measures) of individuals who had contact with confirmed cases of COVID-19, who travelled from countries with a declared outbreak, or who live in regions with high transmission of the disease.

SEARCH METHODS

An information specialist searched PubMed, Ovid MEDLINE, WHO Global Index Medicus, Embase, and CINAHL on 12 February 2020 and updated the search on 12 March 2020. WHO provided records from daily searches in Chinese databases up to 16 March 2020.

SELECTION CRITERIA

Cohort studies, case-control-studies, case series, time series, interrupted time series, and mathematical modelling studies that assessed the effect of any type of quarantine to control COVID-19. We also included studies on SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) as indirect evidence for the current coronavirus outbreak.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened 30% of records; a single review author screened the remaining 70%. Two review authors screened all potentially relevant full-text publications independently. One review author extracted data and assessed evidence quality with GRADE and a second review author checked the assessment. We rated the certainty of evidence for the four primary outcomes: incidence, onward transmission, mortality, and resource use.

MAIN RESULTS

We included 29 studies; 10 modelling studies on COVID-19, four observational studies and 15 modelling studies on SARS and MERS. Because of the diverse methods of measurement and analysis across the outcomes of interest, we could not conduct a meta-analysis and conducted a narrative synthesis. Due to the type of evidence found for this review, GRADE rates the certainty of the evidence as low to very low. Modeling studies consistently reported a benefit of the simulated quarantine measures, for example, quarantine of people exposed to confirmed or suspected cases averted 44% to 81% incident cases and 31% to 63% of deaths compared to no measures based on different scenarios (incident cases: 4 modelling studies on COVID-19, SARS; mortality: 2 modelling studies on COVID-19, SARS, low-certainty evidence). Very low-certainty evidence suggests that the earlier quarantine measures are implemented, the greater the cost savings (2 modelling studies on SARS). Very low-certainty evidence indicated that the effect of quarantine of travellers from a country with a declared outbreak on reducing incidence and deaths was small (2 modelling studies on SARS). When the models combined quarantine with other prevention and control measures, including school closures, travel restrictions and social distancing, the models demonstrated a larger effect on the reduction of new cases, transmissions and deaths than individual measures alone (incident cases: 4 modelling studies on COVID-19; onward transmission: 2 modelling studies on COVID-19; mortality: 2 modelling studies on COVID-19; low-certainty evidence). Studies on SARS and MERS were consistent with findings from the studies on COVID-19.

AUTHORS' CONCLUSIONS

Current evidence for COVID-19 is limited to modelling studies that make parameter assumptions based on the current, fragmented knowledge. Findings consistently indicate that quarantine is important in reducing incidence and mortality during the COVID-19 pandemic. Early implementation of quarantine and combining quarantine with other public health measures is important to ensure effectiveness. In order to maintain the best possible balance of measures, decision makers must constantly monitor the outbreak situation and the impact of the measures implemented. Testing in representative samples in different settings could help assess the true prevalence of infection, and would reduce uncertainty of modelling assumptions. This review was commissioned by WHO and supported by Danube-University-Krems.

Reporting Quality of Discrete Event Simulations in Healthcare-Results From a Generic Reporting Checklist

OBJECTIVES

The aims of this study were to formulate a generic reporting checklist for healthcare-related discrete event simulation (DES) studies and to critically appraise the existing studies.

METHODS

Based on the principles of accessibility and generality, assessment items were derived from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR)-Society for Medical Decision Making (SMDM) Task Force reports. The resulting checklist was applied to all 211 DES studies identified in a previous review. The proportion of fulfilled checklist items served as an indicator of reporting quality. A logistic regression was conducted to investigate whether study characteristics (eg, publication before or after the publication of the ISPOR-SMDM reports) increased the likelihood of fulfilling more than the mean number of items fulfilled by the appraised DES studies.

RESULTS

An 18-item checklist was formulated covering model conceptualization, parameterization and uncertainty assessment, validation, generalizability, and stakeholder involvement. The reporting quality of the DES models fluctuated around the mean of 63.7% (SD 11.0%) over the period studied. A modest nonsignificant improvement in reporting quality was found after the publication of the ISPOR-SMDM reports (64.5% vs 62.9%). Items with the lowest performance were related to predictive validation (2.8% of studies), cross validation (8.5%), face validity assessment (26.5%), and stakeholder involvement (27.5%). Models applied to health economic evaluation (HEE), country under study, and industry sponsorship were significantly associated with the odds of achieving above-average reporting quality.

CONCLUSIONS

The checklist is applicable across various model-based analyses beyond HEEs. Adherence to the ISPOR-SMDM guidelines should be improved, particularly regarding model validation.

Lifetime patient outcomes and healthcare utilization for Bronchopulmonary dysplasia (BPD) and extreme preterm infants: a microsimulation study

BACKGROUND

Bronchopulmonary dysplasia (BPD) is among the most severe chronic lung diseases and predominantly affects premature infants. There is a general understanding of BPD's significant impact on the short-term outcomes however there is little evidence on long-term outcomes. Our study estimates the lifetime clinical outcomes, quality of life, and healthcare costs of BPD and associated complications.

METHODS

We developed a microsimulation model to estimate lifetime clinical and economic burden of BPD among extreme preterm infants (≤28 weeks gestational age at birth) and validated it against the best available Canadian data. We further estimate the cumulative incidence of major complications associated with BPD, differentiated by BPD severity and gestational age category.

RESULTS

We find, on average, patients with BPD and resulting complications will incur over CAD$700,000 in lifetime health systems costs. We also find the average life expectancy of BPD patients to be moderately less than that of the general population and significant reductions in quality-adjusted life year due to major complications. Healthcare utilization and quality of life measures vary dramatically according to BPD severity, suggesting significant therapeutic headroom for interventions that can prevent or mitigate the effects of BPD for patients.

CONCLUSIONS

Our study adds a significant expansion of existing evidence by presenting the lifetime burden of BPD based on key patient characteristics. Given the extreme cost burden at the earliest stage of life and lifetime negative impact on quality of life, there is larger headroom for investment in prevention and mitigation of severe BPD than is currently available.

Reducing overtreatment associated with overdiagnosis in cervical cancer screening-A model-based benefit-harm analysis for Austria

A general concern exists that cervical cancer screening using human papillomavirus (HPV) testing may lead to considerable overtreatment. We evaluated the trade-off between benefits and overtreatment among different screening strategies differing by primary tests (cytology, p16/Ki-67, HPV alone or in combinations), interval, age and diagnostic follow-up algorithms. A Markov state-transition model calibrated to the Austrian epidemiological context was used to predict cervical cancer cases, deaths, overtreatments and incremental harm-benefit ratios (IHBR) for each strategy. When considering the same screening interval, HPV-based screening strategies were more effective compared to cytology or p16/Ki-67 testing (e.g., relative reduction in cervical cancer with biennial screening: 67.7% for HPV + Pap cotesting, 57.3% for cytology and 65.5% for p16/Ki-67), but were associated with increased overtreatment (e.g., 19.8% more conizations with biennial HPV + Papcotesting vs. biennial cytology). The IHBRs measured in unnecessary conizations per additional prevented cancer-related death were 31 (quinquennial Pap + p16/Ki-67-triage), 49 (triennial Pap + p16/Ki-67-triage), 58 (triennial HPV + Pap cotesting), 66 (biennial HPV + Pap cotesting), 189 (annual Pap + p16/Ki-67-triage) and 401 (annual p16/Ki-67 testing alone). The IHBRs increased significantly with increasing screening adherence rates and slightly with lower age at screening initiation, with a reduction in HPV incidence or with lower Pap-test sensitivity. Depending on the accepted IHBR threshold, biennial or triennial HPV-based screening in women as of age 30 and biennial cytology in younger women may be considered in opportunistic screening settings with low or moderate adherence such as in Austria. In organized settings with high screening adherence and in postvaccination settings with lower HPV prevalence, the interval may be prolonged.

HPV-FRAME: A consensus statement and quality framework for modelled evaluations of HPV-related cancer control

Intense research activity in HPV modelling over this decade has prompted the development of additional guidelines to those for general modelling. A specific framework is required to address different policy questions and unique complexities of HPV modelling. HPV-FRAME is an initiative to develop a consensus statement and quality-based framework for epidemiologic and economic HPV models. Its development involved an established process. Reporting standards have been structured according to seven domains reflecting distinct policy questions in HPV and cancer prevention and categorised by relevance to a population or evaluation. Population-relevant domains are: 1) HPV vaccination in pre-adolescent and young adolescent individuals; 2) HPV vaccination in older individuals; 3) targeted vaccination in men who have sex with men; 4) considerations for individuals living with HIV and 5) considerations for low- and middle-income countries. Additional considerations applicable to specific evaluations are: 6) cervical screening or integrated cervical screening and HPV vaccination approaches and 7) alternative vaccine types and alternative dosing schedules. HPV-FRAME aims to promote the development of models in accordance with an explicit framework, to better enable target audiences to understand a model's strength and weaknesses in relation to a specific policy question and ultimately improve the model's contribution to informed decision-making.

Quantifying Positive Health Externalities of Disease Control Interventions: Modeling Chikungunya and Dengue

Purpose. Health interventions can generate positive externalities not captured in traditional, single-disease cost-effectiveness analyses (CEAs), potentially biasing results. We illustrate this with the example of mosquito-borne diseases. When a particular mosquito species can transmit multiple diseases, a single-disease CEA comparing disease-specific interventions (e.g., vaccination) with interventions targeting the mosquito population (e.g., insecticide) would underestimate the insecticide's full benefits (i.e., preventing other diseases). Methods. We developed three dynamic transmission models: chikungunya, dengue, and combined chikungunya and dengue, each calibrated to disease-specific incidence and deaths in Colombia (June 2014 to December 2017). We compared the models' predictions of the incremental benefits and cost-effectiveness of an insecticide (10% efficacy), hypothetical chikungunya and dengue vaccines (40% coverage, 95% efficacy), and combinations of these interventions. Results. Model calibration yielded realistic parameters that produced close matches to disease-specific incidence and deaths. The chikungunya model predicted that vaccine would decrease the incidence of chikungunya and avert more total deaths than insecticide. The dengue model predicted that insecticide and the dengue vaccine would reduce dengue incidence and deaths, with no effect for the chikungunya vaccine. In the combined model, insecticide was more effective than either vaccine in reducing the incidence of and deaths from both diseases. In all models, the combined strategy was at least as effective as the most effective single strategy. In an illustrative CEA, the most frequently preferred strategy was vaccine in the chikungunya model, the status quo in the dengue model, and insecticide in the combined model. Limitations. There is uncertainty in the target calibration data. Conclusions. Failure to capture positive externalities can bias CEA results, especially when evaluating interventions that affect multiple diseases. Multidisease modeling is a reasonable alternative for addressing such biases.