Modeling for health care and other policy decisions: uses, roles, and validity

The potential impact of policies and structural interventions in reducing cardiovascular disease and mortality: a systematic review of simulation-based studies

Background

The aim of the study was to investigate the potential effect of different structural interventions for preventing cardiovascular disease.

Methods

Medline and EMBASE were searched for peer-reviewed simulation-based studies of structural interventions for prevention of cardiovascular disease. We performed a systematic narrative synthesis.

Results

A total of 54 studies met the inclusion criteria. Diet, nutrition, tobacco and alcohol control and other programmes are among the policy simulation models explored. Food tax and subsidies, healthy food and lifestyles policies, palm oil tax, processed meat tax, reduction in ultra-processed foods, supplementary nutrition assistance programmes, stricter food policy and subsidised community-supported agriculture were among the diet and nutrition initiatives. Initiatives to reduce tobacco and alcohol use included a smoking ban, a national tobacco control initiative and a tax on alcohol. Others included the NHS Health Check, WHO 25 × 25 and air quality management policy.

Future work and limitations

There is significant heterogeneity in simulation models, making comparisons of output data impossible. While policy interventions typically include a variety of strategies, none of the models considered possible interrelationships between multiple policies or potential interactions. Research that investigates dose-response interactions between numerous modifications as well as longer-term clinical outcomes can help us better understand the potential impact of policy-level interventions.

Conclusions

The reviewed studies underscore the potential of structural interventions in addressing cardiovascular diseases. Notably, interventions in areas such as diet, tobacco, and alcohol control demonstrate a prospective decrease in cardiovascular incidents. However, to realize the full potential of such interventions, there is a pressing need for models that consider the interplay and cumulative impacts of multiple policies. Rigorous research into holistic and interconnected interventions will pave the way for more effective policy strategies in the future.

Study registration

The study is registered as PROSPERO CRD42019154836.

Funding

This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 17/148/05.

Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization

INTRODUCTION

In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures.

METHODOLOGY

In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO.

RESULTS

The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests.

CONCLUSIONS

The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.

Mortality and Life Expectancy Lost in Canada Attributable to Dietary Patterns: Evidence From Canadian National Nutrition Survey Linked to Routinely Collected Health Administrative Databases

Using 5 diet quality indexes, we estimated the mortality and life expectancy lost, at the national level, attributable to poor dietary patterns, which had previously been largely unknown. We used the Canadian Community Health Survey 2004, linked to vital statistics (n = 16,212 adults; representing n = 22,898,880). After a median follow-up of 7.5 years, 1,722 deaths were recorded. Population attributable fractions were calculated to estimate the mortality burden of poor dietary patterns (Dietary Guidelines for Americans Adherence Index 2015, Dietary Approaches to Stop Hypertension, Healthy Eating Index, Alternative Healthy Eating Index, and Mediterranean Style Dietary Pattern Score). Better diet quality was associated with a 32%-51% and 21%-43% reduction in all-cause mortality among adults aged 45-80 years and ≥20 years, respectively. Projected life expectancy at 45 years was longer for Canadians adhering to a healthy dietary pattern (average of 5.2-8.0 years (men) and 1.6-4.1 (women)). At the population level, 26.5%-38.9% (men) and 8.9%-22.9% (women) of deaths were attributable to poor dietary patterns. Survival benefit was greater for individuals with higher scores on all diet indexes, even with relatively small intake differences. The large attributable burden was likely from assessing overall dietary patterns instead of a limited range of foods and nutrients.

Models for estimating and projecting global, regional and national prevalence and disease burden of asthma: a systematic review

Background

Statistical models are increasingly being used to estimate and project the prevalence and burden of asthma. Given substantial variations in these estimates, there is a need to critically assess the properties of these models and assess their transparency and reproducibility. We aimed to critically appraise the strengths, limitations and reproducibility of existing models for estimating and projecting the global, regional and national prevalence and burden of asthma.

Methods

We undertook a systematic review, which involved searching Medline, Embase, World Health Organization Library and Information Services (WHOLIS) and Web of Science from 1980 to 2017 for modelling studies. Two reviewers independently assessed the eligibility of studies for inclusion and then assessed their strengths, limitations and reproducibility using pre-defined quality criteria. Data were descriptively and narratively synthesised.

Results

We identified 108 eligible studies, which employed a total of 51 models: 42 models were used to derive national level estimates, two models for regional estimates, four models for global and regional estimates and three models for global, regional and national estimates. Ten models were used to estimate the prevalence of asthma, 27 models estimated the burden of asthma – including, health care service utilisation, disability-adjusted life years, mortality and direct and indirect costs of asthma – and 14 models estimated both the prevalence and burden of asthma. Logistic and linear regression models were most widely used for national estimates. Different versions of the DisMod-MR- Bayesian meta-regression models and Cause Of Death Ensemble model (CODEm) were predominantly used for global, regional and national estimates. Most models suffered from a number of methodological limitations – in particular, poor reporting, insufficient quality and lack of reproducibility.

Conclusions

Whilst global, regional and national estimates of asthma prevalence and burden continue to inform health policy and investment decisions on asthma, most models used to derive these estimates lack the required reproducibility. There is a need for better-constructed models for estimating and projecting the prevalence and disease burden of asthma and a related need for better reporting of models, and making data and code available to facilitate replication.

Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review

OBJECTIVE

This study sought to assess the impact and validity of simulation modeling in informing decision making in a complex area of healthcare delivery: colorectal cancer (CRC) screening.

MATERIALS AND METHODS

We searched 10 electronic databases for English-language articles published between January 1, 2008, and March 1, 2019, that described the development of a simulation model with a focus on average-risk CRC screening delivery. Included articles were reviewed for evidence that the model was validated, and provided real or potential contribution to informed decision making using the GRADE EtD (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision) framework.

RESULTS

A total of 43 studies met criteria. The majority used Markov modeling (n = 31 [72%]) and sought to determine cost-effectiveness, compare screening modalities, or assess effectiveness of screening. No study reported full model validation and only (58%) reported conducting any validation. Majority of models were developed to address a specific health systems or policy question; few articles report the model's impact on this decision (n = 39 [91%] vs. n = 5 [12%]). Overall, models provided evidence relevant to every element important to decision makers as outlined in the GRADE EtD framework.

DISCUSSION AND CONCLUSION

Simulation modeling contributes evidence that is considered valuable to decision making in CRC screening delivery, particularly in assessing cost-effectiveness and comparing screening modalities. However, the actual impact on decisions and validity of models is lacking in the literature. Greater validity testing, impact assessment, and standardized reporting of both is needed to understand and demonstrate the reliability and utility of simulation modeling.

Use of Simulation Modeling to Inform Decision Making for Health Care Systems and Policy in Colorectal Cancer Screening: Protocol for a Systematic Review

Background

Simulation modeling has frequently been used to assess interventions in complex aspects of health care, such as colorectal cancer (CRC) screening, where clinical trials are not feasible. Simulation models provide estimates of outcomes, unintended consequences, and costs of an intervention; thus offering an invaluable decision aid for policy makers and health care leaders. However, the contribution that simulation models have made to policy and health system decisions is unknown.

Objective

This study aims to assess if simulation modeling has supported evidence-informed decision making in CRC screening.

Methods

A preliminary literature search and pilot screening of 100 references were conducted by three independent reviewers to define and refine the inclusion criteria of this systematic review. Using the developed inclusion criteria, a search of the academic and gray literature published between January 1, 2008, and March 1, 2019, will be conducted to identify studies that developed a simulation model focusing on the delivery of CRC screening of average-risk individuals. The three independent reviewers will assess the validation process and the extent to which the study contributed evidence toward informed decision making (both reported and potential). Validation will be assessed based on adherence to the best practice recommendations described by the International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM). Criteria for potential contribution to decision making will be defined as outlined in the internationally recognized Grading of Recommendations Assessment, Development and Evaluation Evidence to Decision (GRADE EtD) framework. These criteria outline information that the health system and policy decision makers should consider when making an evidence-informed decision including an intervention’s resource utilization, cost-effectiveness, impact on health equity, and feasibility. Subgroup analysis of articles based on their GRADE EtD criteria will be conducted to identify methods associated with decision support capacity (ie, participatory, quantitative, or mixed methods).

Results

A database search of the literature yielded 484 references to screen for inclusion in the systematic review. We anticipate that this systematic review will provide an insight into the contribution of simulation modeling methods to informed decision making in CRC screening delivery and discuss methods that may be associated with a stronger impact on decision making. The project was funded in May 2019. Data collection took place from January 2008 to March 2019. Data analysis was completed in November 2019, and are expected to be published in spring 2020.

Conclusions

Our findings will help guide researchers and health care leaders to mobilize the potential for simulation modeling to inform evidence-informed decisions in CRC screening delivery. The methods of this study may also be replicated to assess the utility of simulation modeling in other areas of complex health care decision making.

International Registered Report Identifier (IRRID)

DERR1-10.2196/16103

Trial Registration

PROSPERO no. 130823; https://www.crd.york.ac.uk/PROSPERO

Patterns of concomitant use of Ayurveda and conventional anti-diabetic formulations - Experiences at a tertiary care Ayurveda hospital, India

Background:

Majority of the population relies on traditional medicines for many of their health related problems. Particularly individuals with chronic illness like diabetes mellitus (DM) are more likely to simultaneously use herbal medicines. Many of such users believe that traditional medicines are natural and therefore safe, but this is a dangerous over simplification. Some herbal medicines may be associated with adverse effects, which include interactions with prescribed drugs. Information on such concomitant use of anti-diabetic drugs along with Ayurveda medicines is limited in Indian scenario.

Aims and objectives:

To survey the patterns of concomitant use of Ayurveda and conventional anti-diabetic drugs by diabetic patients attending an out-patient department of a tertiary care teaching hospital in New Delhi, India through a validated questionnaire.

Materials and methods:

This is a questionnaire-based survey, carried out after the approval of the Institutional Ethics Committee, subsequently registered at CTRI. A questionnaire to assess the pattern of concomitant use was developed; content was validated and pre-tested by a pilot study in 40 patients, further refined and used in the survey. The data was analyzed to evaluate the patterns of concomitant use of Ayurvedic and conventional anti-diabetic drugs.

Results:

About 95.9% of diabetic patients were taking herbo-mineral formulations concomitantly with conventional anti-diabetic drugs. Although 45.3% of diabetics were using Ayurveda interventions under the supervision of qualified AYUSH physicians, remaining involved in procuring the drugs over the counter (OTC) or from the local vendors. In majority of these instances, the use of Ayurveda formulations was not communicated with their physicians.

Conclusion:

The observations reveal that a majority of the diabetics (95.9%) were taking one or the other form of herbal preparations along with their conventional anti-diabetic drugs and about 44% among them were using these concomitantly. Thus, generating awareness on good practices of drug use seems to be essential.

Cost-Effectiveness Analysis of Multifocal Intraocular Lenses Compared to Monofocal Intraocular Lenses in Cataract Surgery

PURPOSE

To determine the cost-effectiveness of multifocal intraocular lenses (IOLs) compared to that of monofocal IOLs from a societal and health care sector perspective.

DESIGN

Cost-effectiveness analysis.

METHODS

A Markov model was constructed that simulated patients who received either multifocal or monofocal IOLs during cataract surgery. Postoperatively, patients could experience spectacle dependence, glare, and haloes. Cost-effectiveness was determined by measuring the incremental cost-effectiveness ratio (ICER) as the incremental cost in dollars per quality-adjusted life year (QALY) gained. Treatments with an ICER below the standard willingness-to-pay (WTP) threshold of $50,000/QALY were considered cost effective. One-way sensitivity analyses and probabilistic sensitivity analyses were used to evaluate model sensitivity to cost, utilities, and other model inputs.

RESULTS

Multifocal IOLs were associated with a 0.71 QALY increase at an increased cost of $3,415 compared with monofocal IOLs, leading to an ICER of $4,805/QALY from the societal and health care sector perspectives. The cost-effectiveness model was most sensitive to patient age, probability of spectacle dependence with multifocal IOLs and monofocal IOLs, and the disutility of glasses. Probabilistic sensitivity analysis found multifocal IOLs to be the cost-effective option compared with monofocal IOLs 99.9% of the time at a WTP threshold of $50,000/QALY.

CONCLUSIONS

From a societal and health care perspective, multifocal IOLs would be considered a cost-effective strategy compared to monofocal IOLs for patients who desire a higher chance to be spectacle-free. However, more studies need to be conducted to further evaluate the efficacy of multifocal IOLs.

Identification of risk factors of diabetes mellitus in bank employees of selected banks in Ndola town

BACKGROUND

Diabetes mellitus, a lifelong disease is achieving pandemic proportions. The prevalence of diabetes is on the rise and is expected to be worlds 7th leading cause of death by 2030. Studies have associated a number of risk factors like obesity, lack of physical activity, sedentarism, diet, and stress to diabetes mellitus. The job of bank employees is both sedentary in nature and involves high levels of stress. These people spend almost all their working hours seated as they carry out their work. With this background, the primary objective study of this project is to identify risk factors of diabetes mellitus among bank employees of selected banks in Ndola town. This study also aims to find the prevalence of diabetes among bank employees in the selected nine banks.

METHODS

A cross-section study was conducted on 121 bank employees from nine (9) selected banks of Ndola town center. Information about their biosocial characteristics, their weight, height, blood pressure, fasting plasma glucose was measured and recorded and a questionnaire on physical activity of participants was administered. Data was analyzed by SPSS 20.0 for Windows. Univariate, bivariate and multivariate analysis were conducted to ascertain any correlation between the dependent variables and independent variables.

RESULTS

Prevalence of diabetes mellitus was found to be 15%. The risk of developing Diabetes mellitus was high in obese participants (OR 5.1 [cl95%] p = 0.000). And in physical inactive participants it was also high (OR 7.6 [cl95%] p = 0.046).

CONCLUSION

and recommendations: Diabetes mellitus shows significant correlation with physical inactivity, body mass index, blood pressure, age and sex. Findings in this study support the need for programs to promote employee health, to help prevent and monitor the enormity and temporal trends of these factor as well as asses the actions that are directed toward this population group.

Evidence for optimal HIV screening and testing intervals in HIV-negative individuals from various risk groups: A systematic review

Background

Human immunodeficiency virus (HIV) testing plays a crucial role in Canada's HIV prevention and treatment efforts and is the first step to achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets; however, how often Canadians, including populations at increased risk of HIV exposure, should be tested is unclear. We conducted a systematic literature review to determine the optimal HIV screening and testing intervals.

Objective

To examine the current evidence on HIV testing intervals in HIV-negative individuals from various risk groups and to assess the potential harms and patients' values and preferences associated with different testing frequencies.

Methods

We searched MEDLINE/PubMed, Scopus, Embase, the Cochrane Library, PsychINFO and EconLit for studies on different frequencies of HIV testing published between January 2000 and September 2016. An additional search was conducted for grey literature published between January 2000 and October 2016. Data extraction included study characteristics, participants, exposure, outcomes and economic variables. The quality of the studies was assessed and results summarized.

Results

Of the 2,702 articles identified from the searches, 27 met the inclusion criteria for review. This included assessments of HIV testing intervals among the general population, men who have sex with men, people who use injection drugs and sex workers. Optimal testing intervals across risk groups ranged from one-time testing to every three months. Data from modelling studies may not be representative of the Canadian context. Few studies identified potential harms of increased screening, specifically an increase in both false positive and false negative results. There were only two studies that addressed patient values and preferences concerning HIV screening, which suggested that the majority of participants were amenable to routine screening through their primary care provider.

Conclusion

There was insufficient evidence to support optimal HIV screening and testing intervals for different populations. Context-specific factors, such as budget allocation, human resources, local epidemiology, socioeconomic factors and risk behaviours, along with clinical judgement, inform whom and how often to screen, suggesting the need for research specific to Canada. Research on patient preferences as well as the benefits and harms of more frequent screening are also indicated.

Initial validation of a simulation model for estimating the impact of serogroup A Neisseria meningitidis vaccination in the African meningitis belt

We previously developed a mathematical simulation of serogroup A Neisseria meningitidis (NmA) transmission in Burkina Faso, with the goal of forecasting the relative benefit of different vaccination programs. Here, we revisit key structural assumptions of the model by comparing how accurately the different assumptions reproduce observed NmA trends following vaccine introduction. A priori, we updated several of the model's parameters based on recently published studies. We simulated NmA disease under different assumptions about duration of vaccine-induced protection (including the possibility that vaccine-induced protection may last longer than natural immunity). We compared simulated and observed case counts from 2011-2017. We then used the best-fit model to forecast the impact of different vaccination strategies. Our updated model, with the assumption that vaccine-induced immunity lasts longer than immunity following NmA colonization, was able to reproduce observed trends in NmA disease. The updated model predicts that, following a mass campaign among persons 1-29 years of age, either routine immunization of 9 month-old children or periodic mini-campaigns among children 1-4 years of age will lead to sustained control of epidemic NmA in Burkina Faso. This validated model can help public health officials set policies for meningococcal vaccination in Africa.

Simulation modeling to enhance population health intervention research for chronic disease prevention

Population Health Intervention Research (PHIR) is an expanding field that explores the health effects of population-level interventions conducted within and outside of the health sector. Simulation modeling-the use of mathematical models to predict health outcomes in populations given a set of specified inputs-is a useful, yet underutilized tool for PHIR. It can be employed at several phases of the research process: (1) planning and designing PHIR studies; (2) implementation; and (3) knowledge translation of findings across settings and populations. Using the example of community-wide, built environment interventions for the prevention of type 2 diabetes, we demonstrate how simulation models can be a powerful technique for chronic disease prevention research within PHIR. With increasingly available data on chronic disease risk factors and outcomes, the use of simulation modeling in PHIR for chronic disease prevention is anticipated to grow. There is a continued need to ensure models are appropriately validated and researchers should be cautious in their interpretation of model outputs given the uncertainties that are inherent with simulation modeling approaches. However, given the complexity of disease pathways and methodological challenges of PHIR studies, simulation models can be a valuable tool for researchers studying population interventions that hold the potential to improve health and reduce health inequities.

Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections

Background

Spreadsheet software is increasingly used to implement systems science models informing health policy decisions, both in academia and in practice where technical capacity may be limited. However, spreadsheet models are prone to unintentional errors that may not always be identified using standard error-checking techniques. Our objective was to illustrate, through a methodologic case study analysis, the impact of unintentional errors on model projections by implementing parallel model versions.

Methods

We leveraged a real-world need to revise an existing spreadsheet model designed to inform HIV policy. We developed three parallel versions of a previously validated spreadsheet-based model; versions differed by the spreadsheet cell-referencing approach (named single cells; column/row references; named matrices). For each version, we implemented three model revisions (re-entry into care; guideline-concordant treatment initiation; immediate treatment initiation). After standard error-checking, we identified unintentional errors by comparing model output across the three versions. Concordant model output across all versions was considered error-free. We calculated the impact of unintentional errors as the percentage difference in model projections between model versions with and without unintentional errors, using +/-5% difference to define a material error.

Results

We identified 58 original and 4,331 propagated unintentional errors across all model versions and revisions. Over 40% (24/58) of original unintentional errors occurred in the column/row reference model version; most (23/24) were due to incorrect cell references. Overall, >20% of model spreadsheet cells had material unintentional errors. When examining error impact along the HIV care continuum, the percentage difference between versions with and without unintentional errors ranged from +3% to +16% (named single cells), +26% to +76% (column/row reference), and 0% (named matrices).

Conclusions

Standard error-checking techniques may not identify all errors in spreadsheet-based models. Comparing parallel model versions can aid in identifying unintentional errors and promoting reliable model projections, particularly when resources are limited.

Cost-Effectiveness in Global Surgery: Pearls, Pitfalls, and a Checklist

INTRODUCTION

Cost-effectiveness analysis can be a powerful policy-making tool. In the two decades since the first cost-effectiveness analyses in global surgery, the methodology has established the cost-effectiveness of many types of surgery in low- and middle-income countries (LMICs). However, with the crescendo of cost-effectiveness analyses in global surgery has come vast disparities in methodology, with only 15% of studies adhering to published guidelines. This has led to results that have varied up to 150-fold.

METHODS

The theoretical basis, common pitfalls, and guidelines-based recommendations for cost-effectiveness analyses are reviewed, and a checklist to be used for cost-effectiveness analyses in global surgery is created.

RESULTS

Common pitfalls in global surgery cost-effectiveness analyses fall into five categories: the analytic perspective, cost measurement, effectiveness measurement, probability estimation, valuation of the counterfactual, and heterogeneity and uncertainty. These are reviewed in turn, and a checklist to avoid these pitfalls is developed.

CONCLUSION

Cost-effectiveness analyses, when done rigorously, can be very useful for the development of efficient surgical systems in LMICs. This review highlights the common pitfalls in these analyses and methods to avoid these pitfalls.

The OncoSim model: development and use for better decision-making in Canadian cancer control

The Canadian Partnership Against Cancer was created in 2007 by the federal government to accelerate cancer control across Canada. Its OncoSim microsimulation model platform, which consists of a suite of specific cancer models, was conceived as a tool to augment conventional resources for population-level policy- and decision-making. The Canadian Partnership Against Cancer manages the OncoSim program, with funding from Health Canada and model development by Statistics Canada. Microsimulation modelling allows for the detailed capture of population heterogeneity and health and demographic history over time. Extensive data from multiple Canadian sources were used as inputs or to validate the model. OncoSim has been validated through expert consultation; assessments of face validity, internal validity, and external validity; and model fit against observed data. The platform comprises three in-depth cancer models (lung, colorectal, cervical), with another in-depth model (breast) and a generalized model (25 cancers) being in development. Unique among models of its class, OncoSim is available online for public sector use free of charge. Users can customize input values and output display, and extensive user support is provided. OncoSim has been used to support decision-making at the national and jurisdictional levels. Although simulation studies are generally not included in hierarchies of evidence, they are integral to informing cancer control policy when clinical studies are not feasible. OncoSim can evaluate complex intervention scenarios for multiple cancers. Canadian decision-makers thus have a powerful tool to assess the costs, benefits, cost-effectiveness, and budgetary effects of cancer control interventions when faced with difficult choices for improvements in population health and resource allocation.

Developing WHO guidelines: Time to formally include evidence from mathematical modelling studies

In recent years, the number of mathematical modelling studies has increased steeply. Many of the questions addressed in these studies are relevant to the development of World Health Organization (WHO) guidelines, but modelling studies are rarely formally included as part of the body of evidence. An expert consultation hosted by WHO, a survey of modellers and users of modelling studies, and literature reviews informed the development of recommendations on when and how to incorporate the results of modelling studies into WHO guidelines. In this article, we argue that modelling studies should routinely be considered in the process of developing WHO guidelines, but particularly in the evaluation of public health programmes, long-term effectiveness or comparative effectiveness.  There should be a systematic and transparent approach to identifying relevant published models, and to commissioning new models.  We believe that the inclusion of evidence from modelling studies into the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process is possible and desirable, with relatively few adaptations.  No single "one-size-fits-all" approach is appropriate to assess the quality of modelling studies. The concept of the 'credibility' of the model, which takes the conceptualization of the problem, model structure, input data, different dimensions of uncertainty, as well as transparency and validation into account, is more appropriate than 'risk of bias'.

Developing WHO guidelines: Time to formally include evidence from mathematical modelling studies

In recent years, the number of mathematical modelling studies has increased steeply. Many of the questions addressed in these studies are relevant to the development of World Health Organization (WHO) guidelines, but modelling studies are rarely formally included as part of the body of evidence. An expert consultation hosted by WHO, a survey of modellers and users of modelling studies, and literature reviews informed the development of recommendations on when and how to incorporate the results of modelling studies into WHO guidelines. In this article, we argue that modelling studies should routinely be considered in the process of developing WHO guidelines, but particularly in the evaluation of public health programmes, long-term effectiveness or comparative effectiveness.  There should be a systematic and transparent approach to identifying relevant published models, and to commissioning new models.  We believe that the inclusion of evidence from modelling studies into the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process is possible and desirable, with relatively few adaptations.  No single "one-size-fits-all" approach is appropriate to assess the quality of modelling studies. The concept of the 'credibility' of the model, which takes the conceptualization of the problem, model structure, input data, different dimensions of uncertainty, as well as transparency and validation into account, is more appropriate than 'risk of bias'.

Models for estimating and projecting global, regional and national prevalence and disease burden of asthma: protocol for a systematic review

INTRODUCTION

Models that have so far been used to estimate and project the prevalence and disease burden of asthma are in most cases inadequately described and irreproducible. We aim systematically to describe and critique the existing models in relation to their strengths, limitations and reproducibility, and to determine the appropriate models for estimating and projecting the prevalence and disease burden of asthma.

METHODS

We will search the following electronic databases to identify relevant literature published from 1980 to 2017: Medline, Embase, WHO Library and Information Services and Web of Science Core Collection. We will identify additional studies by searching the reference list of all the retrieved papers and contacting experts. We will include observational studies that used models for estimating and/or projecting prevalence and disease burden of asthma regarding human population of any age and sex. Two independent reviewers will assess the studies for inclusion and extract data from included papers. Data items will include authors' names, publication year, study aims, data source and time period, study population, asthma outcomes, study methodology, model type, model settings, study variables, methods of model derivation, methods of parameter estimation and/or projection, model fit information, key findings and identified research gaps. A detailed critical narrative synthesis of the models will be undertaken in relation to their strengths, limitations and reproducibility. A quality assessment checklist and scoring framework will be used to determine the appropriate models for estimating and projecting the prevalence anddiseaseburden of asthma.

ETHICS AND DISSEMINATION

We will not collect any primary data for this review, and hence there is no need for formal National Health Services Research Ethics Committee approval. We will present our findings at scientific conferences and publish the findings in the peer-reviewed scientific journal.

Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between?

Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved.

Methods to construct a step-by-step beginner's guide to decision analytic cost-effectiveness modeling

BACKGROUND

Although guidance on good research practice in health economic modeling is widely available, there is still a need for a simpler instructive resource which could guide a beginner modeler alongside modeling for the first time.

AIM

To develop a beginner's guide to be used as a handheld guide contemporaneous to the model development process.

METHODS

A systematic review of best practice guidelines was used to construct a framework of steps undertaken during the model development process. Focused methods review supplemented this framework. Consensus was obtained among a group of model developers to review and finalize the content of the preliminary beginner's guide. The final beginner's guide was used to develop cost-effectiveness models.

RESULTS

Thirty-two best practice guidelines were data extracted, synthesized, and critically evaluated to identify steps for model development, which formed a framework for the beginner's guide. Within five phases of model development, eight broad submethods were identified and 19 methodological reviews were conducted to develop the content of the draft beginner's guide. Two rounds of consensus agreement were undertaken to reach agreement on the final beginner's guide. To assess fitness for purpose (ease of use and completeness), models were developed independently and by the researcher using the beginner's guide.

CONCLUSION

A combination of systematic review, methods reviews, consensus agreement, and validation was used to construct a step-by-step beginner's guide for developing decision analytical cost-effectiveness models. The final beginner's guide is a step-by-step resource to accompany the model development process from understanding the problem to be modeled, model conceptualization, model implementation, and model checking through to reporting of the model results.

Mathematical Modeling of the Transmission Dynamics of Clostridium difficile Infection and Colonization in Healthcare Settings: A Systematic Review

BACKGROUND

We conducted a systematic review of mathematical models of transmission dynamic of Clostridium difficile infection (CDI) in healthcare settings, to provide an overview of existing models and their assessment of different CDI control strategies.

METHODS

We searched MEDLINE, EMBASE and Web of Science up to February 3, 2016 for transmission-dynamic models of Clostridium difficile in healthcare settings. The models were compared based on their natural history representation of Clostridium difficile, which could include health states (S-E-A-I-R-D: Susceptible-Exposed-Asymptomatic-Infectious-Resistant-Deceased) and the possibility to include healthcare workers and visitors (vectors of transmission). Effectiveness of interventions was compared using the relative reduction (compared to no intervention or current practice) in outcomes such as incidence of colonization, CDI, CDI recurrence, CDI mortality, and length of stay.

RESULTS

Nine studies describing six different models met the inclusion criteria. Over time, the models have generally increased in complexity in terms of natural history and transmission dynamics and number/complexity of interventions/bundles of interventions examined. The models were categorized into four groups with respect to their natural history representation: S-A-I-R, S-E-A-I, S-A-I, and S-E-A-I-R-D. Seven studies examined the impact of CDI control strategies. Interventions aimed at controlling the transmission, lowering CDI vulnerability and reducing the risk of recurrence/mortality were predicted to reduce CDI incidence by 3-49%, 5-43% and 5-29%, respectively. Bundles of interventions were predicted to reduce CDI incidence by 14-84%.

CONCLUSIONS

Although CDI is a major public health problem, there are very few published transmission-dynamic models of Clostridium difficile. Published models vary substantially in the interventions examined, the outcome measures used and the representation of the natural history of Clostridium difficile, which make it difficult to synthesize results and provide a clear picture of optimal intervention strategies. Future modeling efforts should pay specific attention to calibration, structural uncertainties, and transparent reporting practices.

MODELING CHRONIC DISEASE PATIENT FLOWS DIVERTED FROM EMERGENCY DEPARTMENTS TO PATIENT-CENTERED MEDICAL HOMES

Chronic Disease is defined as a long lasting health condition, which can develop and/or worsen over an extended time, but which can also be controlled. The monetary and budgetary toll due to its persistent nature has become unsustainable and requires pressing actions to limit their incidence and burden. This paper demonstrates the utility of the System Dynamics approach to simulate the behavior of key factors involved in the implementation of chronic disease management. We model the patient flow diversion from emergency departments (ED) to patient-centered medical homes (PCMH), with emphasis on the visit rates, as well as the effect of insurance coverage, in an effort to assure continuity of quality care for Asthma patients at lower costs. The model is used as an evaluative method to identify conditions of a maintained health status through adequate policy planning, in terms of resources and capacity. This approach gives decision makers the ability to track the level of implementation of the intervention and generate knowledge about dynamics between population demands and the intervention effectiveness. The functionality of the model is demonstrated through the consideration of hypothetical scenarios executed using sensitivity analysis.

Simulating Strategies for Improving Control of Hypertension Among Patients with Usual Source of Care in the United States: The Blood Pressure Control Model

BACKGROUND

Only half of hypertensive adults achieve blood pressure (BP) control in the United States, and it is unclear how BP control rates may be improved most effectively and efficiently at the population level.

OBJECTIVE

We sought to compare the potential effects of system-wide isolated improvements in medication adherence, visit frequency, and higher physician prescription rate on achieving BP control at 52 weeks.

DESIGN

We developed a Markov microsimulation model of patient-level, physician-level, and system-level processes involved in controlling hypertension with medications. The model is informed by data from national surveys, cohort studies and trials, and was validated against two multicenter clinical trials (ALLHAT and VALUE).

SUBJECTS

We studied a simulated, nationally representative cohort of patients with diagnosed but uncontrolled hypertension with a usual source of care.

INTERVENTIONS

We simulated a base case and improvements of 10 and 50%, and an ideal scenario for three modifiable parameters: visit frequency, treatment intensification, and medication adherence. Ideal scenarios were defined as 100% for treatment intensification and adherence, and return visits occurring within 4 weeks of an elevated office systolic BP.

MAIN OUTCOME

BP control at 52 weeks of follow-up was examined.

RESULTS

Among 25,000 hypothetical adult patients with uncontrolled hypertension (systolic BP ≥ 140 mmHg), only 18% achieved BP control after 52 weeks using base-case assumptions. With 10/50%/idealized enhancements in each isolated parameter, enhanced treatment intensification achieved the greatest BP control (19/23/71%), compared with enhanced visit frequency (19/21/35%) and medication adherence (19/23/26%). When all three processes were idealized, the model predicted a BP control rate of 95% at 52 weeks.

CONCLUSION

Substantial improvements in BP control can only be achieved through major improvements in processes of care. Healthcare systems may achieve greater success by increasing the frequency of clinical encounters and improving physicians' prescribing behavior than by attempting to improve patient adherence to medications.

The Clinical Effectiveness and Cost-Effectiveness of Screening for Age-Related Macular Degeneration in Japan: A Markov Modeling Study

Objective

To investigate the cost-effectiveness of screening and subsequent intervention for age-related macular degeneration (AMD) in Japan.

Methods

The clinical effectiveness and cost-effectiveness of screening and subsequent intervention for AMD were assessed using a Markov model. The Markov model simulation began at the age of 40 years and concluded at the age of 90 years. The first-eye and second-eye combined model assumed an annual state-transition probability, development of prodromal symptoms, choroidal neovascularization (CNV), and reduction in visual acuity. Anti–vascular-endothelial-growth-factor (anti-VEGF) intravitreal injection therapy and photodynamic therapy (PDT) were performed to treat CNV. Intake of supplements was recommended to patients who had prodromal symptoms and unilateral AMD. Data on prevalence, morbidity, transition probability, utility value of each AMD patient, and treatment costs were obtained from published clinical reports.

Results

In the base-case analysis, screening for AMD every 5 years, beginning at the age of 50 years, showed a decrease of 41% in the total number of blind patients. The screening program reduced the incidence of blindness more than did the additional intake of supplements. However, the incremental cost-effectiveness ratio (ICER) of screening versus no screening was 27,486,352 Japanese yen (JPY), or 259,942 US dollars (USD) per quality-adjusted life year (QALY). In the sensitivity analysis, prodromal symptom-related factors for AMD had great impacts on the cost-effectiveness of screening. The lowest ICER obtained from the best scenario was 4,913,717 JPY (46,470 USD) per QALY, which was approximately equal to the willingness to pay in Japan.

Conclusions

Ophthalmologic screening for AMD in adults is highly effective in reducing the number of patients with blindness but not cost-effective as demonstrated by a Markov model based on clinical data from Japan.

Models for estimating projections for disease prevalence and burden: a systematic review focusing on chronic obstructive pulmonary disease

OBJECTIVE

Epidemiological models for estimating the prevalence and burden of disease inform health policy and service planning decisions. Our aim was to describe the challenges in evaluating such models using the example of epidemiological models for chronic obstructive pulmonary disease (COPD).

METHODS

Two reviewers searched Medline, Embase, CAB Abstracts and World Health Organization (WHO) Databases from 1980 to November 2013 for epidemiological models of COPD prevalence and burden. Two reviewers extracted data and assessed the quality of the studies. We then undertook a descriptive and narrative synthesis of data.

RESULTS

We identified 22 models employing a variety of techniques to calculate the prevalence and/or burden of COPD. Models calculated prevalence and/or mortality or other facet of disease burden using demographics and risk factors or trends, Markov-type modelling and microsimulation modelling. The six models which scored highly on the quality framework were: the Peabody model, which generated estimates of COPD prevalence; the WHO DISMOD II model which produced burden estimates in terms of disability adjusted life years with COPD and life years lost to COPD; the Atsou model which gave the life expectancy gains of individual smokers who quit smoking and associated costs; two Dutch COPD models which produced estimates of mortality and health care costs related to COPD; and the Pichon-Riviere model which gave the costs and cost effectiveness of smoking quit programmes.

CONCLUSIONS

The field of chronic disease modelling is burgeoning. As a result, policy makers need to understand how to interpret epidemiological models and their data sources.

Use and cumulation of evidence from modelling studies to inform policy on food taxes and subsidies: biting off more than we can chew?

Background

Food tax-subsidy policies are proposed to hold promise for helping to produce healthier patterns of food purchasing and consumption at population level. Evidence for their effects derives largely from simulation studies that explore the potential effects of untried policies using a mathematical modelling framework. This paper provides a critique first of the nature of the evidence derived from such simulation studies, and second of the challenges of cumulating that evidence to inform public health policy.

Discussion

Effects estimated by simulation studies of food taxes and subsidies can be expected to diverge in potentially important ways from those that would accrue in practice because these models are simplified, typically static, representations of complex adaptive systems. The level of confidence that can be placed in modelled estimates of effects is correspondingly low, and the level of associated uncertainty is high. Moreover, evidence from food tax-subsidy simulation studies cannot meaningfully be cumulated using currently available quantitative evidence synthesis methods, to reduce uncertainty about effects.

Summary

Simulation studies are critical for the initial phases of an incremental research process, for drawing together diverse evidence and exploring potential longer-term effects. While simulation studies of food taxes and subsidies provide a valuable and necessary input to the formulation of public health policy in this area, they are unlikely to be sufficient, and policy makers should not place excessive reliance on evidence from such studies, either singly or cumulatively. To reflect known and unknown limitations of the models, results of such studies should be interpreted cautiously as tentative projections. Modelling studies should increasingly be integrated with more empirical studies of the effects of food tax and subsidy policies in practice.

[Review of models for the analysis of ethical dilemmas]

In pediatric medical practice it is common to encounter situations that represent a dilemma for health professionals. A dilemma occurs when ethical problems found in professional practice cause serious internal conflicts because they imply actions that contradict their colleagues, employees, or their own personal values and are classified as personal value conflicts, conflicts with other professionals, conflicts with clients and with organizations. A literature review allowed identifying different models to debate these types of dilemmas. The present work is a review of the search of scientific articles using databases such as Ebsco Host, ProQuest, Ovid, and InMex as well as metasearch tools such as metacrawler. The models found are as follows: Model of Anne Davis, Nijmegen method, Method of Diego Gracia, Integral method, Bochum Center Ethics model, Model of Brody and Payton, Model of Curtin and Flaherty, Model of Thompson and Thompson, SAD method, Model of Javier Morata, Model of Elaine Congress, IFSW model, Model of Loewenberg and Dolgoff, Ley Social Model, DOER method, Model of Brommer, Model of Corey and Callanan, Model of Pope and Vasquez, Model of Bush, Connell and Denney, Model of Ferrell, Gresham and Fraedrich, and Model of Hunt and Vitell. The key criteria shared in the different models are a) specifying the ethical dilemma, b) description of the facts, c) value definition, moral code and facts, decision making and d) identifying alternative solutions. In order to review the literature, some models are explained with the purpose of identifying and representing critical elements that clinical ethics committees could use in a practical manner in pediatric health institutions in Mexico.

Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012): a systematic review

BACKGROUND

Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine.

METHODS

A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded.

RESULTS

A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively.

CONCLUSIONS

During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.

An epidemiologic model to project the impact of changes in glomerular filtration rate on quality of life and survival among persons with chronic kidney disease

PURPOSE

Predicting the timing and number of end-stage renal disease (ESRD) cases from a population of individuals with pre-ESRD chronic kidney disease (CKD) has not previously been reported. The objective is to predict the timing and number of cases of ESRD occurring over the lifetime of a cohort of hypothetical CKD patients in the US based on a range of baseline estimated glomerular filtration rate (eGFR) values and varying rates of eGFR decline.

METHODS

A three-state Markov model - functioning kidney, ESRD, and death - with an annual cycle length is used to project changes in baseline eGFR on long-term health outcomes in a hypothetical cohort of CKD patients. Using published eGFR-specific risk equations and adjusting for predictive characteristics, the probability of ESRD (eGFR <10), time to death, and incremental cost-effectiveness ratios for hypothetical treatments (costing US$10, $5, and $2/day), are projected over the cohort's lifetime under two scenarios: an acute drop in eGFR (mimicking acute kidney injury) and a reduced hazard ratio for ESRD (mimicking an effective intervention).

RESULTS

Among CKD patients aged 50 years, an acute eGFR decrement from 45 mL/minute to 35 mL/minute yields decreases of 1.6 life-years, 1.5 quality-adjusted life-years (QALYs), 0.8 years until ESRD, and an increase of 183 per 1,000 progressing to ESRD. Among CKD patients aged 60 years, lowering the hazard ratio of ESRD to 0.8 yields values of 0.2, 0.2, 0.2, and 46 per 1,000, respectively. Incremental cost-effectiveness ratios are higher (ie, less favorable) for higher baseline eGFR, indicating that interventions occurring later in the course of disease are more likely to be economically attractive.

CONCLUSION

Both acute kidney injury and slowing the rate of eGFR decline produce substantial shifts in expected numbers and timing of ESRD among CKD patients. This model is a useful tool for planning management of CKD patients.

Projecting long-term graft and patient survival after transplantation

OBJECTIVE

In spite of increases in short-term kidney transplant survival rates and reductions in acute rejection rates, increasing long-term graft survival rates remains a major challenge. The objective here was to project long-term graft- and survival-related outcomes occurring among renal transplant recipients based on short-term outcomes including acute rejection and estimated glomerular filtration rates observed in randomized trials.

METHODS

We developed a two-phase decision model including a trial phase and a Markov state transition phase to project long-term outcomes over the lifetimes of hypothetical renal graft recipients who survived the trial period with a functioning graft. Health states included functioning graft stratified by level of renal function, failed graft, functioning regraft, and death. Transitions between health states were predicted using statistical models that accounted for renal function, acute rejection, and new-onset diabetes after transplant and for donor and recipient predictors of long-term graft and patient survival. Models were estimated using data from 38,015 renal transplant recipients from the United States Renal Data System. The model was populated with data from a 3-year, randomized phase III trial comparing belatacept to cyclosporine.

RESULTS

The decision model was well calibrated with data from the United States Renal Data System. Long-term extrapolation of Belatacept Evaluation of Nephroprotection and Efficacy as Firstline Immunosuppression Trial was projected to yield a 1.9-year increase in time alive with a functioning graft and a 1.2 life-year increase over a 20-year time horizon.

CONCLUSIONS

This is the first long-term follow-up model of renal transplant patients to be based on renal function, acute rejection, and new-onset diabetes. It is a useful tool for undertaking comparative effectiveness and cost-effectiveness studies of immunosuppressive medications.

A systematic review of models used in cost-effectiveness analyses of preventing osteoporotic fractures

This review was aimed at the evolution of health economic models used in evaluations of clinical approaches aimed at preventing osteoporotic fractures. Models have improved, with medical continuance becoming increasingly recognized as a contributor to health and economic outcomes, as well as advancements in epidemiological data. Model-based health economic evaluation studies are increasingly used to investigate the cost-effectiveness of osteoporotic fracture preventions and treatments. The objective of this study was to carry out a systematic review of the evolution of health economic models used in the evaluation of osteoporotic fracture preventions. Electronic searches within MEDLINE and EMBASE were carried out using a predefined search strategy. Inclusion and exclusion criteria were used to select relevant studies. References listed of included studies were searched to identify any potential study that was not captured in our electronic search. Data on country, interventions, type of fracture prevention, evaluation perspective, type of model, time horizon, fracture sites, expressed costs, types of costs included, and effectiveness measurement were extracted. Seventy-four models were described in 104 publications, of which 69% were European. Earlier models focused mainly on hip, vertebral, and wrist fracture, but later models included multiple fracture sites (humerus, pelvis, tibia, and other fractures). Modeling techniques have evolved from simple decision trees, through deterministic Markov processes to individual patient simulation models accounting for uncertainty in multiple parameters. Treatment continuance has been increasingly taken into account in the models in the last decade. Models have evolved in their complexity and emphasis, with medical continuance becoming increasingly recognized as a contributor to health and economic outcomes. This evolution may be driven in part by the desire to capture all the important differentiating characteristics of medications under scrutiny, as well as the advancement in epidemiological data relevant to osteoporosis fractures.

Adapting and validating diabetes simulation models across settings: accounting for mortality differences using administrative data

AIMS

To develop age and sex-specific risk equations for predicting mortality following major complications of diabetes, using a large linked administrative dataset from Western Australia (WA) and to incorporate these into an existing diabetes simulation model.

METHODS

The study uses linked hospital and mortality records on 13,884 patients following a major diabetes-related complication with a mean (SD) duration of 2.62 (2.25) years. Risk equations for predicting mortality were derived and integrated into the UKPDS Outcomes Model. Estimates of life expectancy and incremental QALYs gained as a result of two theoretical therapies (a reduction of HbA1c of 1%, and reduction of systolic blood pressure of 10 mmHg) were determined using the original and adapted models.

RESULTS

The two versions of the model generated differences in life expectancy following specific events; however there was little impact of using alternative mortality equations on incremental QALYs gained as a result of reducing HbA(1c) or systolic blood pressure, or on outcomes of life expectancy for a cohort initially free of complications.

CONCLUSIONS

Mortality following complications varies across diabetic populations and can impact on estimates of life expectancy, but appears to have less impact on incremental benefits of interventions that are commonly used in pharmoeconomic analyses.

Predictive validation of an influenza spread model

BACKGROUND

Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread.

METHODS AND FINDINGS

We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic.

CONCLUSIONS

Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability.

The comparison of trial data-based and registry data-based cost-effectiveness of infliximab treatment for rheumatoid arthritis in Sweden using a modeling approach

OBJECTIVE

To evaluate the precision of the predictive cost-effectiveness assessment based on a phase 3 clinical trial with infliximab for the treatment of rheumatoid arthritis in Swedish clinical practice.

METHODS

Three patient cohorts were identified: the patients included in the infliximab trial (ATTRACT), patients initially treated with infliximab from a Swedish registry (STURE), a subset of these registry patients meeting inclusion criteria for the ATTRACT trial was the third patient cohort; two sets of assumptions in relation to the efficacy data were evaluated: "ATTRACT" (efficacy data over the duration of the trial) and "STURE" (effectiveness data over 10 years). In addition, the impact of including the placebo effect for the comparator was evaluated as a basis for the calculation of cost-effectiveness by using a modeling approach. A health economic model was utilized to estimate the cost per quality-adjusted life-year (QALY) gained.

RESULTS

The results for the three patient cohorts ranged from cost saving to a cost per QALY gained of €2,400 and €24,900 to €26,000 when the ATTRACT and STURE assumptions were used, respectively. Sensitivity analyses indicated that the inclusion of placebo effect had the largest effect on the results, increasing the cost per QALY gained to approximately €50,000 for all patient cohorts.

CONCLUSIONS

The treatment effect of infliximab measured in clinical trials and clinical practice results in comparable cost-effectiveness ratios, as calculated by using a modeling approach, whereas the assumptions made in relation to the effectiveness data and the chosen comparator have a large impact on the results. This reinforces the value of early modeling studies based on randomized clinical trial data, but assumptions made need to be carefully assessed.

Comparative effectiveness of alternative prostate-specific antigen--based prostate cancer screening strategies: model estimates of potential benefits and harms

BACKGROUND

The U.S. Preventive Services Task Force recently concluded that the harms of existing prostate-specific antigen (PSA) screening strategies outweigh the benefits.

OBJECTIVE

To evaluate comparative effectiveness of alternative PSA screening strategies.

DESIGN

Microsimulation model of prostate cancer incidence and mortality quantifying harms and lives saved for alternative PSA screening strategies.

DATA SOURCES

National and trial data on PSA growth, screening and biopsy patterns, incidence, treatment distributions, treatment efficacy, and mortality.

TARGET POPULATION

A contemporary cohort of U.S. men.

TIME HORIZON

Lifetime.

PERSPECTIVE

Societal.

INTERVENTION

35 screening strategies that vary by start and stop ages, screening intervals, and thresholds for biopsy referral.

OUTCOME MEASURES

PSA tests, false-positive test results, cancer detected, overdiagnoses, prostate cancer deaths, lives saved, and months of life saved.

RESULTS OF BASE-CASE ANALYSIS

Without screening, the risk for prostate cancer death is 2.86%. A reference strategy that screens men aged 50 to 74 years annually with a PSA threshold for biopsy referral of 4 µg/L reduces the risk for prostate cancer death to 2.15%, with risk for overdiagnosis of 3.3%. A strategy that uses higher PSA thresholds for biopsy referral in older men achieves a similar risk for prostate cancer death (2.23%) but reduces the risk for overdiagnosis to 2.3%. A strategy that screens biennially with longer screening intervals for men with low PSA levels achieves similar risks for prostate cancer death (2.27%) and overdiagnosis (2.4%), but reduces total tests by 59% and false-positive results by 50%.

RESULTS OF SENSITIVITY ANALYSIS

Varying incidence inputs or reducing the survival improvement due to screening did not change conclusions.

LIMITATION

The model is a simplification of the natural history of prostate cancer, and improvement in survival due to screening is uncertain.

CONCLUSION

Compared with standard screening, PSA screening strategies that use higher thresholds for biopsy referral for older men and that screen men with low PSA levels less frequently can reduce harms while preserving lives.

PRIMARY FUNDING SOURCE

National Cancer Institute and Centers for Disease Control and Prevention.

Cost-effectiveness analysis of boceprevir for the treatment of chronic hepatitis C virus genotype 1 infection in Portugal

BACKGROUND

The recent approval of two protease inhibitors, boceprevir and telaprevir, is likely to change the management of chronic hepatitis C virus (HCV) genotype 1 infection.

OBJECTIVES

We evaluated the long-term clinical outcomes and the cost effectiveness of therapeutic strategies using boceprevir with peginterferon plus ribavirin (PR) in comparison with PR alone for treating HCV genotype 1 infection in Portugal.

METHODS

A Markov model was developed to project the expected lifetime costs and quality-adjusted life-years (QALYs) associated with PR alone and the treatment strategies outlined by the European Medicines Agency in the boceprevir summary of product characteristics. The boceprevir-based therapeutic strategies differ according to whether or not the patient was previously treated and whether or not the patient had compensated cirrhosis. The model simulated the experience of a series of cohorts of chronically HCV-infected patients (each defined by age, sex, race and fibrosis score). All treatment-related inputs were obtained from boceprevir clinical trials - SPRINT-2, RESPOND-2 and PROVIDE. Estimates of the natural history parameters and health state utilities were based on published studies. Portugal-specific annual direct costs of HCV health states were estimated by convening a panel of experts to derive health state resource use and multiplying the results by national unit costs. The model was developed from a healthcare system perspective with a timeframe corresponding to the remaining duration of the patients' lifetimes. Both future costs and QALYs were discounted at 5 %. To test the robustness of the conclusions, we conducted deterministic and probabilistic sensitivity analyses.

RESULTS

In comparison with the treatment with PR alone, boceprevir-based regimens were projected to reduce the lifetime incidence of advanced liver disease, liver transplantation, and liver-related death by 45-51 % and increase life expectancy by 2.3-4.3 years. Although the addition of BOC increased treatment costs by €13,300-€19,700, the reduction of disease burden resulted in a decrease of €5,400-€9,000 in discounted health state costs and an increase of 0.68-1.23 in discounted QALYs per patient. The incremental cost-effectiveness ratios of the boceprevir-based regimens compared with PR among previously untreated and previously treated patients were €11,600/QALY and €8,700/QALY, respectively. The results were most sensitive to variations in sustained virologic response rates, discount rates and age at treatment.

CONCLUSIONS

Adding boceprevir to PR was projected to reduce the number of liver complications and liver-related deaths, and to be cost effective in treating both previously untreated and treated patients.

Adding decision models to systematic reviews: informing a framework for deciding when and how to do so

OBJECTIVE

Decision models are sometimes used alongside systematic reviews to synthesize evidence. Clarity, however, is lacking about when and how to conduct modeling studies in tandem with systematic reviews, as well as about how to evaluate and present model results. The objective of this study was to collect and analyze information from various sources to inform the development of a framework for deciding when and how a decision model should be added to a systematic review.

METHODS

We collected data through 1) review and analysis of evidence reports that used decision models; 2) review and synthesis of current best practices for the development of decision models; 3) interviews of Evidence-Based Practice Center directors and selected staff, United States Preventive Services Task Force members, and decision modelers who developed models used by the United States Preventive Services Task Force; and 4) a focus group of expert modelers.

RESULTS

Models are well suited to address gaps in the literature, better suited for certain types of research questions, and essential for determining the value of information relating to future research. Opinions differ regarding whether model outputs constitute evidence, but interviewees expressed concern over the lack of standards and directions in grading and reporting such "evidence." Interviews of stakeholders and modelers revealed the importance of communication and presentation of model results as well as the importance of model literacy and involvement of stakeholders.

CONCLUSIONS

The study demonstrates the need for a framework for deciding when and how to use models alongside systematic reviews and provides information to develop such a framework.

The cost-effectiveness of tracking newborns with bilateral hearing impairment in Bavaria: a decision-analytic model

Background

Although several countries, including Germany, have established newborn hearing screening programmes for early detection and treatment of newborns with hearing impairments, nationwide tracking systems for follow-up of newborns with positive test results until diagnosis of hearing impairment have often not been implemented. However, a recent study on universal newborn hearing screening in Bavaria showed that, in a high proportion of newborns, early diagnosis was only possible with the use of a tracking system. The aim of this study was, therefore, to assess the cost-effectiveness of tracking newborns with bilateral hearing impairment in Bavaria.

Methods

Data from a Bavarian pilot project on newborn hearing screening and Bavarian newborn hearing screening facilities were used to assess the cost-effectiveness of the inclusion of a tracking system within a newborn hearing screening programme. A model-based cost-effectiveness analysis was conducted. The time horizon of the model was limited to the newborn hearing screening programme. Costs of the initial hearing screening test and subsequent tests were included, as well as costs of diagnosis and costs of tracking. The outcome measure of the economic analysis was the cost per case of bilateral hearing impairment detected. In order to reflect uncertainty, deterministic and probabilistic sensitivity analyses were performed.

Results

The incremental cost-effectiveness ratio of tracking vs. no tracking was €1,697 per additional case of bilateral hearing impairment detected.

Conclusions

Compared with no tracking, tracking resulted in more cases of bilateral hearing impairment detected as well as higher costs. If society is willing to pay at least €1,697 per additional case of bilateral hearing impairment detected, tracking can be recommended.

Reporting guidelines for modelling studies

Background

Modelling studies are used widely to help inform decisions about health care and policy and their use is increasing. However, in order for modelling to gain strength as a tool for health policy, it is critical that key model factors are transparent so that users of models can have a clear understanding of the model and its limitations.Reporting guidelines are evidence-based tools that specify minimum criteria for authors to report their research such that readers can both critically appraise and interpret study findings. This study was conducted to determine whether there is an unmet need for population modelling reporting guidelines.

Methods

We conducted a review of the literature to identify: 1) guidance for reporting population modelling studies; and, 2) evidence on the quality of reporting of population modelling studies. Guidance for reporting was analysed using a thematic approach and the data was summarised as frequencies. Evidence on the quality of reporting was reviewed and summarized descriptively.

Results

There were no guidelines that specifically addressed the reporting of population modelling studies. We identified a number of reporting guidelines for economic evaluation studies, some of which had sections that were relevant population modelling studies. Amongst seven relevant records, we identified 69 quality criteria that have distinct reporting characteristics. We identified two papers that addressed reporting practices of modelling studies. Overall, with the exception of describing the data used for calibration, there was little consistency in reporting.

Conclusions

While numerous guidelines exist for developing and evaluating health technology assessment and economic evaluation models, which by extension could be applicable to population modelling studies, there is variation in their comprehensiveness and in the consistency of reporting these methods. Population modelling studies may be an area which would benefit from the development of a reporting guideline.

Model Transparency and Validation

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well it reproduces reality). This report describes recommendations for achieving transparency and validation, developed by a task force appointed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM). Recommendations were developed iteratively by the authors. A nontechnical description should be made available to anyone—including model type and intended applications; funding sources; structure; inputs, outputs, other components that determine function, and their relationships; data sources; validation methods and results; and limitations. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing same problem), external validity (comparing model results to real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this paper contains a number of recommendations that were iterated among the authors, as well as the wider modeling task force jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--7

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well the model reproduces reality). This report describes recommendations for achieving transparency and validation developed by a taskforce appointed by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Recommendations were developed iteratively by the authors. A nontechnical description--including model type, intended applications, funding sources, structure, intended uses, inputs, outputs, other components that determine function, and their relationships, data sources, validation methods, results, and limitations--should be made available to anyone. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this article contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

Stakeholder perspectives on decision-analytic modeling frameworks to assess genetic services policy

PURPOSE

Genetic services policymakers and insurers often make coverage decisions in the absence of complete evidence of clinical utility and under budget constraints. We evaluated genetic services stakeholder opinions on the potential usefulness of decision-analytic modeling to inform coverage decisions, and asked them to identify genetic tests for decision-analytic modeling studies.

METHODS

We presented an overview of decision-analytic modeling to members of the Western States Genetic Services Collaborative Reimbursement Work Group and state Medicaid representatives and conducted directed content analysis and an anonymous survey to gauge their attitudes toward decision-analytic modeling. Participants also identified and prioritized genetic services for prospective decision-analytic evaluation.

RESULTS

Participants expressed dissatisfaction with current processes for evaluating insurance coverage of genetic services. Some participants expressed uncertainty about their comprehension of decision-analytic modeling techniques. All stakeholders reported openness to using decision-analytic modeling for genetic services assessments. Participants were most interested in application of decision-analytic concepts to multiple-disorder testing platforms, such as next-generation sequencing and chromosomal microarray.

CONCLUSION

Decision-analytic modeling approaches may provide a useful decision tool to genetic services stakeholders and Medicaid decision-makers.

Monte Carlo role in radiobiological modelling of radiotherapy outcomes

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.