Keeping the noise down: common random numbers for disease simulation modeling

Estimating the Relationship Between the Symptom-Free Waiting Period and Injury Rates After Return-to-Play from Concussion: A Simulation Analysis Using CARE Consortium Data

BACKGROUND

A key component of return-to-play (RTP) from sport-related concussion is the symptom-free waiting period (SFWP), i.e., the period during which athletes must remain symptom-free before permitting RTP. Yet, the exact relationship between SFWP and post-RTP injury rates is unclear.

OBJECTIVE

We design computational simulations to estimate the relationship between the SFWP and rates of repeat concussion and non-concussion time-loss injury up to 30 days post-RTP for male and female collegiate athletes across 13 sports.

METHODS

We leverage N = 735 female and N = 1,094 male post-injury trajectories from the National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research, and Education Consortium.

RESULTS

With a 6-day SFWP, the mean [95% CI] rate of repeat concussion per 1,000 simulations was greatest in ice hockey for females (20.31, [20.16, 20.46]) and American football for males (24.16, [24.05, 24.28]). Non-concussion time-loss injury rates were greatest in field hockey for females (153.66, [152.59, 154.74]) and wrestling for males (247.34, [246.20, 248.48]). Increasing to a 13-day SFWP, ice hockey for females (18.88, [18.79, 18.98]) and American football for males (23.16, [23.09, 24.22]) exhibit the greatest decrease in repeat concussion rates across all sports within their respective sexes. Field hockey for females (143.24, [142.53, 143.94]) and wrestling for males (237.73, [236.67, 237.90]) exhibit the greatest decrease in non-concussion time-loss injury rates. Males receive marginally smaller reductions in injury rates for increased SFWP compared to females (OR = 1.003, p ≤ 0.002).

CONCLUSION

Longer SFWPs lead to greater reductions in post-RTP injury rates for athletes in higher risk sports. Moreover, SFWPs should be tailored to sport-specific post-RTP injury risks.

Cost-Effectiveness of Treatment Thresholds for Subsolid Pulmonary Nodules in CT Lung Cancer Screening

Background Guidelines such as the Lung CT Screening Reporting and Data System (Lung-RADS) are available for determining when subsolid nodules should be treated within lung cancer screening programs, but they are based on expert opinion. Purpose To evaluate the cost-effectiveness of varying treatment thresholds for subsolid nodules within a lung cancer screening setting by using a simulation model. Materials and Methods A previously developed model simulated 10 million current and former smokers undergoing CT lung cancer screening who were assumed to have a ground-glass nodule (GGN) at baseline. Nodules were allowed to grow and to develop solid components over time according to a monthly cycle and lifetime horizon. Management strategies generated by varying treatment thresholds, including the solid component size and use of the Brock risk calculator, were tested. For each strategy, average U.S. costs and quality-adjusted life years (QALYs) gained per patient were computed, and the incremental cost-effectiveness ratios (ICERs) of those on the efficient frontier were calculated. One-way and probabilistic sensitivity analyses of results were performed by varying several relevant parameters, such as treatment costs or malignancy growth rates. Results Variants of the Lung-RADS guidelines that did not treat pure GGNs were cost-effective. Strategies based on the Brock risk calculator did not reach the efficient frontier. The strategy with the highest QALYs under a willingness-to-pay threshold of $100 000 per QALY included no treatment of GGNs and a threshold of 4-mm solid component size for treatment of subsolid nodules. This strategy yielded an ICER of $52 993 per QALY (95% CI: 44 407, 64 372). Probabilistic sensitivity analysis showed this was the optimal strategy under a range of parameter variations. Conclusion Treatment of pure ground-glass nodules was not cost-effective. Strategies that use modifications of the Lung CT Screening Reporting and Data System guidelines were cost-effective for treating part-solid nodules; an optimal threshold of 4 mm for the solid component yielded the most quality-adjusted life years. © RSNA, 2021 Online supplemental material is available for this article.

Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of "jumpover" states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

The impact of energy retrofits on pediatric asthma exacerbation in a Boston multi-family housing complex: a systems science approach

BACKGROUND

Pediatric asthma is currently the most prevalent chronic disease in the United States, with children in lower income families disproportionately affected. This increased health burden is partly due to lower-quality and insufficient maintenance of affordable housing. A movement towards 'green' retrofits that improve energy efficiency and increase ventilation in existing affordable housing offers an opportunity to provide cost-effective interventions that can address these health disparities.

METHODS

We combine indoor air quality modeling with a previously developed discrete event model for pediatric asthma exacerbation to simulate the effects of different types of energy retrofits implemented at an affordable housing site in Boston, MA.

RESULTS

Simulation results show that retrofits lead to overall better health outcomes and healthcare cost savings if reduced air exchange due to energy-saving air tightening is compensated by mechanical ventilation. Especially when exposed to indoor tobacco smoke and intensive gas-stove cooking such retrofit would lead to an average annual cost saving of over USD 200, while without mechanical ventilation the same children would have experienced an increase of almost USD 200/year in health care utilization cost.

CONCLUSION

The combination of indoor air quality modeling and discrete event modeling applied in this paper can allow for the inclusion of health impacts in cost-benefit analyses of proposed affordable housing energy retrofits.

Evaluation of the Cost-effectiveness of Infection Control Strategies to Reduce Hospital-Onset Clostridioides difficile Infection

Importance

Clostridioides difficile infection is the most common hospital-acquired infection in the United States, yet few studies have evaluated the cost-effectiveness of infection control initiatives targeting C difficile.

Objective

To compare the cost-effectiveness of 9 C difficile single intervention strategies and 8 multi-intervention bundles.

Design, Setting, and Participants

This economic evaluation was conducted in a simulated 200-bed tertiary, acute care, adult hospital. The study relied on clinical outcomes from a published agent-based simulation model of C difficile transmission. The model included 4 agent types (ie, patients, nurses, physicians, and visitors). Cost and utility estimates were derived from the literature.

Interventions

Daily sporicidal cleaning, terminal sporicidal cleaning, health care worker hand hygiene, patient hand hygiene, visitor hand hygiene, health care worker contact precautions, visitor contact precautions, C difficile screening at admission, and reduced intrahospital patient transfers.

Main Outcomes and Measures

Cost-effectiveness was evaluated from the hospital perspective and defined by 2 measures: cost per hospital-onset C difficile infection averted and cost per quality-adjusted life-year (QALY).

Results

In this agent-based model of a simulated 200-bed tertiary, acute care, adult hospital, 5 of 9 single intervention strategies were dominant, reducing cost, increasing QALYs, and averting hospital-onset C difficile infection compared with baseline standard hospital practices. They were daily cleaning (most cost-effective, saving $358 268 and 36.8 QALYs annually), health care worker hand hygiene, patient hand hygiene, terminal cleaning, and reducing intrahospital patient transfers. Screening at admission cost $1283/QALY, while health care worker contact precautions and visitor hand hygiene interventions cost $123 264/QALY and $5 730 987/QALY, respectively. Visitor contact precautions was dominated, with increased cost and decreased QALYs. Adding screening, health care worker hand hygiene, and patient hand hygiene sequentially to the daily cleaning intervention formed 2-pronged, 3-pronged, and 4-pronged multi-intervention bundles that cost an additional $29 616/QALY, $50 196/QALY, and $146 792/QALY, respectively.

Conclusions and Relevance

The findings of this study suggest that institutions should seek to streamline their infection control initiatives and prioritize a smaller number of highly cost-effective interventions. Daily sporicidal cleaning was among several cost-saving strategies that could be prioritized over minimally effective, costly strategies, such as visitor contact precautions.

Reducing C. difficile in children: An agent-based modeling approach to evaluate intervention effectiveness

OBJECTIVE

Clostridioides difficile infection (CDI) is rapidly increasing in children's hospitals nationwide. Thus, we aimed to compare the effectiveness of 9 infection prevention interventions and 6 multiple-intervention bundles at reducing hospital-onset CDI and asymptomatic C. difficile colonization.

DESIGN

Agent-based simulation model of C. difficile transmission.

SETTING

Computer-simulated, 80-bed freestanding, tertiary-care pediatric hospital, including 8 identical wards with 10 single-bed patient rooms each.

PARTICIPANTS

The model includes 5 distinct agent types: patients, visitors, caregivers, nurses, and physicians.

INTERVENTIONS

Daily and terminal environmental disinfection, screening at admission, reduced intrahospital patient transfers, healthcare worker (HCW), visitor, and patient hand hygiene, and HCW and visitor contact precautions.

RESULTS

The model predicted that daily environmental disinfection with sporicidal product, combined with screening for asymptomatic C. difficile at admission, was the most effective 2-pronged infection prevention bundle, reducing hospital-onset CDI by 62.0% and asymptomatic colonization by 88.4%. Single-intervention strategies, including daily disinfection, terminal disinfection, asymptomatic screening at admission, HCW hand hygiene, and patient hand hygiene, as well as decreasing intrahospital patient transfers, all also reduced both hospital-onset CDI and asymptomatic colonization in the model. Visitor hand hygiene and visitor and HCW contact precautions were not effective at reducing either measure.

CONCLUSIONS

Hospitals can achieve substantial reduction in hospital-onset CDIs by implementing a small number of highly effective interventions.

Estimation of Eating Disorders Prevalence by Age and Associations With Mortality in a Simulated Nationally Representative US Cohort

IMPORTANCE

Eating disorders (EDs) are common psychiatric disorders associated with high mortality. However, data on ED disease dynamics and treatment coverage are sparse.

OBJECTIVES

To model the individual-level disease dynamics of ED from birth to age 40 years and to estimate the association of increased treatment coverage with ED-related mortality.

DESIGN, SETTING, AND PARTICIPANTS

In this decision analytical model study, an individual-level Markov state transition model was empirically calibrated in April 2019 using a Bayesian approach to synthesize available clinical and epidemiologic ED data. The simulation model was calibrated to nationally representative US survey data from 2007 and 2011. A virtual cohort of 100 000 individuals (50 000 [50%] male) was modeled from birth to age 40 years for 4 ED diagnoses: anorexia nervosa, bulimia nervosa, binge eating disorder, and other specified feeding and eating disorders.

EXPOSURES

Age-specific ED incidence and mortality rates and background (all-cause) mortality.

MAIN OUTCOMES AND MEASURES

The main outcomes were age-specific 12-month and lifetime ED prevalence and number of deaths per 100 000 general population individuals by age 40 years. The mean and 95% uncertainty intervals (UIs) of 1000 simulations, accounting for stochastic and parameter uncertainty, are reported.

RESULTS

The highest estimated mean annual prevalence of ED occurred at approximately age 21 years for both male individuals (7.4%; 95% UI, 3.5%-11.5%) and female individuals (10.3%; 95% UI, 7.0%-14.2%), with lifetime mean prevalence estimates increasing to 14.3% (95% UI, 9.7%-19.0%) for male individuals and 19.7% (95% UI, 15.8%-23.9%) for female individuals by age 40 years. Ninety-five percent of first-time cases occurred by age 25 years. Current treatment coverage averts an estimated mean of 41.7 deaths per 100 000 people (95% UI, 13.0-82.0 deaths per 100 000 people) by age 40 years, whereas increasing treatment coverage for all patients with ED could avert an estimated mean of 70.5 deaths per 100 000 people by age 40 years (95% UI, 26.0-143.0 deaths per 100 000 people).

CONCLUSIONS AND RELEVANCE

In this simulation modeling study, the estimated lifetime prevalence of ED was high, with approximately 1 in 7 male and 1 in 5 female individuals having an ED by age 40 years. The initial onset of EDs was highly concentrated during adolescence and young adulthood, suggesting that this is a critical period for prevention efforts. However, the high estimated prevalence of recurring ED later in life highlights the importance of identification and treatment of ED at older ages as well. These findings suggest that increasing treatment coverage could substantially reduce ED-related mortality.

Healthcare Policy Changes in Osteoporosis Can Improve Outcomes and Reduce Costs in the United States

In the United States, osteoporosis affects over 10 million adults, has high societal costs ($22 billion in 2008), and is currently being underdiagnosed and undertreated. Given an aging population, this burden is expected to rise. We projected the fracture burden in US women by modeling the expected demographic shift as well as potential policy changes. With the anticipated population aging and growth, annual fractures are projected to increase from 1.9 million to 3.2 million (68%), from 2018 to 2040, with related costs rising from $57 billion to over $95 billion. Policy‐driven expansion of case finding and treatment of at‐risk women could lower this burden, preventing 6.1 million fractures over the next 22 years while reducing payer costs by $29 billion and societal costs by $55 billion. Increasing use of osteoporosis‐related interventions can reduce fractures and result in substantial cost‐savings, a rare and fortunate combination given the current landscape in healthcare policy. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Protecting the gains: What changes are needed to prevent a reversal of the downward cardiovascular disease mortality trend?

AIMS

Cardiovascular disease (CVD) mortality has decreased over 60% over the past 50 years in the United States; however, emerging data indicate CVD incidence may be rising because of shifting demographics, increasing risk factor prevalence, and competing needs for limited resources. We projected CVD mortality from 2015 to 2040 given varying informed assumptions regarding changes in risk factor prevalence, uptake of current therapeutic options, and future innovations.

METHODS

A microsimulation model was used to project US CVD mortality trends. National Health and Nutrition Examination Survey data were used to estimate population-level trends in CVD risk factors. Risk factors were used to generate Framingham Risk Scores for cohorts of 1 000 000 individuals from the general population to determine each individuals' CVD risk. Annual cardiovascular incidence, prevalence, and mortality were projected for scenarios differing by uptake of current therapies, anticipated pharmaceutical innovations with variable efficacy, risk factor prevalence, and changes in health disparities.

RESULTS

When incorporating a demographic shift, continued changes in risk factors, current treatment utilization, and no major innovations, we predicted the CVD mortality rate would increase 41% by 2040. If innovations providing incremental benefits equal to those associated with the introduction of statins are identified and widely utilized, CVD mortality could remain constant through 2040. With more efficacious innovations, CVD mortality could be further reduced.

CONCLUSIONS

Given demographic and risk prevalence changes, increasing access and adherence to current preventative therapeutics could slow the expected mortality increase, but new therapies may be needed to maintain the downward trend in CVD deaths.

Interventions to Reduce the Incidence of Hospital-Onset Clostridium difficile Infection: An Agent-Based Modeling Approach to Evaluate Clinical Effectiveness in Adult Acute Care Hospitals

Background

Despite intensified efforts to reduce hospital-onset Clostridium difficile infection (HO-CDI), its clinical and economic impacts continue to worsen. Many institutions have adopted bundled interventions that vary considerably in composition, strength of evidence, and effectiveness. Considerable gaps remain in our knowledge of intervention effectiveness and disease transmission, which hinders HO-CDI prevention.

Methods

We developed an agent-based model of C. difficile transmission in a 200-bed adult hospital using studies from the literature, supplemented with primary data collection. The model includes an environmental component and 4 distinct agent types: patients, visitors, nurses, and physicians. We used the model to evaluate the comparative clinical effectiveness of 9 single interventions and 8 multiple-intervention bundles at reducing HO-CDI and asymptomatic C. difficile colonization.

Results

Daily cleaning with sporicidal disinfectant and C. difficile screening at admission were the most effective single-intervention strategies, reducing HO-CDI by 68.9% and 35.7%, respectively (both P < .001). Combining these interventions into a 2-intervention bundle reduced HO-CDI by 82.3% and asymptomatic hospital-onset colonization by 90.6% (both, P < .001). Adding patient hand hygiene to healthcare worker hand hygiene reduced HO-CDI rates an additional 7.9%. Visitor hand hygiene and contact precaution interventions did not reduce HO-CDI, compared with baseline. Excluding those strategies, healthcare worker contact precautions were the least effective intervention at reducing hospital-onset colonization and infection.

Conclusions

Identifying and managing the vast hospital reservoir of asymptomatic C. difficile by screening and daily cleaning with sporicidal disinfectant are high-yield strategies. These findings provide much-needed data regarding which interventions to prioritize for optimal C. difficile control.

Health and economic benefits of reducing sugar intake in the USA, including effects via non-alcoholic fatty liver disease: a microsimulation model

OBJECTIVES

Excessive consumption of added sugars in the human diet has been associated with obesity, type 2 diabetes (T2D), coronary heart disease (CHD) and other elements of the metabolic syndrome. Recent studies have shown that non-alcoholic fatty liver disease (NAFLD) is a critical pathway to metabolic syndrome. This model assesses the health and economic benefits of interventions aimed at reducing intake of added sugars.

METHODS

Using data from US National Health Surveys and current literature, we simulated an open cohort, for the period 2015-2035. We constructed a microsimulation model with Markov chains for NAFLD (including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis and hepatocellular carcinoma (HCC)), body mass index, T2D and CHD. We assessed reductions in population disease prevalence, disease-attributable disability-adjusted life years (DALYs) and costs, with interventions that reduce added sugars consumption by either 20% or 50%.

FINDINGS

The model estimated that a 20% reduction in added sugars intake will reduce prevalence of hepatic steatosis, NASH, cirrhosis, HCC, obesity, T2D and CHD. Incidence of T2D and CHD would be expected to decrease by 19.9 (95% CI 12.8 to 27.0) and 9.4 (95% CI 3.1 to 15.8) cases per 100 000 people after 20 years, respectively. A 20% reduction in consumption is also projected to annually avert 0.767 million (M) DALYs (95% CI 0.757M to 0.777M) and a total of US$10.3 billion (B) (95% CI 10.2B to 10.4B) in discounted direct medical costs by 2035. These effects increased proportionally when added sugars intake were reduced by 50%.

CONCLUSIONS

The decrease in incidence and prevalence of disease is similar to results in other models, but averted costs and DALYs were higher, mainly due to inclusion of NAFLD and CHD. The model suggests that efforts to reduce consumption of added sugars may result in significant public health and economic benefits.

Validation of a Cardiovascular Disease Policy Microsimulation Model Using Both Survival and Receiver Operating Characteristic Curves

BACKGROUND

Despite some advances, cardiovascular disease (CVD) remains the leading cause of death and healthcare costs in the United States. We therefore developed a comprehensive CVD policy simulation model that identifies cost-effective approaches for reducing CVD burden. This paper aims to: 1) describe our model in detail; and 2) perform model validation analyses.

METHODS

The model simulates 1,000,000 adults (ages 35 to 80 years) using a variety of CVD-related epidemiological data, including previously calibrated Framingham-based risk scores for coronary heart disease and stroke. We validated our microsimulation model using recent National Health and Nutrition Examination Survey (NHANES) data, with baseline values collected in 1999-2000 and cause-specific mortality follow-up through 2011. Model-based (simulated) results were compared to observed all-cause and CVD-specific mortality data (from NHANES) for the same starting population using survival curves and, in a method not typically used for disease model validation, receiver operating characteristic (ROC) curves.

RESULTS

Observed 10-year all-cause mortality in NHANES v. the simulation model was 11.2% (95% CI, 10.3% to 12.2%) v. 10.9%; corresponding results for CVD mortality were 2.2% (1.8% to 2.7%) v. 2.6%. Areas under the ROC curves for model-predicted 10-year all-cause and CVD mortality risks were 0.83 (0.81 to 0.85) and 0.84 (0.81 to 0.88), respectively; corresponding results for 5-year risks were 0.80 (0.77 to 0.83) and 0.81 (0.75 to 0.87), respectively.

LIMITATIONS

The model is limited by the uncertainties in the data used to estimate its input parameters. Additionally, our validation analyses did not include non-fatal CVD outcomes due to NHANES data limitations.

CONCLUSIONS

The simulation model performed well in matching to observed nationally representative longitudinal mortality data. ROC curve analysis, which has been traditionally used for risk prediction models, can also be used to assess discrimination for disease simulation models.

Cost-Effectiveness Analysis of Four Simulated Colorectal Cancer Screening Interventions, North Carolina

INTRODUCTION

Colorectal cancer (CRC) screening rates are suboptimal, particularly among the uninsured and the under-insured and among rural and African American populations. Little guidance is available for state-level decision makers to use to prioritize investment in evidence-based interventions to improve their population's health. The objective of this study was to demonstrate use of a simulation model that incorporates synthetic census data and claims-based statistical models to project screening behavior in North Carolina.

METHODS

We used individual-based modeling to simulate and compare intervention costs and results under 4 evidence-based and stakeholder-informed intervention scenarios for a 10-year intervention window, from January 1, 2014, through December 31, 2023. We compared the proportion of people living in North Carolina who were aged 50 to 75 years at some point during the window (that is, age-eligible for screening) who were up to date with CRC screening recommendations across intervention scenarios, both overall and among groups with documented disparities in receipt of screening.

RESULTS

We estimated that the costs of the 4 intervention scenarios considered would range from $1.6 million to $3.75 million. Our model showed that mailed reminders for Medicaid enrollees, mass media campaigns targeting African Americans, and colonoscopy vouchers for the uninsured reduced disparities in receipt of screening by 2023, but produced only small increases in overall screening rates (0.2-0.5 percentage-point increases in the percentage of age-eligible adults who were up to date with CRC screening recommendations). Increased screenings ranged from 41,709 additional life-years up to date with screening for the voucher intervention to 145,821 for the mass media intervention. Reminders mailed to Medicaid enrollees and the mass media campaign for African Americans were the most cost-effective interventions, with costs per additional life-year up to date with screening of $25 or less. The intervention expanding the number of endoscopy facilities cost more than the other 3 interventions and was less effective in increasing CRC screening.

CONCLUSION

Cost-effective CRC screening interventions targeting observed disparities are available, but substantial investment (more than $3.75 million) and additional approaches beyond those considered here are required to realize greater increases population-wide.

Economic evaluations with agent-based modelling: an introduction

Agent-based modelling (ABM) is a relatively new technique, which overcomes some of the limitations of other methods commonly used for economic evaluations. These limitations include linearity, homogeneity and stationarity. Agents in ABMs are autonomous entities, who interact with each other and with the environment. ABMs provide an inductive or 'bottom-up' approach, i.e. individual-level behaviours define system-level components. ABMs have a unique property to capture emergence phenomena that otherwise cannot be predicted by the combination of individual-level interactions. In this tutorial, we discuss the basic concepts and important features of ABMs. We present a case study of an application of a simple ABM to evaluate the cost effectiveness of screening of an infectious disease. We also provide our model, which was developed using an open-source software program, NetLogo. We discuss software, resources, challenges and future research opportunities of ABMs for economic evaluations.

Expected lifetime numbers and costs of fractures in postmenopausal women with and without osteoporosis in Germany: a discrete event simulation model

BACKGROUND

Osteoporotic fractures cause a large health burden and substantial costs. This study estimated the expected fracture numbers and costs for the remaining lifetime of postmenopausal women in Germany.

METHODS

A discrete event simulation (DES) model which tracks changes in fracture risk due to osteoporosis, a previous fracture or institutionalization in a nursing home was developed. Expected lifetime fracture numbers and costs per capita were estimated for postmenopausal women (aged 50 and older) at average osteoporosis risk (AOR) and for those never suffering from osteoporosis. Direct and indirect costs were modeled. Deterministic univariate and probabilistic sensitivity analyses were conducted.

RESULTS

The expected fracture numbers over the remaining lifetime of a 50 year old woman with AOR for each fracture type (% attributable to osteoporosis) were: hip 0.282 (57.9%), wrist 0.229 (18.2%), clinical vertebral 0.206 (39.2%), humerus 0.147 (43.5%), pelvis 0.105 (47.5%), and other femur 0.033 (52.1%). Expected discounted fracture lifetime costs (excess cost attributable to osteoporosis) per 50 year old woman with AOR amounted to € 4,479 (€ 1,995). Most costs were accrued in the hospital € 1,743 (€ 751) and long-term care sectors € 1,210 (€ 620). Univariate sensitivity analysis resulted in percentage changes between -48.4% (if fracture rates decreased by 2% per year) and +83.5% (if fracture rates increased by 2% per year) compared to base case excess costs. Costs for women with osteoporosis were about 3.3 times of those never getting osteoporosis (€ 7,463 vs. € 2,247), and were markedly increased for women with a previous fracture.

CONCLUSION

The results of this study indicate that osteoporosis causes a substantial share of fracture costs in postmenopausal women, which strongly increase with age and previous fractures.

Using common random numbers in health care cost-effectiveness simulation modeling

OBJECTIVES

To identify the problem of separating statistical noise from treatment effects in health outcomes modeling and analysis. To demonstrate the implementation of one technique, common random numbers (CRNs), and to illustrate the value of CRNs to assess costs and outcomes under uncertainty.

METHODS

A microsimulation model was designed to evaluate osteoporosis treatment, estimating cost and utility measures for patient cohorts at high risk of osteoporosis-related fractures. Incremental cost-effectiveness ratios (ICERs) were estimated using a full implementation of CRNs, a partial implementation of CRNs, and no CRNs. A modification to traditional probabilistic sensitivity analysis (PSA) was used to determine how variance reduction can impact a decision maker's view of treatment efficacy and costs.

RESULTS

The full use of CRNs provided a 93.6 percent reduction in variance compared to simulations not using the technique. The use of partial CRNs provided a 5.6 percent reduction. The PSA results using full CRNs demonstrated a substantially tighter range of cost-benefit outcomes for teriparatide usage than the cost-benefits generated without the technique.

CONCLUSIONS

CRNs provide substantial variance reduction for cost-effectiveness studies. By reducing variability not associated with the treatment being evaluated, CRNs provide a better understanding of treatment effects and risks.

State-Transition Modeling

State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs.

State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3

State-transition modeling is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling including both Markov model cohort simulation and individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, state-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. The goal of this article was to provide consensus-based guidelines for the application of state-transition models in the context of health care. We structured the best practice recommendations in the following sections: choice of model type (cohort vs. individual-level model), model structure, model parameters, analysis, reporting, and communication. In each of these sections, we give a brief description, address the issues that are of particular relevance to the application of state-transition models, give specific examples from the literature, and provide best practice recommendations for state-transition modeling. These recommendations are directed both to modelers and to users of modeling results such as clinicians, clinical guideline developers, manufacturers, or policymakers.