INVESTMENT IN ANTIVIRAL DRUGS:A REAL OPTIONS APPROACH

Real options analysis is a promising approach to model investment under uncertainty. We employ this approach to value stockpiling of antiviral drugs as a precautionary measure against a possible influenza pandemic. Modifications of the real options approach to include risk attitude and deviations from expected utility are presented. We show that risk aversion counteracts the tendency to delay investment for this case of precautionary investment, which is in contrast to earlier applications of risk aversion to real options analysis. Moreover, we provide a numerical example using real world data and discuss the implications of real options analysis for health policy. Suggestions for further extensions of the model and a comparison with the expected value of information analysis are put forward. Copyright (C) 2009 John Wiley & Sons, Ltd

Evaluating the Validation Process:Embracing Complexity and Transparency in Health Economic Modelling

Reimbursement decisions and price negotiation of healthcare interventions often rely on health economic model results. Such decisions affect resource allocation, patient outcomes and future healthcare choices. To ensure optimal decisions, assessing the validity of health economic models may be crucial. Validation involves much more than identifying (and hopefully correcting) errors in the model implementation. It also includes assessing the conceptual validity of the model and validation of the model input data, and checking whether the model’s predictions align sufficiently well with real-world data. In the context of health economics, validation can be defined as “the act of evaluating whether a model is a proper and sufficient representation of the system it is intended to represent in view of an application”, meaning that the model complies with what is known about the system and its outcomes provide a robust basis for decision making.[...]Validation of health economic models should be seen as a critical component of evidence-based decision making in healthcare. However, as of today, it still faces several important challenges, including the lack of consensus guidance and standardised procedures, the need for greater rigour or the question of who should oversee the validation process. To address these challenges, we encourage model developers, agencies requiring models for their decision making and editors of journals that publish models to recommend the use of state-of-the-art tools for reporting (and conducting) validations of health economic models, such as those mentioned in this editorial

Evaluating the Validation Process:Embracing Complexity and Transparency in Health Economic Modelling

Reimbursement decisions and price negotiation of healthcare interventions often rely on health economic model results. Such decisions affect resource allocation, patient outcomes and future healthcare choices. To ensure optimal decisions, assessing the validity of health economic models may be crucial. Validation involves much more than identifying (and hopefully correcting) errors in the model implementation. It also includes assessing the conceptual validity of the model and validation of the model input data, and checking whether the model’s predictions align sufficiently well with real-world data. In the context of health economics, validation can be defined as “the act of evaluating whether a model is a proper and sufficient representation of the system it is intended to represent in view of an application”, meaning that the model complies with what is known about the system and its outcomes provide a robust basis for decision making.[...]Validation of health economic models should be seen as a critical component of evidence-based decision making in healthcare. However, as of today, it still faces several important challenges, including the lack of consensus guidance and standardised procedures, the need for greater rigour or the question of who should oversee the validation process. To address these challenges, we encourage model developers, agencies requiring models for their decision making and editors of journals that publish models to recommend the use of state-of-the-art tools for reporting (and conducting) validations of health economic models, such as those mentioned in this editorial

Mapping Chronic Disease Prevalence based on Medication Use and Socio-demographic variables: an Application of LASSO in healthcare in the Netherlands

BACKGROUND: Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. METHODS: Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. RESULTS: Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. CONCLUSION: Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10754-4

Life years lost for users of specialized mental healthcare

Background: Mental disorders are burdensome and are associated with increased mortality. Mortality has been researched for various mental disorders, especially in countries with national registries, including the Nordic countries. Yet, knowledge gaps exist around national differences, while also relatively less studies compare mortality of those seeking help for mental disorders in specialized mental healthcare (SMH) by diagnosis. Additional insight into such mortality distributions for SMH users would be beneficial for both policy and research purposes. We aim to describe and compare the mortality in a population of SMH users with the mortality of the general population. Additionally, we aim to investigate mortality differences between sexes and major diagnosis categories: anxiety, depression, schizophrenia spectrum and other psychotic disorders, and bipolar disorder.Methods: Mortality and basic demographics were available for a population of N = 10,914 SMH users in the north of The Netherlands from 2010 until 2017. To estimate mortality over the adult lifespan, parametric Gompertz distributions were fitted on observed mortality using interval regression. Life years lost were computed by calculating the difference between integrals of the survival functions for the general population and the study sample, thus correcting for age. Survival for the general population was obtained from Statistics Netherlands (CBS).Results: SMH users were estimated to lose 9.5 life years (95% CI: 9.4–9.6). Every major diagnosis category was associated with a significant loss of life years, ranging from 7.2 (95% CI: 6.4–7.9) years for anxiety patients to 11.7 (95% CI: 11.0–12.5) years for bipolar disorder patients. Significant differences in mortality were observed between male SMH users and female SMH users, with men losing relatively more life years: 11.0 (95% CI: 10.9–11.2) versus 8.3 (95% CI: 8.2–8.4) respectively. This difference was also observed between sexes within every diagnosis, although the difference was insignificant for bipolar disorder. Conclusion: There were significant differences in mortality between SMH users and the general population. Substantial differences were observed between sexes and between diagnoses. Additional attention is required, and possibly specific interventions are needed to reduce the amount of life years lost by SMH users.</p

Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation

<p>Abstract</p> <p>Background</p> <p>To support health policy makers in setting priorities, quantifying the potential effects of tobacco control on the burden of disease is useful. However, smoking is related to a variety of diseases and the dynamic effects of smoking cessation on the incidence of these diseases differ. Furthermore, many people who quit smoking relapse, most of them within a relatively short period.</p> <p>Methods</p> <p>In this paper, a method is presented for calculating the effects of smoking cessation interventions on disease incidence that allows to deal with relapse and the effect of time since quitting. A simulation model is described that links smoking to the incidence of 14 smoking related diseases. To demonstrate the model, health effects are estimated of two interventions in which part of current smokers in the Netherlands quits smoking.</p> <p>To illustrate the advantages of the model its results are compared with those of two simpler versions of the model. In one version we assumed no relapse after quitting and equal incidence rates for all former smokers. In the second version, incidence rates depend on time since cessation, but we assumed still no relapse after quitting.</p> <p>Results</p> <p>Not taking into account time since smoking cessation on disease incidence rates results in biased estimates of the effects of interventions. The immediate public health effects are overestimated, since the health risk of quitters immediately drops to the mean level of all former smokers. However, the long-term public health effects are underestimated since after longer periods of time the effects of past smoking disappear and so surviving quitters start to resemble never smokers. On balance, total health gains of smoking cessation are underestimated if one does not account for the effect of time since cessation on disease incidence rates. Not taking into account relapse of quitters overestimates health gains substantially.</p> <p>Conclusion</p> <p>The results show that simulation models are sensitive to assumptions made in specifying the model. The model should be specified carefully in accordance with the questions it is supposed to answer. If the aim of the model is to estimate effects of smoking cessation interventions on mortality and morbidity, one should include relapse of quitters and dependency on time since cessation of incidence rates of smoking-related chronic diseases. A drawback of such models is that data requirements are extensive.</p

Association between lung function and exacerbation frequency in patients with COPD

To quantify the relationship between severity of chronic obstructive pulmonary disease (COPD) as expressed by Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage and the annual exacerbation frequency in patients with COPD. We performed a systematic literature review to identify randomized controlled trials and cohort studies reporting the exacerbation frequency in COPD patients receiving usual care or placebo. Annual frequencies were determined for total exacerbations defined by an increased use of health care (event-based), total exacerbations defined by an increase of symptoms, and severe exacerbations defined by a hospitalization. The association between the mean forced expiratory volume in one second (FEV(1))% predicted of study populations and the exacerbation frequencies was estimated using weighted log linear regression with random effects. The regression equations were applied to the mean FEV(1)% predicted for each GOLD stage to estimate the frequency per stage. Thirty-seven relevant studies were found, with 43 reports of total exacerbation frequency (event-based, n = 19; symptom-based, n = 24) and 14 reports of frequency of severe exacerbations. Annual event-based exacerbation frequencies per GOLD stage were estimated at 0.82 (95% confidence interval 0.46-1.49) for mild, 1.17 (0.93-1.50) for moderate, 1.61 (1.51-1.74) for severe, and 2.10 (1.51-2.94) for very severe COPD. Annual symptom-based frequencies were 1.15 (95% confidence interval 0.67-2.07), 1.44 (1.14-1.87), 1.76 (1.70-1.88), and 2.09 (1.57-2.82), respectively. For severe exacerbations, annual frequencies were 0.11 (95% confidence interval 0.02-0.56), 0.16 (0.07-0.33), 0.22 (0.20-0.23), and 0.28 (0.14-0.63), respectively. Study duration or type of study (cohort versus trial) did not significantly affect the outcomes. This study provides an estimate of the exacerbation frequency per GOLD stage, which can be used for health economic and modeling purposes

Real-World Treatment Costs and Care Utilization in Patients with Major Depressive Disorder With and Without Psychiatric Comorbidities in Specialist Mental Healthcare

BACKGROUND: The majority of patients with major depressive disorder (MDD) have comorbid mental conditions. OBJECTIVES: Since most cost-of-illness studies correct for comorbidity, this study focuses on mental healthcare utilization and treatment costs in patients with MDD including psychiatric comorbidities in specialist mental healthcare, particularly patients with a comorbid personality disorder (PD). METHODS: The Psychiatric Case Register North Netherlands contains administrative data of specialist mental healthcare providers. Treatment episodes were identified from uninterrupted healthcare use. Costs were calculated by multiplying care utilization with unit prices (price level year: 2018). Using generalized linear models, cost drivers were investigated for the entire cohort. RESULTS: A total of 34,713 patients had MDD as a primary diagnosis over the period 2000–2012. The number of patients with psychiatric comorbidities was 24,888 (71.7%), including 13,798 with PD. Costs were highly skewed, with an average ± standard deviation cost per treatment episode of €21,186 ± 74,192 (median €2320). Major cost drivers were inpatient days and daycare days (50 and 28% of total costs), occurring in 12.7 and 12.5% of episodes, respectively. Compared with patients with MDD only (€11,612), costs of patients with additional PD and with or without other comorbidities were, respectively, 2.71 (p < .001) and 2.06 (p < .001) times higher and were 1.36 (p < .001) times higher in patients with MDD and comorbidities other than PD. Other cost drivers were age, calendar year, and first episodes. CONCLUSIONS: Psychiatric comorbidities (especially PD) in addition to age and first episodes drive costs in patients with MDD. Knowledge of cost drivers may help in the development of future stratified disease management programs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40273-021-01012-x