Future Medical Costs Research Articles

The Impact of Unrelated Future Medical Costs on Economic Evaluation Outcomes for Different Models of Diabetes.

This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus. Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes. The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations. For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations.

Cost-Effectiveness of Fractional Flow Reserve–Guided Treatment for Acute Myocardial Infarction and Multivessel Disease

Complete revascularization by non-infarct-related artery (IRA) percutaneous coronary intervention (PCI) in patients with acute myocardial infarction is standard practice to improve patient prognosis. However, it is unclear whether a fractional flow reserve (FFR)-guided or angiography-guided treatment strategy for non-IRA PCI would be more cost-effective. To evaluate the cost-effectiveness of FFR-guided compared with angiography-guided PCI in patients with acute myocardial infarction and multivessel disease. In this prespecified cost-effectiveness analysis of the FRAME-AMI randomized clinical trial, patients were randomly allocated to either FFR-guided or angiography-guided PCI for non-IRA lesions between August 19, 2016, and December 24, 2020. Patients were aged 19 years or older, had ST-segment elevation myocardial infarction (STEMI) or non-STEMI and underwent successful primary or urgent PCI, and had at least 1 non-IRA lesion (diameter stenosis >50% in a major epicardial coronary artery or major side branch with a vessel diameter of ≥2.0 mm). Data analysis was performed on August 27, 2023. Fractional flow reserve-guided vs angiography-guided PCI for non-IRA lesions. The model simulated death, myocardial infarction, and repeat revascularization. Future medical costs and benefits were discounted by 4.5% per year. The main outcomes were quality-adjusted life-years (QALYs), direct medical costs, incremental cost-effectiveness ratio (ICER), and incremental net monetary benefit (INB) of FFR-guided PCI compared with angiography-guided PCI. State-transition Markov models were applied to the Korean, US, and European health care systems using medical cost (presented in US dollars), utilities data, and transition probabilities from meta-analysis of previous trials. The FRAME-AMI trial randomized 562 patients, with a mean (SD) age of 63.3 (11.4) years. Most patients were men (474 [84.3%]). Fractional flow reserve-guided PCI increased QALYs by 0.06 compared with angiography-guided PCI. The total cumulative cost per patient was estimated as $1208 less for FFR-guided compared with angiography-guided PCI. The ICER was -$19 484 and the INB was $3378, indicating that FFR-guided PCI was more cost-effective for patients with acute myocardial infarction and multivessel disease. Probabilistic sensitivity analysis showed consistent results and the likelihood iteration of cost-effectiveness in FFR-guided PCI was 97%. When transition probabilities from the pairwise meta-analysis of the FLOWER-MI and FRAME-AMI trials were used, FFR-guided PCI was more cost-effective than angiography-guided PCI in the Korean, US, and European health care systems, with an INB of $3910, $8557, and $2210, respectively. In probabilistic sensitivity analysis, the likelihood iteration of cost-effectiveness with FFR-guided PCI was 85%, 82%, and 31% for the Korean, US, and European health care systems, respectively. This cost-effectiveness analysis suggests that FFR-guided PCI for non-IRA lesions saved medical costs and increased quality of life better than angiography-guided PCI for patients with acute myocardial infarction and multivessel disease. Fractional flow reserve-guided PCI should be considered in determining the treatment strategy for non-IRA stenoses in these patients. ClinicalTrials.gov Identifier: NCT02715518.

Context-Specific Estimation of Future Unrelated Medical Costs and Their Impact on Cost-Effectiveness Analyses.

This study constructed and applied procedures for the estimation of unrelated future medical costs (UFMC) of women with breast cancer in Israel (as a case study) and examined the influence of including UFMC in cost-effectiveness analyses (CEAs). Part I consisted of a retrospective cohort study based on patient-level claims data of both patients with breast cancer and matched controls during 14 years of follow-up. UFMC were estimated as (a) the annual average all-cause healthcare costs of the control subjects, and (b) as predicted values based on a generalized linear model (GLM) adjusted to patients' characteristics. Part II consisted of a CEA performed using a Markov simulation model comparing regimens of chemotherapy with/without trastuzumab, both excluding and including UFMC and for each of the UFMC estimates separately. All costs were adjusted to 2019 prices. Costs and QALYs were discounted at a yearly rate of 3%. The average annual healthcare costs in the control group were $2328 (± $5662). The corresponding incremental cost-effectiveness ratio (ICER) was $53,411/QALY and $55,903/QALY, when UFMC were excluded or included, respectively. Hence, trastuzumab was not considered cost-effective compared with a threshold of willingness-to-pay of $37,000 per QALY, regardless of the inclusion of UFMC. When UFMC were estimated on the basis of the prediction model, the ICERs were $37,968/QALY and $39,033/QALY, when UFMC were excluded or included, respectively. Thus, in this simulation, trastuzumab was not considered cost-effective, independent of the inclusion of UFMC. Our case study revealed that the inclusion of UFMC had modest effect on the ICERs, and thus did not alter the conclusion. Thus, we should estimate context-specific UFMC if they are expected to change the ICERs significantly, and transparently report the corresponding assumptions to uphold the integrity and reliability of the economic evaluation.

Prediction of Future Medical Costs by Modifiable Measures of Health.

Strategies to mitigate rising health-care costs are a priority for patients, employers, and health insurers. Yet gaps currently exist in whether health risk assessment can forecast medical claims costs. This study examined the ability of a health quotient (HQ) based on modifiable risk factors, age, sex, and chronic conditions to predict future medical claims spending. The study included 18,695 employees and adult dependents who participated in health assessments and were enrolled in an employer-sponsored health plan. Linear mixed effect models stratified by chronic conditions and adjusted for age and sex were utilized to evaluate the relationship between the health quotient (score of 0-100) and future medical claims spending. Lower baseline health quotient was associated with higher medical claims cost over 2 years of follow up. For participants with chronic condition(s), costs were $3628 higher for those with a low health quotient (<73; N = 2673) compared to those with high health quotient (>85; N = 1045), after adjustment for age and sex (P value = 0.004). Each one-unit increase in health quotient was associated with a decrease of $154 (95% CI: 87.4, 220.3) in average yearly medical claims costs during follow up. This study used a large employee population with 2 years of follow-up data, which provides insights that are applicable to other large employers. Results of this analysis contribute to our ability to predict health-care costs using modifiable aspects of health, objective laboratory testing and chronic condition status.

EE517 The Potential Effect of Future Medical Cost Changes on the Net Cost of Multi-Cancer Early Detection Testing

Multi-cancer early detection (MCED) testing along with standard of care (SoC) could improve survival outcomes and lower treatment costs but increase screening costs. Spending on cancer therapeutics in the US is projected to increase >10% annually over the next 5 years, while cost of lab testing has generally remained flat or decreased over time. This study explores the effect of the differential in future cost changes between cancer treatment and MCED testing on the economic impact of MCED testing.

SUDs Cost Employer Health Insurance $35 Billion Per Year

SUDs Cost Employer Health Insurance $35 Billion Per Year

Cost-effectiveness of Sodium-Glucose Cotransporter-2 Inhibitors for the Treatment of Heart Failure With Preserved Ejection Fraction

Adding a sodium-glucose cotransporter-2 inhibitor (SGLT2-I) to standard-of-care treatment in patients with heart failure with preserved ejection fraction (HFpEF) reduces the risk of a composite outcome of worsening heart failure or cardiovascular mortality, but the cost-effectiveness in US patients with HFpEF is uncertain. To evaluate the lifetime cost-effectiveness of standard therapy plus an SGLT2-I compared with standard therapy in individuals with HFpEF. In this economic evaluation conducted from September 8, 2021, to December 12, 2022, a state-transition Markov model simulated monthly health outcomes and direct medical costs. Input parameters including hospitalization rates, mortality rates, costs, and utilities were extracted from HFpEF trials, published literature, and publicly available data sets. The base-case annual cost of SGLT2-I was $4506. A simulated cohort with similar characteristics as participants of the Empagliflozin in Heart Failure With a Preserved Ejection Fraction (EMPEROR-Preserved) and Dapagliflozin in Heart Failure With Mildly Reduced or Preserved Ejection Fraction (DELIVER) trials was used. Standard of care plus SGLT2-I vs standard of care. The model simulated hospitalizations, urgent care visits, and cardiovascular and noncardiovascular death. Future medical costs and benefits were discounted by 3% per year. Main outcomes were quality-adjusted life-years (QALYs), direct medical costs (2022 US dollars), and incremental cost-effectiveness ratio (ICER) of SGLT2-I therapy from a US health care sector perspective. The ICER of SGLT2-I therapy was evaluated according to the American College of Cardiology/American Heart Association value framework (high value: <$50 000; intermediate value: $50 000 to <$150 000; and low value: ≥$150 000). The simulated cohort had a mean (SD) age of 71.7 (9.5) years and 6828 of 12 251 participants (55.7%) were male. Standard of care plus SGLT2-I increased quality-adjusted survival by 0.19 QALYs at an increased cost of $26 300 compared with standard of care. The resulting ICER was $141 200 per QALY gained, with 59.1% of 1000 probabilistic iterations indicating intermediate value and 40.9% indicating low value. The ICER was most sensitive to SGLT2-I costs and effect of SGLT2-I therapy on cardiovascular death (eg, increasing to $373 400 per QALY gained if SGLT2-I therapy was assumed to have no effect on mortality). Results of this economic evaluation suggest that at 2022 drug prices, adding an SGLT2-I to standard of care was of intermediate or low economic value compared with standard of care in US adults with HFpEF. Efforts to expand access to SGLT2-I for individuals with HFpEF should be coupled with efforts to lower the cost of SGLT2-I therapy.

Status of Health Economics Research on Cognitive Behavioral Therapy for Insomnia

Cognitive behavioral therapy for insomnia (CBT-I) is a first-line treatment for insomnia disorder. Herein, we reviewed the currently prevailing forms of application of CBT-I and the corresponding studies on their health economics, comparing them with medication treatment. At present, most of the findings suggest that CBT-I has long-lasting effects and can help reduce future medical costs, thus showing long-term economic advantages. At present, there are no relevant research reports on the economic benefits of CBT-I in China. This review could provide reference for relevant studies to be conducted in the future in China. In addition, it provides reference from an economic perspective for clinicians and patients and facilitates their decision-making concerning using CBT-I as a treatment option for insomnia in the future.

Development and Evaluation of Machine Learning-Based High-Cost Prediction Model Using Health Check-Up Data by the National Health Insurance Service of Korea.

In this study, socioeconomic, medical treatment, and health check-up data from 2010 to 2017 of the National Health Insurance Service (NHIS) of Korea were analyzed. This year’s socioeconomic, treatment, and health check-up data are used to develop a predictive model for high medical expenses in the next year. The characteristic of this study is to derive important variables related to the high cost of domestic medical expenses users by using data on health check-up items conducted by the country. In this study, we tried to classify data and evaluate its performance using classification supervised learning algorithms for high-cost medical expense prediction. Supervised learning for predicting high-cost medical expenses was performed using the logistic regression model, random forest, and XGBoost, which have been known to result the best performance and explanatory power among the machine learning algorithms used in previous studies. Our experimental results show that the XGBoost model had the best performance with 77.1% accuracy. The contribution of this study is to identify the variables that affect the prediction of high-cost medical expenses by analyzing the medical bills using the health check-up variables and the Korea Classification Disease (KCD) large group as input variables. Through this study, it was confirmed that musculoskeletal disorders (M) and respiratory diseases (J), which are the most frequently treated diseases, as important KCD disease groups for high-cost prediction in Korea, affect the future high cost prediction. In addition, it was confirmed that malignant neoplasia diseases (C) with high medical cost per treatment are a group of diseases related to high future medical cost prediction. Unlike previous studies, it is the result of analyzing all disease data, so it is expected that the study will be more meaningful when compared with the results of other national health check-up data.

Future medical and non-medical costs and their impact on the cost-effectiveness of life-prolonging interventions: a comparison of five European countries

When healthcare interventions prolong life, people consume medical and non-medical goods during the years of life they gain. It has been argued that the costs for medical consumption should be included in cost-effectiveness analyses from both a healthcare and societal perspective, and the costs for non-medical consumption should additionally be included when a societal perspective is applied. Standardized estimates of these so-called future costs are available in only a few countries and the impact of inclusion of these costs is likely to differ between countries. In this paper we present and compare future costs for five European countries and estimate the impact of including these costs on the cost-effectiveness of life-prolonging interventions. As countries differ in the availability of data, we illustrate how both individual- and aggregate-level data sources can be used to construct standardized estimates of future costs. Results show a large variation in costs between countries. The medical costs for the Netherlands, Germany, and the United Kingdom are large compared to Spain and Greece. Non-medical costs are higher in Germany, Spain, and the United Kingdom than in Greece. The impact of including future costs on the ICER similarly varied between countries, ranging from €1000 to €35,000 per QALY gained. The variation between countries in impact on the ICER is largest when considering medical costs and indicate differences in both structure and level of healthcare financing in these countries. Case study analyses were performed in which we highlight the large impact of including future costs on ICER relative to willingness-to-pay thresholds.

Examining the Relationship between Climate Change and Vibriosis in the United States: Projected Health and Economic Impacts for the 21st Century.

Background:This paper represents, to our knowledge, the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through food- and waterborne exposures to various Vibrio species (e.g., nonV. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life, such as shellfish and finfish.Objectives:The objective of this study was to project climate-induced changes in vibriosis and associated economic impacts in the United States related to changes in sea surface temperatures (SSTs).Methods:For our analysis to identify climate links to vibriosis incidence, we constructed three logistic regression models by Vibrio species, using vibriosis data sourced from the Cholera and Other Vibrio Illness Surveillance system and historical SSTs. We relied on previous estimates of the cost-per-case of vibriosis to estimate future total annual medical costs, lost income from productivity loss, and mortality-related indirect costs throughout the United States. We separately reported results for V. parahaemolyticus, V. vulnificus, V. alginolyticus, and “V. spp.,” given the different associated health burden of each.Results:By 2090, increases in SST are estimated to result in a 51% increase in cases annually relative to the baseline era (centered on 1995) under Representative Concentration Pathway (RCP) 4.5, and a 108% increase under RCP8.5. The cost of these illnesses is projected to reach annually under RCP4.5, and annually under RCP8.5, relative to in the baseline (2018 U.S. dollars), equivalent to 140% and 234% increases respectively.Discussion:Vibriosis incidence is likely to increase in the United States under moderate and unmitigated climate change scenarios through increases in SST, resulting in a substantial burden of morbidity and mortality, and costing billions of dollars. These costs are mostly attributable to deaths, primarily from exposure to V. vulnificus. Evidence suggests that other factors, including sea surface salinity, may contribute to further increases in vibriosis cases in some regions of the United States and should also be investigated. https://doi.org/10.1289/EHP9999a

Internet of things based electrocardiogram monitoring system using machine learning algorithm

&lt;span&gt;In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.&lt;/span&gt;

RWD4 Impact of Therapeutic Inertia on Healthcare Outcomes for Patients with Type 2 Diabetes Mellitus

Therapeutic inertia is the failure of health-care providers to initiate or intensify therapy when therapeutic goals are not reached. Few studies have compared insulin initiation delay times with their expected negative effect on health outcomes. Our study quantifies the effects of delaying the start of insulin for patients with Type 2 Diabetes Mellitus (T2DM) on future A1c values, medical costs, and development of new T2D-related complications.

Primary Care Visit Cadence Based on Risk: A Comparison of Machine Learning and Provider Judgment

Summary More frequent primary care visits are associated with improvements in health outcomes and overall well-being and a reduction in health care disparities. Visit cadence is a core part of Oak Street Health’s value-based care model. Although its providers are generally good at predicting a patient’s risk of mortality, they are less accurate at predicting nonmortality outcomes, and they may not update each patient’s risk level in a timely manner. Oak Street launched a pilot initiative to assess whether machine-learning models could outperform provider visit cadence assignment in predicting three key outcomes: acute inpatient admissions, medical cost, and mortality. Pilot results demonstrated that the models were more accurate than provider judgment alone at identifying patients’ risk of admission and future medical cost. On the basis of these positive findings, Oak Street deployed the machine-learning tiering across the organization, with more than 90% of all eligible patients assigned a tier that is routinely refreshed using the predictive models.

Incorporating future unrelated medical costs in cost-effectiveness analysis in China

The occurrence of future unrelated medical costs is a direct consequence of life-prolonging interventions, but most pharmacoeconomic guidelines recommend the exclusion of these costs. The Chinese guidelines were updated in...

Don’t forget about the future: The impact of including future costs on the cost-effectiveness of adult pneumococcal conjugate vaccination with PCV13 in the Netherlands

BackgroundWhen vaccines increase longevity, vaccinated people may experience costs and benefits during added life-years. These future benefits and costs may include increased productivity as well as medical and non-medical costs. Such impacts should be considered in cost-effectiveness analyses (CEA) of vaccines but are often omitted. Here, we illustrate the impact of including future costs on the cost-effectiveness of vaccination against pneumococcus disease. We emphasize the relevance of differentiating cost estimates between risk groups. MethodsWe updated an existing Dutch CEA of vaccination against pneumococcus disease with the 13-valent pneumococcal conjugate vaccine (PCV13) to include all future medical and non-medical costs. We linked costs by age and risk with survival information and estimates of cases prevented per vaccination strategy based on the original study to calculate the impact of inclusion. Future medical costs were adjusted for relevant risk groups. ResultsFor the base-case strategy, the original incremental cost-effectiveness ratio (ICER) of PVC13 was €9,157 per quality adjusted life-year (QALY). Including all future medical costs increased the ICER to €28,540 per QALY. Also including future non-medical costs resulted in an ICER of €45,691 per QALY. The impact of future medical costs varied considerably per risk group and generally increased with age. Discussion and conclusionThis study showed a substantial effect of the inclusion of future costs on the ICER of vaccinating with PCV13. Especially when lives of people with underlying health conditions are extended, the impact of future medical costs is large. This inclusion may make vaccination a less attractive option, especially in relation to low thresholds as often applied for prevention. Although this raises important questions, ignoring these real future costs may lead to an inefficient use of healthcare resources. Our results may imply that prices for some vaccines need to be lowered to be cost-effective.

Cost-Effectiveness of Four Financial Incentive Programs for Smoking Cessation.

Rationale: A trial of four financial incentive programs, conducted at CVS Caremark, a large employer, documented their effectiveness in promoting sustained abstinence from smoking, but their cost-effectiveness is unknown, and the significant up-front cost of the incentives is a deterrent to their adoption. Objectives: To determine the cost-effectiveness of these incentives from the healthcare sector and employer perspectives. Methods: This study examines a decision model built with trial data, supplemented by data from the literature. Life-expectancy gains for quitters were projected on the basis of U.S. life tables. The two individual-oriented programs paid $800 for smoking cessation at 6 months; one required participants to deposit $150 at baseline. Payments in the two group-oriented programs varied with the group's success; again, one required participants to deposit $150. Results: Life-years, quality-adjusted life-years (QALYs), costs (2012 dollars), and cost-effectiveness ratios are described. From the healthcare sector perspective, costs ranged from $3,200 per life-year ($2,500 per QALY) for the competitive deposit program, compared with usual care, to $6,500 per life-year ($5,100 per QALY) for the individual reward program. From the employer perspective, costs ranged from $256,600 per life-year gained for the individual deposit program to $1,711,100 per life-year gained for the individual reward program; the cost per QALY ranged from $65,300 for the competitive deposit program to $128,800 for the individual reward program. Cost-effectiveness from the employer perspective improved with longer decision horizons. Including future medical costs reduced cost-effectiveness from both perspectives. Conclusions: Four financial incentive programs that paid smokers to quit are very cost-effective from the healthcare sector perspective. They are more expensive from the employer perspective but may be cost-effective for employers with longer decision horizons.

Counting the Costs of COVID-19: Why Future Treatment Option Values Matter

I critique a recent analysis (Miles, Stedman &amp; Heald, 2020) of COVID-19 lockdown costs and benefits, focussing on the United Kingdom (UK). Miles et al. (2020) argue that the March-June UK lockdown was more costly than the benefit of lives saved, evaluated using the NICE threshold of £30000 for a quality-adjusted life year (QALY) and that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than any plausible estimate of the benefits, even if easing produced a second infection wave causing over 7000 deaths weekly by mid-September. I note here two key problems that significantly affect their estimates and cast doubt on their conclusions. Firstly, their calculations arbitrarily cut off after 13 weeks, without costing the epidemic end state. That is, they assume indifference between mid-September states of 13 or 7500 weekly deaths and corresponding infection rates. This seems indefensible unless one assumes that (a) there is little chance of any effective vaccine or improved medical or social interventions for the foreseeable future, (b) notwithstanding temporary lockdowns, COVID-19 will very likely propagate until herd immunity. Even under these assumptions it is very questionable. Secondly, they ignore the costs of serious illness, possible long-term lowering of life quality and expectancy for survivors. These are uncertain, but plausibly at least as large as the costs in deaths.In summary, policy on tackling COVID-19 cannot be rationally made without estimating probabilities of future medical interventions and long-term illness costs. More work on modelling these uncertainties is urgently needed.

Assessing the future medical cost burden for the European health systems under alternative exposure-to-risks scenarios.

BackgroundAgeing populations and rising prevalence of non-communicable diseases (NCDs) increasingly contribute to the growing cost burden facing European healthcare systems. Few studies have attempted to quantify the future magnitude of this burden at the European level, and none of them consider the impact of potential changes in risk factor trajectories on future health expenditures.MethodsThe new microsimulation model forecasts the impact of behavioural and metabolic risk factors on NCDs, longevity and direct healthcare costs, and shows how changes in epidemiological trends can modify those impacts. Economic burden of NCDs is modelled under three scenarios based on assumed future risk factors trends: business as usual (BAU); best case and worst case predictions (BCP and WCP).FindingsThe direct costs of NCDs in the EU 27 countries and the UK (in constant 2014 prices) will grow under all scenarios. Between 2014 and 2050, the overall healthcare spending is expected to increase by 0.8% annually under BAU. In the all the countries, 605 billion Euros can be saved by 2050 if BCP is realized compared to the BAU, while excess spending under the WCP is forecast to be around 350 billion. Interpretation: Although the savings realised under the BCP can be substantial, population ageing is a stronger driver of rising total healthcare expenditures in Europe compared to scenario-based changes in risk factor prevalence.

Cured Today, Ill Tomorrow: A Method for Including Future Unrelated Medical Costs in Economic Evaluation in England and Wales.

In many countries, future unrelated medical costs occurring during life-years gained are excluded from economic evaluation, and benefits of unrelated medical care are implicitly included, leading to life-extending interventions being disproportionately favored over quality of life-improving interventions. This article provides a standardized framework for the inclusion of future unrelated medical costs and demonstrates how this framework can be applied in England and Wales. Data sources are combined to construct estimates of per-capita National Health Service spending by age, sex, and time to death, and a framework is developed for adjusting these estimates for costs of related diseases. Using survival curves from 3 empirical examples illustrates how our estimates for unrelated National Health Service spending can be used to include unrelated medical costs in cost-effectiveness analysis and the impact depending on age, life-years gained, and baseline costs of the target group. Our results show that including future unrelated medical costs is feasible and standardizable. Empirical examples show that this inclusion leads to an increase in the ICER of between 7% and 13%. This article contributes to the methodology debate over unrelated costs and how to systematically include them in economic evaluation. Results show that it is both important and possible to include future unrelated medical costs.