Individualized cost-effectiveness analysis

Understanding healthcare efficiency-an AI-supported narrative review of diverse terminologies used

BACKGROUND

Physicians have become more responsible for pursuing healthcare efficiency. However, contemporary literature uses multiple terminologies to describe healthcare efficiency. To identify which term is best suitable for medical education to equip physicians to contribute to healthcare efficiency delivery in clinical practice, we performed a narrative review to elucidate these terms' meanings, commonalities, and differences.

METHODS

The PubMed-database was searched for articles published in 2019-2024 describing healthcare efficiency terminology. Eligible articles conceptually described and applied relevant terminologies for physicians, while empirical studies and practice-specific articles were excluded. The screening was supported by an open-source artificial intelligence tool (ASReview), which prioritizes articles through machine learning. Two reviewers independently screened the resulting articles, resolving disagreements by consensus. Final eligibility was determined through predefined inclusion criteria.

RESULTS

Out of 3,655 articles identified, 26 met the inclusion criteria. Key terminologies: cost-effectiveness, high-value care, low-value care, and value-based healthcare, were identified, and explored into more depth. 'Value' is central in all terms, but our findings reveal that the perspectives herein differ on what constitutes value. Within cost-effectiveness, resource allocation to the population's needs drives decision-making-maximizing value at population-level. Within value-based healthcare, patient-centricity guides decision-making-maximizing value at individual patient-level. High-value and low-value care are somewhat ambiguous, depending solely on cost-effectiveness results or patient preferences to determine whether care is considered as low or high value.

CONCLUSIONS

Cost-effectiveness may be too rigid for patient-physician interactions, while value-based healthcare might not ensure sustainable care. As physicians are both stewards of finite societal resources and advocates of individual patients, integrating cost-effectiveness (resource allocation for population needs) and value-based healthcare (individualized care plans) seems necessary. Both terms emphasize delivering high-value care and avoiding low-value care. We suggest that medical education: (1) train (future) physicians to apply healthcare efficiency principles through case-based discussion, (2) use the cost-effectiveness plane to evaluate treatments, (3) deepen knowledge of diagnostic and treatment procedures' costs within evidence-based guidelines, and (4) enhance communication skills supporting a healthcare efficiency-driven open shared decision-making with patients.

Incorporating Economic Evidence in Clinical Guidelines: A Framework From the Clinical Guidelines Committee of the American College of Physicians

In recognition of accelerating health care spending and alignment with the American College of Physicians (ACP) principles of promoting high-value care, the ACP Clinical Guidelines Committee (CGC) developed a framework to standardize its approach to identifying, appraising, and considering economic evidence in the development of ACP clinical guidelines. This article presents the CGC's process for incorporating economic evidence, which encompasses cost-effectiveness analyses, economic outcomes in randomized controlled trials, and resource utilization (intervention cost) data. Economic evidence is one component of ACP recommendations. The CGC first and foremost assesses the certainty of evidence for clinical net benefit of interventions; it then considers patient values and preferences, and only then considers economic evidence to develop recommendations.

Minimal differences observed when comparing the morphological profiling of microglia obtained by confocal laser scanning and optical sectioning microscopy

Background

While widefield microscopy has long been constrained by out-of-focus scattering, advancements have generated a solution in the form of confocal laser scanning microscopy (cLSM) and optical sectioning microscopy using structured illumination (OSM). In this study, we aim to investigate, using microglia branching, if cLSM and OSM can produce images with comparable morphological characteristics.

Results

By imaging the somatosensory microglia from a tissue slice of a 3-week-old mouse and establishing morphological parameters that characterizes the microglial branching pattern, we were able to show that there is no difference in total length of the branch tree, number of branches, mean branch length and number of primary to terminal branches. We did find that area-based parameters such as mean occupied area and mean surveillance area were bigger in cLSM isolated microglia compared to OSM ones. Additionally, by investigating the difference in acquisition time between techniques and personal costs we were able to establish that the amortization could be made in 6.11 ± 2.93 years in the case of countries with a Human Development Index (HDI) = 7-9 and 7.06 ± 3.13 years, respectably, for countries with HDI < 7. As such, OSM systems seem a valid option if one just wants basic histological evaluation, and cLSM should be considered for groups that demand higher resolution or volumetric images.

Challenges around quantifying uncertainty in a holistic approach to hard-to-heal wound management: Health economic perspective

Treatment of hard-to-heal wounds involves a holistic approach for choosing between available treatment options. However, evidence for informing these choices is sparse, introducing uncertainty into decisions about the optimum treatment pathways that reflect the vast heterogeneity in this patient population. This paper discusses the existing clinical and health economic literature in order to provide insight into sources of uncertainty in the evaluation of the holistic approach to management of the hard-to-heal wounds, and how this uncertainty can be appropriately reflected in research. We identified three key sources of uncertainty in the evaluation of chronic wound treatments, namely heterogeneity in aetiology and patient populations, heterogeneity in treatment pathways, and challenges around capturing all relevant outcomes. Reflecting these complexities requires sophisticated modelling of treatment sequencing and long-term outcomes. The paper discusses how the scope specification, scenario analyses, and sensitivity analyses can be used to fully characterise analytical uncertainty.

The Online Elicitation of Personal Utility Functions (OPUF) tool: a new method for valuing health states

Introduction Standard valuation methods, such as TTO and DCE are inefficient. They require data from hundreds if not thousands of participants to generate value sets. Here, we present the Online elicitation of Personal Utility Functions (OPUF) tool; a new type of online survey for valuing EQ-5D-5L health states using more efficient, compositional elicitation methods, which even allow estimating value sets on the individual level. The aims of this study are to report on the development of the tool, and to test the feasibility of using it to obtain individual-level value sets for the EQ-5D-5L. Methods We applied an iterative design approach to adapt the PUF method, previously developed by Devlin et al., for use as a standalone online tool. Five rounds of qualitative interviews, and one quantitative pre-pilot were conducted to get feedback on the different tasks. After each round, the tool was refined and re-evaluated. The final version was piloted in a sample of 50 participants from the UK. A demo of the EQ-5D-5L OPUF survey is available at: https://eq5d5l.me Results On average, it took participants about seven minutes to complete the OPUF Tool. Based on the responses, we were able to construct a personal EQ-5D-5L value set for each of the 50 participants. These value sets predicted a participants' choices in a discrete choice experiment with an accuracy of 80%. Overall, the results revealed that health state preferences vary considerably on the individual-level. Nevertheless, we were able to estimate a group-level value set for all 50 participants with reasonable precision. Discussion We successfully piloted the OPUF Tool and showed that it can be used to derive a group-level as well as personal value sets for the EQ-5D-5L. Although the development of the online tool is still in an early stage, there are multiple potential avenues for further research.

COVID outcome prediction in the emergency department (COPE): using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

OBJECTIVES

Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19.

DESIGN

Retrospective.

SETTING

Secondary care in four large Dutch hospitals.

PARTICIPANTS

Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation.

OUTCOME MEASURES

We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots.

RESULTS

Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model-COVID outcome prediction in the emergency department (COPE)-with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)).

CONCLUSIONS

COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

Improving hospital readmission prediction using individualized utility analysis

OBJECTIVE

Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective was to determine whether ML models for allocating readmission-mitigating interventions have different usefulness based on their overall utility and discriminative ability.

MATERIALS AND METHODS

We conducted a retrospective utility analysis of ML models using claims data acquired from the Optum Clinformatics Data Mart, including 513,495 commercially-insured inpatients (mean [SD] age 69 [19] years; 294,895 [57%] Female) over the period January 2016 through January 2017 from all 50 states with mean 90 day cost of $11,552. Utility analysis estimates the cost, in dollars, of allocating interventions for lowering readmission risk based on the reduction in the 90-day cost.

RESULTS

Allocating readmission-mitigating interventions based on a GBDT model trained to predict readmissions achieved an estimated utility gain of $104 per patient, and an AUC of 0.76 (95% CI 0.76, 0.77); allocating interventions based on a model trained to predict cost as a proxy achieved a higher utility of $175.94 per patient, and an AUC of 0.62 (95% CI 0.61, 0.62). A hybrid model combining both intervention strategies is comparable with the best models on either metric. Estimated utility varies by intervention cost and efficacy, with each model performing the best under different intervention settings.

CONCLUSION

We demonstrate that machine learning models may be ranked differently based on overall utility and discriminative ability. Machine learning models for allocation of limited health resources should consider directly optimizing for utility.

Economic Considerations in Infectious Diseases Emergency Response Preparedness: It's All About the Point of View

Outbreaks and emergence of novel pathogens present a challenge in economic evaluations of prevention strategies, due to unusually high levels of risk aversion and uncertainty. Here, we discuss cost-effectiveness investigations and interpretation of economic analyses in the context of outbreak planning and containment, and outline considerations for providers, administrators, patients, and policy makers for infection emergency preparedness response.

How to assess the value of low-value care

Background

Many of the strategies designed to reduce “low-value care” have been implemented without a consensus on the definition of the term “value”. Most “low value care” lists are based on the comparative effectiveness of the interventions.

Main text

Defining the value of an intervention based on its effectiveness may generate an inefficient use of resources, as a very effective intervention is not necessarily an efficient intervention, and a low effective intervention is not always an inefficient intervention. The cost-effectiveness plane may help to differentiate between high and low value care interventions. Reducing low value care should include three complementary strategies: eliminating ineffective interventions that entail a cost; eliminating interventions whose cost is higher and whose effectiveness is lower than that of other options (quadrant IV); and eliminating interventions whose incremental or decremental cost-effectiveness is unacceptable in quadrants I and III, respectively. Defining low-value care according to the efficiency of the interventions, ideally at the level of subgroups and individuals, will contribute to develop true value-based health care systems.

Conclusion

Cost-effectiveness rather than effectiveness should be the main criterion to assess the value of health care services and interventions. Payment-for-value strategies should be based on the definition of high and low value provided by the cost-effectiveness plane.

Continuous Glucose Monitoring as a Matter of Justice

Type 1 diabetes (T1D) is a chronic illness that requires intensive lifelong management of blood glucose concentrations by means of external insulin administration. There have been substantial developments in the ways of measuring glucose levels, which is crucial to T1D self-management. Recently, continuous glucose monitoring (CGM) has allowed people with T1D to keep track of their blood glucose levels in near real-time. These devices have alarms that warn users about potentially dangerous blood glucose trends, which can often be shared with ther people. CGM is consistently associated with improved glycemic control and reduced hypoglycemia and is currently recommended by doctors. However, due to the costs of CGM, only those who qualify for hospital provision or those who can personally afford it are able to use it, which excludes many people. In this paper, I argue that unequal access to CGM results in: (1) unjust health inequalities, (2) relational injustice, (3) injustice with regard to agency and autonomy, and (4) epistemic injustice. These considerations provide prima facie moral reasons why all people with T1D should have access to CGM technology. I discuss the specific case of CGM policy in the Netherlands, which currently only provides coverage for a small group of people with T1D, and argue that, especially with additional considerations of cost-effectiveness, the Dutch government ought to include CGM in basic health care insurance for all people with T1D.

Scientific challenges for precision public health

The notion of ‘precision’ public health has been the subject of much debate, with recent articles coming to its defence following the publication of several papers questioning its value.

Critics of precision public health raise the following problems and questionable assumptions: the inherent limits of prediction for individuals; the limits of approaches to prevention that rely on individual agency, in particular the potential for these approaches to widen inequalities; the undue emphasis on the supposed new information contained in individuals’ molecules and their ‘big data’ at the expense of their own preferences for a particular intervention strategy and the diversion of resources and attention from the social determinants of health.

In order to refocus some of these criticisms of precision public health as scientific questions, this article outlines some of the challenges when defining risk for individuals; the limitations of current theory and study design for precision public health; and the potential for unintended harms.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

Economic Analysis of Hospital Palliative Care: Investigating Heterogeneity by Noncancer Diagnoses

Background. Single-disease-focused treatment and hospital-centric care are poorly suited to meet complex needs in an era of multimorbidity. Understanding variation in palliative care’s association with treatment choices is essential to optimizing interdisciplinary decision making in care of complex patients. Aim. To estimate the association between palliative care and hospital costs by primary diagnosis and multimorbidity for adults with one of six life-limiting conditions: heart failure, chronic obstructive pulmonary disease (COPD), liver failure, kidney failure, neurodegenerative conditions including dementia, and HIV/AIDS. Methods. Data from four studies (2002–2015) were pooled to provide an analytic dataset of 73,304 participants with mean costs $10,483, of whom 5,348 (7%) received palliative care. We estimated average effect of palliative care on direct hospital costs among the treated, using propensity scores to control for observed confounding. Results. Palliative care was associated with a statistically significant reduction in total direct costs for heart failure (estimated treatment effect: −$2666; 95% confidence interval [CI]: −$3440 to −$1892), neurodegenerative conditions (−$3523; −$4394 to −$2651), COPD (−$1613; −$2217 to −$1009), kidney failure (−$3589; −$5132 to −$2045), and liver failure (−$7574; −$9232 to −$5916). The association for liver failure patients was statistically significantly larger than for any other disease group. Cost-saving associations were also statistically larger for patients with multimorbidity than single disease for two of the six groups: neurodegenerative and liver failure. Conclusions. Heterogeneity in treatment effect estimates was observable in assessing association between palliative care and hospital costs for adults with serious life-limiting illnesses other than cancer. The results illustrate the importance of careful definition of palliative care populations in research and practice, and raise further questions about the role of interdisciplinary decision making in treatment of complex medical illness.

Incorporating Future Medical Costs: Impact on Cost-Effectiveness Analysis in Cancer Patients

BACKGROUND

The inclusion of future medical costs in cost-effectiveness analyses remains a controversial issue. The impact of capturing future medical costs is likely to be particularly important in patients with cancer where costly lifelong medical care is necessary. The lack of clear, definitive pharmacoeconomic guidelines can limit comparability and has implications for decision making.

OBJECTIVE

The aim of this study was to demonstrate the impact of incorporating future medical costs through an applied example using original data from a clinical study evaluating the cost effectiveness of a sepsis intervention in cancer patients.

METHODS

A decision analytic model was used to capture quality-adjusted life-years (QALYs) and lifetime costs of cancer patients from an Australian healthcare system perspective over a lifetime horizon. The evaluation considered three scenarios: (1) intervention-related costs (no future medical cost), (2) lifetime cancer costs and (3) all future healthcare costs. Inputs to the model included patient-level data from the clinical study, relative risk of death due to sepsis, cancer mortality and future medical costs sourced from published literature. All costs are expressed in 2017 Australian dollars and discounted at 5%. To further assess the impact of future costs on cancer heterogeneity, variation in survival and lifetime costs between cancer types and the implications for cost-effectiveness analysis were explored.

RESULTS

The inclusion of future medical costs increased incremental cost-effectiveness ratios (ICERs) resulting in a shift from the intervention being a dominant strategy (cheaper and more effective) to an ICER of $7526/QALY. Across different cancer types, longer life expectancies did not necessarily result in greater lifetime healthcare costs. Incremental costs differed across cancers depending on the respective costs of managing cancer and survivorship, thus resulting in variations in ICERs.

CONCLUSIONS

There is scope for including costs beyond intervention costs in economic evaluations. The inclusion of future medical costs can result in markedly different cost-effectiveness results, leading to higher ICERs in a cancer population, with possible implications for funding decisions.

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

BACKGROUND

Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level.

OBJECTIVE

This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation.

METHODS

Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment.

RESULTS

Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%).

CONCLUSIONS

Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

Qualitative process study to explore the perceived burdens and benefits of a digital intervention for self-managing high blood pressure in Primary Care in the UK

OBJECTIVES

Digital interventions can change patients' experiences of managing their health, either creating additional burden or improving their experience of healthcare. This qualitative study aimed to explore perceived burdens and benefits for patients using a digital self-management intervention for reducing high blood pressure. A secondary aim was to further our understanding of how best to capture burdens and benefits when evaluating health interventions.

DESIGN

Inductive qualitative process study nested in a randomised controlled trial.

SETTING

Primary Care in the UK.

PARTICIPANTS

35 participants taking antihypertensive medication and with uncontrolled blood pressure at baseline participated in semistructured telephone interviews.

INTERVENTION

Digital self-management intervention to support blood pressure self-monitoring and medication change when recommended by the healthcare professional.

ANALYSIS

Data were analysed using inductive thematic analysis with techniques from grounded theory.

RESULTS

Seven themes were developed which reflected perceived burdens and benefits of using the intervention, including worry about health, uncertainty about self-monitoring and reassurance. The analysis showed how beliefs about their condition and treatment appeared to influence participants' appraisal of the value of the intervention. This suggested that considering illness and treatment perceptions in Burden of Treatment theory could further our understanding of how individuals appraise the personal costs and benefits of self-managing their health.

CONCLUSIONS

Patients' appraisal of the burden or benefit of using a complex self-management intervention seemed to be influenced by experiences within the intervention (such as perceived availability of support) and beliefs about their condition and treatment (such as perceived control and risk of side effects). Developing our ability to adequately capture these salient burdens and benefits for patients could help enhance evaluation of self-management interventions in the future. Many participants perceived important benefits from using the intervention, highlighting the need for theory to recognise that engaging in self-management can include positive as well as negative aspects.

TRIAL REGISTRATION NUMBER

ISRCTN13790648; Pre-results.

Patient Variability Seldom Assessed in Cost-effectiveness Studies

BACKGROUND

Cost-effectiveness analysis (CEA) estimates can vary substantially across patient subgroups when patient characteristics influence preferences, outcome risks, treatment effectiveness, life expectancy, or associated costs. However, no systematic review has reported the frequency of subgroup analysis in CEA, what type of heterogeneity they address, and how often heterogeneity influences whether cost-effectiveness ratios exceed or fall below conventional thresholds.

METHODS

We reviewed the CEA literature cataloged in the Tufts Medical Center CEA Registry, a repository describing cost-utility analyses published through 2016. After randomly selecting 200 of 642 articles published in 2014, we ascertained whether each study reported subgroup results and collected data on the defining characteristics of these subgroups. We identified whether any of the CEA subgroup results crossed conventional cost-effectiveness benchmarks (e.g., $100,000 per QALY) and compared characteristics of studies with and without subgroup-specific findings.

RESULTS

Thirty-eight studies (19%) reported patient subgroup results. Articles reporting subgroup analyses were more likely to be US-based, government funded (v. drug industry- or nonprofit foundation-funded) studies, with a focus on primary or secondary (v. tertiary) prevention (P < 0.05 for comparisons). One or more patient characteristics were used to stratify CEA results 68 times within the 38 studies, with most stratifications using one characteristic (n = 47), most commonly age (n = 35). Among the 23 stratifications reported alongside average ratios in US studies, 13 produced subgroup ratios that crossed a conventional CEA ratio benchmark.

CONCLUSIONS

Most CEAs do not report any subgroup results, and those that do most often stratify only by patient age. Over half of the subgroup analyses reported could lead to different value-based decision making for at least some patients.

Costs and expected gain in lifetime health from intensive care versus general ward care of 30,712 individual patients: a distribution-weighted cost-effectiveness analysis

BACKGROUND

Clinicians, hospital managers, policy makers, and researchers are concerned about high costs, increased demand, and variation in priorities in the intensive care unit (ICU). The objectives of this modelling study are to describe the extra costs and expected health gains associated with admission to the ICU versus the general ward for 30,712 patients and the variation in cost-effectiveness estimates among subgroups and individuals, and to perform a distribution-weighted economic evaluation incorporating extra weighting to patients with high severity of disease.

METHODS

We used a decision-analytic model that estimates the incremental cost per quality-adjusted life year (QALY) gained (ICER) from ICU admission compared with general ward care using Norwegian registry data from 2008 to 2010. We assigned increasing weights to health gains for those with higher severity of disease, defined as less expected lifetime health if not admitted. The study has inherent uncertainty of findings because a randomized clinical trial comparing patients admitted or rejected to the ICU has never been performed. Uncertainty is explored in probabilistic sensitivity analysis.

RESULTS

The mean cost-effectiveness of ICU admission versus ward care was €11,600/QALY, with 1.6 QALYs gained and an incremental cost of €18,700 per patient. The probability (p) of cost-effectiveness was 95% at a threshold of €22,000/QALY. The mean ICER for medical admissions was €10,700/QALY (p = 97%), €12,300/QALY (p = 93%) for admissions after acute surgery, and €14,700/QALY (p = 84%) after planned surgery. For individualized ICERs, there was a 50% probability that ICU admission was cost-effective for 85% of the patients at a threshold of €64,000/QALY, leaving 15% of the admissions not cost-effective. In the distributional evaluation, 8% of all patients had distribution-weighted ICERs (higher weights to gains for more severe conditions) above €64,000/QALY. High-severity admissions gained the most, and were more cost-effective.

CONCLUSIONS

On average, ICU admission versus general ward care was cost-effective at a threshold of €22,000/QALY (p = 95%). According to the individualized cost-effectiveness information, one in six ICU admissions was not cost-effective at a threshold of €64,000/QALY. Almost half of these admissions that were not cost-effective can be regarded as acceptable when weighted by severity of disease in terms of expected lifetime health. Overall, existing ICU services represent reasonable resource use, but considerable uncertainty becomes evident when disaggregating into individualized results.

Understanding the Value of Individualized Information: The Impact of Poor Calibration or Discrimination in Outcome Prediction Models

BACKGROUND

Risk prediction models allow for the incorporation of individualized risk and clinical effectiveness information to identify patients for whom therapy is most appropriate and cost-effective. This approach has the potential to identify inefficient (or harmful) care in subgroups at different risks, even when the overall results appear favorable. Here, we explore the value of personalized risk information and the factors that influence it.

METHODS

Using an expected value of individualized care (EVIC) framework, which monetizes the value of customizing care, we developed a general approach to calculate individualized incremental cost effectiveness ratios (ICERs) as a function of individual outcome risk. For a case study (tPA v. streptokinase to treat possible myocardial infarction), we used a simulation to explore how an EVIC is influenced by population outcome prevalence, model discrimination (c-statistic) and calibration, and willingness-to-pay (WTP) thresholds.

RESULTS

In our simulations, for well-calibrated models, which do not over- or underestimate predicted v. observed event risk, the EVIC ranged from $0 to $700 per person, with better discrimination (higher c-statistic values) yielding progressively higher EVIC values. For miscalibrated models, the EVIC ranged from -$600 to $600 in different simulated scenarios. The EVIC values decreased as discrimination improved from a c-statistic of 0.5 to 0.6, before becoming positive as the c-statistic reached values of ~0.8.

CONCLUSIONS

Individualizing treatment decisions using risk may produce substantial value but also has the potential for net harm. Good model calibration ensures a non-negative EVIC. Improvements in discrimination generally increase the EVIC; however, when models are miscalibrated, greater discriminating power can paradoxically reduce the EVIC under some circumstances.

Biases in Individualized Cost-effectiveness Analysis: Influence of Choices in Modeling Short-Term, Trial-Based, Mortality Risk Reduction and Post-Trial Life Expectancy

BACKGROUND

The benefits and costs of a treatment are typically heterogeneous across individual patients. Randomized clinical trials permit the examination of individualized treatment benefits over the trial horizon but extrapolation to lifetime horizon usually involves combining trial-based individualized estimates of short-term risk reduction with less detailed (less granular) population life tables. However, the underlying assumption of equal post-trial life expectancy for low- and high-risk patients of the same sex and age is unrealistic. We aimed to study the influence of unequal granularity between models of short-term risk reduction and life expectancy on individualized estimates of cost-effectiveness of aggressive thrombolysis for patients with an acute myocardial infarction.

METHODS

To estimate life years gained, we multiplied individualized estimates of short-term risk reduction either with less granular and with equally granular post-trial life expectancy estimates. Estimates of short-term risk reduction were obtained from GUSTO trial data (30,510 patients) using logistic regression analysis with treatment, sex, and age as predictor variables. Life expectancy estimates were derived from sex- and age-specific US life tables.

RESULTS

Based on sex- and age-specific, short-term risk reductions but average population life expectancy (less granularity), we found that aggressive thrombolysis was cost-effective (incremental cost-effectiveness ratio below $50,000) for women above age 49 y and men above age 53 y (92% and 69% of the population, respectively). Considering sex- and age-specific short-term mortality risk reduction and correspondingly sex- and age-specific life expectancy (equal granularity), aggressive thrombolysis was cost-effective for men above age 45 y and women above age 50 y (95% and 76% of the population, respectively).

CONCLUSIONS

Failure to model short-term risk reduction and life expectancy at an equal level of granularity may bias our estimates of individualized cost-effectiveness and misallocate resources.

Individualized cost-effectiveness analysis of patient-centered care: a case series of hospitalized patient preferences departing from practice-based guidelines

OBJECTIVE

To develop cases of preference-sensitive care and analyze the individualized cost-effectiveness of respecting patient preference compared to guidelines.

METHODS

Four cases were analyzed comparing patient preference to guidelines: (a) high-risk cancer patient preferring to forgo colonoscopy; (b) decubitus patient preferring to forgo air-fluidized bed use; (c) anemic patient preferring to forgo transfusion; (d) end-of-life patient requesting all resuscitative measures. Decision trees were modeled to analyze cost-effectiveness of alternative treatments that respect preference compared to guidelines in USD per quality-adjusted life year (QALY) at a $100,000/QALY willingness-to-pay threshold from patient, provider and societal perspectives.

RESULTS

Forgoing colonoscopy dominates colonoscopy from patient, provider, and societal perspectives. Forgoing transfusion and air-fluidized bed are cost-effective from all three perspectives. Palliative care is cost-effective from provider and societal perspectives, but not from the patient perspective.

CONCLUSION

Prioritizing incorporation of patient preferences within guidelines holds good value and should be prioritized when developing new guidelines.

Tolerance limits and tolerance intervals for ratios of normal random variables using a bootstrap calibration

This paper addresses the problem of deriving one-sided tolerance limits and two-sided tolerance intervals for a ratio of two random variables that follow a bivariate normal distribution, or a lognormal/normal distribution. The methodology that is developed uses nonparametric tolerance limits based on a parametric bootstrap sample, coupled with a bootstrap calibration in order to improve accuracy. The methodology is also adopted for computing confidence limits for the median of the ratio random variable. Numerical results are reported to demonstrate the accuracy of the proposed approach. The methodology is illustrated using examples where ratio random variables are of interest: an example on the radioactivity count in reverse transcriptase assays and an example from the area of cost-effectiveness analysis in health economics.

Healthcare resource use in patients of the Australian Transition Care Program

OBJECTIVE

The aim of the present study was to describe, from the perspective of the healthcare funder, the cost components of the Australian Transition Care Program (TCP) and the healthcare resource use and costs for a group of transition care clients over a 6-month period following admission to the program.

METHODS

A prospective cohort observational study of 351 consenting patients entering community-based transition care at six sites in two states in Australia from November 2009 to September 2010 was performed. Patients were followed up 6 months after admission to the TCP to ascertain current living status and hospital re-admissions over the follow-up period. Cost data were collected by transition care teams and from administrative data (hospital and Medicare records).

RESULTS

The TCP provides a range of services with most costs attributed to provision of personal care support, case management, physiotherapy and occupational therapy. Most healthcare costs up to 6 months after transition care admission were incurred from the hospital admission leading to transition care and from re-admissions. Orthopaedic conditions incurred the highest costs, with many of these for elective procedures and others resulting from falls. Hospital re-admission rates in the present study were 10% lower than in a previous evaluation ofthe TCP. Over 6 months, approximately 40% of patients in the study were re-admitted to hospital at an average cost of A$7038.

CONCLUSIONS

Although the cost of the TCP is relatively high, it may have some impact on reducing hospital re-admissions and preventing or delaying residential care admissions.

HCV: the best cure possible or the best possible cure?

Progress in medicine goes along with an exponential growth of the cost of drugs and devices. While any person has the right to obtain the best possible benefit from medical care, a state needs to strike a balance between granting the optimal personal benefit to each individual and the needs of the society as a whole. Health systems in all countries therefore are facing a huge problem of distributive justice, as while they should guarantee individual rights, among which the right to health in its broader sense, including physical, psychological and social well-being (therefore not limited to healing, but extending to compliance and quality of life), they must also grant equal access to the healthcare resources and keep the distribution system sustainable.

Cost-effectiveness analysis and insurance coverage: solving a puzzle

The conventional model for the use of cost-effectiveness analysis for health programs involves determining whether the cost per unit of effectiveness of the program is lower than some socially determined maximum acceptable cost per unit of effectiveness. If a program is better by this criterion, the policy implication is that it should be implemented by full coverage of its cost by insurance; if not, the program should not be implemented. This paper examines the unanswered question of how cost-effectiveness analysis should be performed and interpreted when insurance coverage may involve cost sharing. It explores the question of how cost sharing should be related to the magnitude of a cost-effectiveness ratio. A common view that cost sharing should vary inversely with program cost-effectiveness is shown to be incorrect. A key issue in correct analysis is whether there is heterogeneity in marginal effectiveness of care that cannot be perceived by the social planner but is known by the demander. It is possible that some programs that would fail the social efficiency test at full coverage will be acceptable with positive cost sharing. Combining individual and social preferences affects both the choice of programs and the extent of cost sharing.

Descriptive analysis of the cost-effectiveness of depressed patients undergoing total knee arthroplasty: an economic decision analysis

BACKGROUND

Recent rising concern about the cost-effectiveness ratio of total knee arthroplasty (TKA) led us to evaluate this successful procedure from the economic perspective, thereby helping policy-makers to conduct medical resource allocation more effectively and efficiently. However, up to now no study has investigated the influence of patients' psychological factors on their evaluation of TKA's cost-effectiveness. Therefore, we decided to determine whether and how depression, which is a common negative psychological factor in the population undergoing TKA, affects their economic evaluation of the procedure.

METHODS

A total of 312 patients who had undergone TKA were graded into three groups based on the level of depression measured by the Center for Epidemiological Studies Depression Scale (CES-D) scores. Clinical effectiveness information was obtained using the WOMAC questionnaire; total costs related to TKA were acquired through interviews with patients and review of their medical records in the computing system.

RESULTS

Patients with high-level depression (3491.9$/QALYS; 95% CI, 3471.1-3491.9$/QALYS) had greater cost-effectiveness compared tothose with low-level (2447.1$/QALYS; 95% CI, 2427.9-2466.3$/QALYS) and middle-level (3027.2$/QALYS; 95% CI, 3011.0-3043.4$/QALYS) depression. We concluded there was a significant positive correlation between cost-effectiveness and the level of depression after TKA (r = 0.703, P = 0.014). Significant differences in the costs of hospital stay, medical treatment, rehabilitation, and outpatient care were detected among the three groups.

CONCLUSIONS

Our study might help policy-makers and clinicians identify which types of patients benefit most from TKA and then advise high-risk patients (high-level depression status in this study) about how to recovery better with limited resource allocation. Preoperative evaluation of patients' psychological state may decrease unnecessary economic burdens and suffering during the recovery period.

Economic analysis of revision amputation and replantation treatment of finger amputation injuries

BACKGROUND

The purpose of this study was to perform a cost-utility analysis to compare revision amputation and replantation treatment of finger amputation injuries across a spectrum of injury scenarios.

METHODS

The study was conducted from the societal perspective. Decision tree models were created for the reference case (two-finger amputation injury) and seven additional injury scenarios for comparison. Inputs included cost, quality of life, and probability of each health state. A Web-based time trade-off survey was created to determine quality-adjusted life-years for health states; 685 nationally representative adult community members were invited to participate in the survey. Overall cost and quality-adjusted life-years for revision amputation and replantation were calculated for each decision tree. An incremental cost-effectiveness ratio was calculated if a treatment was more costly but more effective.

RESULTS

The authors had a 64 percent response rate (n = 437). Replantation treatment had greater costs and quality-adjusted life-years compared with revision amputation in all injury scenarios. Replantation of single-digit injuries had the highest incremental cost-effectiveness ratio ($136,400 per quality-adjusted life-year gained). Replantation of three- and four-digit amputation injuries had relatively low cost-to-benefit ratios ($27,100 and $23,800 per quality-adjusted life-year, respectively). Replantation for distal thumb amputation had a relatively low incremental cost-effectiveness ratio ($26,300 per quality-adjusted life-year) compared with replantation of nonthumb distal amputations ($60,200 per quality-adjusted life-year).

CONCLUSIONS

The relative cost per quality-adjusted life-year gained with replantation treatment varied greatly among the injury scenarios. Situations in which indications for replantation are debated had higher cost per quality-adjusted life-year gained. This study highlights variability in value for replantation among different injury scenarios.

Cost-effectiveness of a national exercise referral programme for primary care patients in Wales: results of a randomised controlled trial

Background

A recent HTA review concluded that there was a need for RCTs of exercise referral schemes (ERS) for people with a medical diagnosis who might benefit from exercise. Overall, there is still uncertainty as to the cost-effectiveness of ERS. Evaluation of public health interventions places challenges on conventional health economics approaches. This economic evaluation of a national public health intervention addresses this issue of where ERS may be most cost effective through subgroup analysis, particularly important at a time of financial constraint.

Method

This economic analysis included 798 individuals aged 16 and over (55% of the randomised controlled trial (RCT) sample) with coronary heart disease risk factors and/or mild to moderate anxiety, depression or stress. Individuals were referred by health professionals in a primary care setting to a 16 week national exercise referral scheme (NERS) delivered by qualified exercise professionals in local leisure centres in Wales, UK. Health-related quality of life, health care services use, costs per participant in NERS, and willingness to pay for NERS were measured at 6 and 12 months.

Results

The base case analysis assumed a participation cost of £385 per person per year, with a mean difference in QALYs between the two groups of 0.027. The incremental cost-effectiveness ratio was £12,111 per QALY gained. Probabilistic sensitivity analysis demonstrated an 89% probability of NERS being cost-effective at a payer threshold of £30,000 per QALY. When participant payments of £1 and £2 per session were considered, the cost per QALY fell from £12,111 (base case) to £10,926 and £9,741, respectively. Participants with a mental health risk factor alone or in combination with a risk of chronic heart disease generated a lower ICER (£10,276) compared to participants at risk of chronic heart disease only (£13,060).

Conclusions

Results of cost-effectiveness analyses suggest that NERS is cost saving in fully adherent participants. Though full adherence to NERS (62%) was higher for the economics sample than the main sample (44%), results still suggest that NERS can be cost-effective in Wales with respect to existing payer thresholds particularly for participants with mental health and CHD risk factors.

Trial registration

Current Controlled Trials ISRCTN47680448

Multiomics medicine in oncology: assessing effectiveness, cost–effectiveness and future research priorities for the molecularly unique individual

The development of genomic technologies has ushered in the era of pharmacogenomics. However, discoveries and clinical use of targeted therapies are still in their infancy. A focus on monogenic pharmacogenetic traits may contribute to this lack of progress. Variation in drug response is likely a complex paradigm involving not only genomic factors but proteomic, metabolomic and epigenomic influences. The incorporation of these omics elements into pharmaceutical development and clinical decision-making will ultimately require the use of methods to determine clinical and economic value. Current methodologies and guidelines for determining clinical effectiveness and cost–effectiveness may have limited applicability to the increasingly personalized nature of omics treatment strategies. Using examples from oncology, this article argues for the adaptation and tailoring of three existing methods for ensuring development and clinical use of multiomics-guided therapies that are effective, safe and offer value for money.

A generalised model for individualising a treatment recommendation based on group-level evidence from randomised clinical trials

OBJECTIVES

Randomised controlled trials report group-level treatment effects. However, an individual patient confronting a treatment decision needs to know whether that person's expected treatment benefit will exceed the expected treatment harm. We describe a flexible model for individualising a treatment decision. It individualises group-level results from randomised trials using clinical prediction guides.

METHODS

We constructed models that estimate the size of individualised absolute risk reduction (ARR) for the target outcome that is required to offset individualised absolute risk increase (ARI) for the treatment harm. Inputs to the model include estimates for the individualised predicted absolute treatment benefit and harm, and the relative value assigned by the patient to harm/benefit. A decision rule recommends treatment when the predicted benefit exceeds the predicted harm, value-adjusted. We also derived expressions for the maximum treatment harm, or the maximum relative value for harm/benefit, above which treatment would not be recommended.

RESULTS

For the simpler model, including one kind of benefit and one kind of harm, the individualised ARR required to justify treatment was expressed as required ARRtarget(i)=ARIharm(i) × RVharm/target(i). A complex model was also developed, applicable to treatments causing multiple kinds of benefits and/or harms. We demonstrated the applicability of the models to treatments tested in superiority trials (either placebo or active control, either fixed harm or variable harm) and non-inferiority trials.

CONCLUSIONS

Individualised treatment recommendations can be derived using a model that applies clinical prediction guides to the results of randomised trials in order to identify which individual patients are likely to derive a clinically important benefit from the treatment. The resulting individualised prediction-based recommendations require validation by comparison with strategies of treat all or treat none.

Cost-effectiveness of endometrial evaluation prior to morcellation in surgical procedures for prolapse

OBJECTIVE

The objective of the study was to compare the cost-effectiveness of 3 screening options for endometrial cancer in asymptomatic, postmenopausal women prior to undergoing morcellation in minimally invasive supracervical hysterectomy and minimally invasive sacral colpopexy for the treatment of pelvic organ prolapse.

STUDY DESIGN

A decision tree model was constructed to compare no screening, endometrial biopsy, and transvaginal ultrasound for asymptomatic, postmenopausal women prior to surgery. Effectiveness was measured by life-years. The incremental cost-effectiveness ratio, defined as the difference in cost between 2 screening options divided by the difference in life-years between the 2 options, was calculated in 2012 US dollars for endometrial biopsy and transvaginal ultrasound, in comparison with no screening.

RESULTS

Using an endometrial cancer prevalence of 0.6% and a 40% risk of upstaging after morcellation, the expected per-patient cost was $8800, $9023, and $9112 over 5 years for no screening, endometrial biopsy, and transvaginal ultrasound, respectively. The expected life-years saved compared with no screening were 0.00108 for endometrial biopsy and 0.00105 for transvaginal ultrasound, ie, 0.39 and 0.38 days, respectively. The estimated incremental cost-effectiveness ratio was $207,348 for endometrial biopsy and $298,038 for transvaginal ultrasound compared with no screening. A sensitivity analysis showed that the prevalence of endometrial cancer and the risk of endometrial cancer upstaging after morcellation had the greatest impact on the cost-effectiveness of screening.

CONCLUSION

For asymptomatic, postmenopausal women, preoperative endometrial evaluation via endometrial biopsy or transvaginal ultrasound helps improve the preoperative detection of endometrial cancer, but universal screening is not cost effective.

Limitations of medical research and evidence at the patient-clinician encounter scale

We explore some philosophical and scientific underpinnings of clinical research and evidence at the patient-clinician encounter scale. Insufficient evidence and a common failure to use replicable and sound research methods limit us. Both patients and health care may be, in part, complex nonlinear chaotic systems, and predicting their outcomes is a challenge. When trustworthy (credible) evidence is lacking, making correct clinical choices is often a low-probability exercise. Thus, human (clinician) error and consequent injury to patients appear inevitable. Individual clinician decision-makers operate under the philosophical influence of Adam Smith's "invisible hand" with resulting optimism that they will eventually make the right choices and cause health benefits. The presumption of an effective "invisible hand" operating in health-care delivery has supported a model in which individual clinicians struggle to practice medicine, as they see fit based on their own intuitions and preferences (and biases) despite the obvious complexity, errors, noise, and lack of evidence pervading the system. Not surprisingly, the "invisible hand" does not appear to produce the desired community health benefits. Obtaining a benefit at the patient-clinician encounter scale requires human (clinician) behavior modification. We believe that serious rethinking and restructuring of the clinical research and care delivery systems is necessary to assure the profession and the public that we continue to do more good than harm. We need to evaluate whether, and how, detailed decision-support tools may enable reproducible clinician behavior and beneficial use of evidence.

Selective decontamination of the digestive tract and selective oropharyngeal decontamination in intensive care unit patients: a cost-effectiveness analysis

OBJECTIVE

To determine costs and effects of selective digestive tract decontamination (SDD) and selective oropharyngeal decontamination (SOD) as compared with standard care (ie, no SDD/SOD (SC)) from a healthcare perspective in Dutch Intensive Care Units (ICUs).

DESIGN

A post hoc analysis of a previously performed cluster-randomised trial (NEJM 2009;360:20).

SETTING

13 Dutch ICUs.

PARTICIPANTS

Patients with ICU-stay of >48 h that received SDD (n=2045), SOD (n=1904) or SC (n=1990).

INTERVENTIONS

SDD or SOD.

PRIMARY AND SECONDARY OUTCOME MEASURES

Effects were based on hospital survival, expressed as crude Life Years Gained (cLYG). The incremental cost-effectiveness ratio (ICER) was calculated, with corresponding cost acceptability curves. Sensitivity analyses were performed for discount rates, costs of SDD, SOD and mechanical ventilation.

RESULTS

Total costs per patient were €41 941 for SC (95% CI €40 184 to €43 698), €40 433 for SOD (95% CI €38 838 to €42 029) and €41 183 for SOD (95% CI €39 408 to €42 958). SOD and SDD resulted in crude LYG of +0.04 and +0.25, respectively, as compared with SC, implying that both SDD and SOD are dominant (ie, cheaper and more beneficial) over SC. In cost-effectiveness acceptability curves probabilities for cost-effectiveness, compared with standard care, ranged from 89% to 93% for SOD and from 63% to 72% for SDD, for acceptable costs for 1 LYG ranging from €0 to €20 000. Sensitivity analysis for mechanical ventilation and discount rates did not change interpretation. Yet, if costs of the topical component of SDD and SOD would increase 40-fold to €400/day and €40/day (maximum values based on free market prices in 2012), the estimated ICER as compared with SC for SDD would be €21 590 per LYG. SOD would remain cost-saving.

CONCLUSIONS

SDD and SOD were both effective and cost-saving in Dutch ICUs.

Cost-effectiveness analysis for clinicians

In a climate of economic uncertainty, cost effectiveness analysis is a potentially important tool for making choices about health care interventions. Methods for such analyses are well established, but the results need to be interpreted carefully and are subject to bias. Making decisions based on results of cost-effectiveness analyses can involve setting thresholds, but for individual patients, there needs to be disaggregation of benefits and harms included in a quality adjusted life year to ensure appropriate consideration of benefits and harms as well as personal preferences and circumstances.