Cost-effectiveness of Prophylactic Moisturization for Atopic Dermatitis

IMPORTANCE

Emerging evidence suggests that the use of moisturizers on newborns and infants (ie, from birth to 6 months of age) is potentially helpful in preventing the development of atopic dermatitis.

OBJECTIVE

To investigate the cost-effectiveness of using a daily moisturizer as prevention against atopic dermatitis among high-risk newborns.

DESIGN, SETTING, AND PARTICIPANTS

In a cost-effectiveness analysis, the average cost of total-body moisturization using 7 common moisturizers from birth to 6 months of age was determined for male and female infants. We assumed the same unit of weight per moisturizer used for a given body surface area. Based on previously reported data (relative risk reduction of 50%), the incremental gain in quality-adjusted life-years (QALYs) was determined using a 6-month time window. The cost-effectiveness of each moisturizer was determined by assuming equal efficacy. A sensitivity analysis was conducted by varying the relative risk from 0.28 to 0.90.

INTERVENTIONS

Use of prophylactic moisturizing compounds.

MAIN OUTCOMES AND MEASURES

The primary outcomes were the incremental cost-effectiveness values ($/QALY) for each moisturizer in preventing atopic dermatitis during a 6-month time window.

RESULTS

The calculated amount of daily all-body moisturizer needed at birth was 3.6 g (0.12 oz) per application, which increased to 6.6 g (0.22 oz) at 6 months of age. Of the 7 products evaluated, the average price was $1.07/oz (range, $0.13/oz-$2.96/oz). For a 6-month time window, the average incremental QALY benefit was 0.021. The sensitivity analysis showed that the incremental gain of QALY ranged from 0.0041 to 0.030. Petrolatum was the most cost-effective ($353/QALY [95% CI, $244-$1769/QALY) moisturizer in the cohort. Even assuming the lowest incremental QALYs for the most expensive moisturizer, the intervention was still less than $45 000/QALY.

CONCLUSIONS AND RELEVANCE

Overall, atopic dermatitis represents a major health expenditure and has been associated with multiple comorbidities. Daily moisturization may represent a cost-effective, preventative strategy to reduce the burden of atopic dermatitis.

A Common Rationale for Global Sensitivity Measures and Their Estimation

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double-loop form. Recently, a more efficient single-loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single-loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.

Large-Sample Bayesian Posterior Distributions for Probabilistic Sensitivity Analysis

In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative effect parameters, for both controlled and uncontrolled studies, and discuss how to use these posterior distributions in a probabilistic sensitivity analysis. These results draw on and extend procedures from the literature on large-sample Bayesian posterior distributions and Bayesian random effects meta-analysis. They improve on standard approaches to probabilistic sensitivity analysis by allowing a proper accounting for heterogeneity across studies as well as dependence between control and treatment parameters, while still being simple enough to be carried out on a spreadsheet. The authors apply these methods to conduct a probabilistic sensitivity analysis for a recently published analysis of zidovudine prophylaxis following rapid HIV testing in labor to prevent vertical HIV transmission in pregnant women.

NUTORC-a transdisciplinary health services and outcomes research team in transplantation

The field of solid organ transplantation has historically concentrated research efforts on basic science and translational studies. However, there has been increasing interest in health services and outcomes research. The aim was to build an effective and sustainable, inter- and transdisciplinary health services and outcomes research team (NUTORC), that leveraged institutional strengths in social science, engineering, and management disciplines, coupled with an international recognized transplant program. In 2008, leading methodological experts across the university were identified and intramural funding was obtained for the NUTORC initiative. Inter- and transdisciplinary collaborative teams were created across departments and schools within the university. Within 3 years, NUTORC became fiscally sustainable, yielding more than tenfold return of the initial investment. Academic productivity included funding for 39 grants, publication of 60 manuscripts, and 166 national presentations. Sustainable educational opportunities for students were created. Inter- and transdisciplinary health services and outcomes research in transplant can be innovative and sustainable.

Comparative effectiveness of donation after cardiac death versus donation after brain death liver transplantation: Recognizing who can benefit

Due to organ scarcity and wait-list mortality, transplantation of donation after cardiac death (DCD) livers has increased. However, the group of patients benefiting from DCD liver transplantation is unknown. We studied the comparative effectiveness of DCD versus donation after brain death (DBD) liver transplantation. A Markov model was constructed to compare undergoing DCD transplantation with remaining on the wait-list until death or DBD liver transplantation. Differences in life years, quality-adjusted life years (QALYs), and costs according to candidate Model for End-Stage Liver Disease (MELD) score were considered. A separate model for hepatocellular carcinoma (HCC) patients with and without MELD exception points was constructed. For patients with a MELD score <15, DCD transplantation resulted in greater costs and reduced effectiveness. Patients with a MELD score of 15 to 20 experienced an improvement in effectiveness (0.07 QALYs) with DCD liver transplantation, but the incremental cost-effectiveness ratio (ICER) was >$2,000,000/QALY. Patients with MELD scores of 21 to 30 (0.25 QALYs) and >30 (0.83 QALYs) also benefited from DCD transplantation with ICERs of $478,222/QALY and $120,144/QALY, respectively. Sensitivity analyses demonstrated stable results for MELD scores <15 and >20, but the preferred strategy for the MELD 15 to 20 category was uncertain. DCD transplantation was associated with increased costs and reduced survival for HCC patients with exception points but led to improved survival (0.26 QALYs) at a cost of $392,067/QALY for patients without exception points. In conclusion, DCD liver transplantation results in inferior survival for patients with a MELD score <15 and HCC patients receiving MELD exception points, but provides a survival benefit to patients with a MELD score >20 and to HCC patients without MELD exception points.

Development of Goal-Sensitive Health-Related Utility Assessment Procedures

Purpose. To measure the degree to which people express willingness to trade life or health for nonmedical goals. Method. In 3 studies, outpatients provided important life goals. In study 1, patients performed time-tradeoff between life-years and goal achievement and chose between states that varied in goal achievement, life expectancy, and disability; in study 2, patients made choices that traded off health state and goal achievement; in study 3, patients performed time-tradeoff assessments in 3 different goal achievement contexts. Results. In study 1 (n = 58), participants were eager to trade life-years for goal achievement, trading, on average, 71% of their remaining life for certain achievement v. certain nonachievement or 54% of their remaining life for their expected likelihood of achievement v. nonachievement. Life expectancy, disability status, and goal achievement each had a significant main effect on utility. In study 2 (n = 54), participants equally preferred a moderately impaired health state with goal achievement to perfect health without goal achievement and more strongly preferred the moderately impaired state with goal achievement than other less impaired states without goal achievement. Study 3 (n = 62) demonstrated that the mere discussion of goals and goal achievement or nonachievement in the context of a standard time-tradeoff assessment (without trading off goals) did not impact the assessment. Conclusions. Nonmedical life goals are important determinants of quality of life. People express willingness to trade off life and health in pursuit of these goals, which are extrinsic to the standard quality-adjusted life-year model.

Incorporating extrinsic goals into decision and cost-effectiveness analyses

It has not been widely recognized that medical patients as individuals may have goals that are not easily expressed in terms of quality-adjusted life years (QALYs). The QALY model deals with ongoing goals such as reducing pain or maintaining mobility, but goals such as completing an important project or seeing a child graduate from college occur at unique points in time and do not lend themselves to easy expression in terms of QALYs. Such extrinsic goals have been posited as explanations for preferences inconsistent with the QALY model, such as unwillingness to trade away time or accept gambles. In this article, the authors examine methods for including extrinsic goals in medical decision and cost-effectiveness analyses. As illustrations, they revisit 2 previously published analyses, the management of unruptured intracranial arteriovenous malformations (AVMs) and the evaluation of preventive strategies for BRCA + women.

Stochastic trees and the StoTree modeling environment: models and software for medical decision analysis

In this paper we present a review of stochastic trees, a convenient modeling approach for medical treatment decision analyses. Stochastic trees are a generalization of decision trees that incorporate useful features from continuous-time Markov chains. We also discuss StoTree, a freely available software tool for the formulation and solution of stochastic trees, implemented in the Excel spreadsheet environment.

A Bayesian approach to sensitivity analysis

Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures. We advocate a new approach based on information value and argue that such an approach is better suited to address the decision-maker's real concerns. We provide an example comparing conventional sensitivity analysis to one based on information value. This article is a US government work and is in the public domain in the United States.

Sensitivity analysis and the expected value of perfect information

Measures of decision sensitivity that have been applied to medical decision problems were examined. Traditional threshold proximity methods have recently been supplemented by probabilistic sensitivity analysis, and by entropy-based measures of sensitivity. The authors propose a fourth measure based upon the expected value of perfect information (EVPI), which they believe superior both methodologically and pragmatically. Both the traditional and the newly suggested sensitivity measures focus entirely on the likelihood of decision change without attention to corresponding changes in payoff, which are often small. Consequently, these measures can dramatically overstate problem sensitivity. EVPI, on the other hand, incorporates both the probability of a decision change and the marginal benefit of such a change into a single measure, and therefore provides a superior picture of problem sensitivity. To lend support to this contention, the authors revisit three problems from the literature and compare the results of sensitivity analyses using probabilistic, entropy-based, and EVPI-based measures.

A cost-effectiveness analysis of total hip arthroplasty for osteoarthritis of the hip

OBJECTIVE

To quantify the trade-off between the expected increased short- and long-term costs and the expected increase in quality-adjusted life expectancy (QALE) associated with total hip arthroplasty (THA) for persons with functionally significant hip osteoarthritis.

DESIGN

A cost-effectiveness study was performed from the societal perspective by constructing stochastic tree, decision analytic models designed to estimate lifetime functional outcomes and costs of THA and nonoperative managements.

MAIN OUTCOME MEASURES

A modified four-state American College of Rheumatology functional status classification was used to measure effectiveness. These functional classes were assigned utility values to allow the relative effectiveness of THA to be expressed in quality-adjusted life years (QALYs). Lifetime costs included costs associated with primary and potential revision surgeries and long-term care costs associated with the functionally dependent class. DATA USED IN THE COST-EFFECTIVENESS MODEL: Probability and incidence rate data were summarized from the literature. The THA hospital cost data were obtained from local teaching hospitals' cost accounting systems. Estimates of recurring medical costs for functionally significant hip osteoarthritis and for custodial care were derived from the literature.

RESULTS

The THA cost-effectiveness ratio increases with age and is higher for men than for women. In the base-case scenario for 60-year-old white women who have functionally significant but not dependent hip osteoarthritis, the model predicts that THA is cost saving because of the high costs of custodial care associated with dependency due to worsening hip osteoarthritis and that the procedure increases QALE by about 6.9 years. In the base-case scenario for men aged 85 years and older, the average lifetime cost associated with THA is $9100 more than nonoperative management, with an average increase in QALE of about 2 years. Thus, the THA cost-effectiveness ratio for men aged 85 years and older is $4600 per QALY gained, less than that of procedures intended to extend life such as coronary artery bypass surgery or renal dialysis. Worst-case analysis suggests that THA remains minimally cost-effective for this oldest age category ($80,000/QALY) even if probabilities, rates, utilities, costs, and the discount rate are simultaneously varied to extreme values that bias the analysis against surgery.

CONCLUSIONS

For persons with hip osteoarthritis associated with significant functional limitation, THA can be cost saving or, at worst, cost- effective in improving QALE when both short- and long-term outcomes are considered. Further research is needed to determine whether this procedure is actually being used in this cost-effective manner, especially in older age categories.

Factored stochastic trees: a tool for solving complex temporal medical decision models

The stochastic tree is a continuous-time version of a Markov-cycle tree, useful for constructing and solving medical decision models in which risks of mortality and morbidity may extend over time. Stochastic trees have advantages over Markov-cycle trees in graphic display and computational solution. Like the decision tree or Markov-cycle tree, stochastic tree models of complex medical decision problems can be too large for convenient graphic formulation and display. This paper introduces the notion of factoring a large stochastic tree into simpler components, each of which may be easily displayed. It also shows how the rollback solution procedure for unfactored stochastic trees may be conveniently adapted to solve factored trees. These concepts are illustrated using published examples from the medical literature.

Stochastic trees: a new technique for temporal medical decision modeling

This paper introduces stochastic trees, a new modeling approach for the class of medical decision problems in which risks of mortality and morbidity may extend over time. A stochastic tree may be regarded as a continuous-time version of a Markov-cycle tree, or alternately, as a multi-state DEALE model. Optimal decisions in stochastic trees can be determined by rollback, much in the same fashion as decision trees. The author discusses how age-dependent mortality rates and declining incidence rates may be modeled using stochastic trees. Concepts are illustrated using examples from the medical literature. It is argued that stochastic trees possess important advantages over Markov-cycle trees for medical decision modeling.

Continuous-risk utility assessment in medical decision making

The authors argue that for risky medical treatment decisions, conventional utility assessment techniques are inadequate due to their emphasis on unrealistic risk magnitudes and sure consequences, leading to assessment questions that are unfamiliar to most patients, have little educational value, and do not reliably extend to the application at hand. As an alternative, medical utility assessments should be performed in a continuous-risk domain with risk levels matching those of the actual decision problem. In support of this position, the authors describe an empirical study that compares the responses of subjects under a conventional assessment protocol with those of subjects under a continuous-risk utility assessment protocol. Preliminary results show that conventional assessment protocols result in significantly lower estimates of the degrees of risk aversion compared with a more realistic continuous-risk protocol.