De definitie van specialistische geneesmiddelen

Samenvatting Vanwege bekostigingsproblemen met bepaalde geneesmiddelen is er een afbakeningsdiscussie rond dit onderwerp ontstaan. Buiten het ziekenhuis worden soms geneesmiddelen gebruikt waarvan zorgverzekeraars vinden dat ze niet via de extramurale geneesmiddelfinanciering moeten worden bekostigd maar dat ze ten laste van het ziekenhuisbudget zouden moeten komen. Ziekenhuizen zijn bereid deze geneesmiddelen te betalen als ze er maar voor gecompenseerd worden. Tegen deze achtergrond is een mogelijke oplossing voor de financieringsproblematiek om extramuraal afgeleverde geneesmiddelen te scheiden in specialistische en generalistische middelen. Door de specialistische geneesmiddelen medisch-inhoudelijk, beleidsmatig en financieel onder de reikwijdte van het ziekenhuis te brengen kan de continuïteit in behandeling door de medisch specialist ook worden doorgetrokken naar de farmacotherapie, ongeacht waar de patiënt zich bevindt (intramuraal of extramuraal). Voor generalistische middelen zou de medisch-inhoudelijke, beleidsmatige en financiële praktijk niet anders zijn dan in de huidige situatie. etc ..

Mapping onto Eq-5 D for patients in poor health

<p>Abstract</p> <p>Background</p> <p>An increasing amount of studies report mapping algorithms which predict EQ-5 D utility values using disease specific non-preference-based measures. Yet many mapping algorithms have been found to systematically overpredict EQ-5 D utility values for patients in poor health. Currently there are no guidelines on how to deal with this problem. This paper is concerned with the question of why overestimation of EQ-5 D utility values occurs for patients in poor health, and explores possible solutions.</p> <p>Method</p> <p>Three existing datasets are used to estimate mapping algorithms and assess existing mapping algorithms from the literature mapping the cancer-specific EORTC-QLQ C-30 and the arthritis-specific Health Assessment Questionnaire (HAQ) onto the EQ-5 D. Separate mapping algorithms are estimated for poor health states. Poor health states are defined using a cut-off point for QLQ-C30 and HAQ, which is determined using association with EQ-5 D values.</p> <p>Results</p> <p>All mapping algorithms suffer from overprediction of utility values for patients in poor health. The large decrement of reporting 'extreme problems' in the EQ-5 D tariff, few observations with the most severe level in any EQ-5 D dimension and many observations at the least severe level in any EQ-5 D dimension led to a bimodal distribution of EQ-5 D index values, which is related to the overprediction of utility values for patients in poor health. Separate algorithms are here proposed to predict utility values for patients in poor health, where these are selected using cut-off points for HAQ-DI (> 2.0) and QLQ C-30 (< 45 average of QLQ C-30 functioning scales). The QLQ-C30 separate algorithm performed better than existing mapping algorithms for predicting utility values for patients in poor health, but still did not accurately predict mean utility values. A HAQ separate algorithm could not be estimated due to data restrictions.</p> <p>Conclusion</p> <p>Mapping algorithms overpredict utility values for patients in poor health but are used in cost-effectiveness analyses nonetheless. Guidelines can be developed on when the use of a mapping algorithms is inappropriate, for instance through the identification of cut-off points. Cut-off points on a disease specific questionnaire can be identified through association with the causes of overprediction. The cut-off points found in this study represent severely impaired health. Specifying a separate mapping algorithm to predict utility values for individuals in poor health greatly reduces overprediction, but does not fully solve the problem.</p

Putting the Choice in Choice Tasks:Incorporating Preference Elicitation Tasks in Health Preference Research

Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a ‘real world’ health-related decision context and that consequently the choice data can be held to be a true representation of the respondent’s health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and ‘setting up’ the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.</p

Putting the Choice in Choice Tasks:Incorporating Preference Elicitation Tasks in Health Preference Research

Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a ‘real world’ health-related decision context and that consequently the choice data can be held to be a true representation of the respondent’s health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and ‘setting up’ the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.</p

The “Health Benefit Basket” in The Netherlands

This contribution describes the entitlements in Dutch health care and explores how these entitlements are determined and to whom they apply. The focus is on services of curative care. No comprehensive positive or negative list of individual services is included in formal laws. Instead, the legislation states only what general types of medical services are covered and generally the “usual care” criterion determines to which interventions patients are entitled. This criterion is not very restrictive and yields local variations in service provision, which are moderated by practice guidelines. It is conceivable, however, that the recent introduction of the DBC financing system will change the reimbursement and therefore benefit-setting policy

Dutch Tariff for the Five-Level Version of EQ-5D

Background: In 2009, a new version of the EuroQol five-dimensional questionnaire (EQ-5D) was introduced with five rather than three answer levels per dimension. This instrument is known as the EQ-5D-5L. To make the EQ-5D-5L suitable for use in economic evaluations, societal values need to be attached to all 3125 health states. Objectives: To derive a Dutch tariff for the EQ-5D-5L. Methods: Health state values were elicited during face-to-face interviews in a general population sample stratified for age, sex, and education, using composite time trade-off (cTTO) and a discrete choice experiment (DCE). Data were modeled using ordinary least squares and tobit regression (for cTTO) and a multinomial conditional logit model (for DCE). Model performance was evaluated on the basis of internal consistency, parsimony, goodness of fit, handling of left-censored values, and theoretical considerations. Results: A representative sample (N = 1003) of the Dutch population participated in the valuation study. Data of 979 and 992 respondents were included in the analysis of the cTTO and the DCE, respectively. The cTTO data were left-censored at -1. The tobit model was considered the preferred model for the tariff on the basis of its handling of the censored nature of the data, which was confirmed through comparison with the DCE data. The predicted values for the EQ-5D-5L ranged from -0.446 to 1. Conclusions: This study established a Dutch tariff for the EQ-5D-5L on the basis of cTTO. The values represent the preferences of the Dutch population. The tariff can be used to estimate the impact of health care interventions on quality of life, for example, in context of economic evaluations.</p

The effects of lead time and visual aids in TTO valuation: a study of the EQ-VT framework

__Abstract__ __Background__ The effect of lead time in time trade-off (TTO) valuation is not well understood. The purpose of this study was to investigate the effects on health-state valuation of the length of lead time and the way the lead-time TTO task is displayed visually. __Methods__ Using two general population samples, we compared three lead-time TTO variants: 10 years of lead time in full health preceding 5 years of unhealthy time (standard); 5 years of lead time preceding 5 years of unhealthy time (experimental); and 10 years of lead time and 5 years of unhealthy time, presented with a visual aid to highlight the point where the lead time ends (experimental). Participants were randomized to receive one of the lead-time variants, as administered by a computer software program. __Results__ Health-state values generated by TTO valuation tasks using a longer lead time were slightly lower than those generated by tasks using a shorter lead time. When lead time and unhealthy time were presented with visual aids highlighting the difference between the lead time and unhealthy time, respondents spent more time considering health states with a value close to 0. __Conclusions__ Different lead-time time trade-off variants should be carefully studied in order to achieve the best measurement of health-state values using this new method

Dealing with the health state ‘dead’ when using discrete choice experiments to obtain values for EQ-5D-5L heath states - Springer

__Abstract__ __Objective__ : To evaluate two different methods to obtain a dead (0)—full health (1) scale for EQ-5D-5L valuation studies when using discrete choice (DC) modeling. __Method__ : The study was carried out among 400 respondents from Barcelona who were representative of the Spanish population in terms of age, sex, and level of education. The DC design included 50 pairs of health states in five blocks. Participants were forced to choose between two EQ-5D-5L states (A and B). Two extra questions concerned whether A and B were considered worse than dead. Each participant performed ten choice exercises. In addition, values were collected using lead-time trade-off (lead-time TTO), for which 100 states in ten blocks were selected. Each participant performed five lead-time TTO exercises. These consisted of DC models offering the health state ‘dead’ as one of the choices—for which all participants’ responses were used (DCdead)—and a model that included only the responses of participants who chose at least one state as worse than dead (WTD) (DCWTD). The study also estimated DC models rescaled with lead-time TTO data and a lead-time TTO linear model. __Results__ : The DCdead and DCWTD models produced relatively similar results, although the coefficients in the DCdead model were slightly lower. The DC model rescaled with lead-time TTO data produced higher utility decrements. Lead-time TTO produced the highest utility decrements. __Conclusions__: The incorporation of the state ‘dead’ in the DC models produces results in concordance with DC models that do not include ‘dead’

Time to tweak the TTO: results from a comparison of alternative specifications of the TTO

Abstract This article examines the effect that different specifications of the time trade-off (TTO) valuation task may have on values for EQ-5D-5L health states. The new variants of the TTO, namely lead-time TTO and lag-time TTO, along with the classic approach to TTO were compared using two durations for the health states (15 and 20 years). The study tested whether these methods yield comparable health-state values. TTO tasks were administered online. It was found that lag-time TTO produced lower values than lead-time TTO and that the difference was larger in the longer time frame. Classic TTO values most resembled those of the lag-time TTO in a 20-year time frame in terms of mean absolute difference. The relative importance of different domains of health was systematically affected by the duration of the health state. In the tasks with a 10-year health-state duration, anxiety/ depression had the largest negative impact on health-state values; in the tasks with a 5-year duration, the pain/discomfort domain had the largest negative impact

Introducing the composite time trade-off: a test of feasibility and face validity

__Abstract__ __Introduction__ This study was designed to test the feasibility and face validity of the composite time trade-off (composite TTO), a new approach to TTO allowing for a more consistent elicitation of negative health state values. __Methods__ The new instrument combines a conventional TTO to elicit values for states regarded better than dead and a lead-time TTO for states worse than dead. __Results__ A total of 121 participants completed the composite TTO for ten EQ-5D-5L health states. Mean values ranged from −0.104 for health state 53555 to 0.946 for 21111. The instructions were clear to 98 % of the respondents, and 95 % found the task easy to understand, indicating feasibility. Further, the average number of steps taken in the iteration procedure to achieve the point of indifference in the TTO and the average duration of each task were indicative of a deliberate cognitive process. __Conclusion__ Face validity was confirmed by the high mean values for the mild health states (>0.90) and low mean values for the severe states (<0.42). In conclusion, this study demonstrates the feasibility and face validity of the composite TTO in a face-to-face standardized computer-assisted interview setting