Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Estimating a Preference-Based Index for an Eight-Dimensional Health State Classification System for Multiple Sclerosis. Economic evaluation in short bowel syndrome (SBS): an algorithm to estimate utility scores for a patient-reported SBS-specific quality of life scale (SBS-QoL™) Modeling a preference-based index for two condition-specific measures (asthma and overactive bladder) using a nonparametric Bayesian method. Estimating the SF-6D value set for a population-based sample of Brazilians. Estimation of a Preference-Based Index from a Condition-Specific Measure: The King's Health Questionnaire Scoring the Child Health Utility 9D instrument: estimation of a Chinese child and adolescent-specific tariff Assessing Care of Vulnerable Elders: ACOVE Project Overview Quality of Life and General Health After Elective Surgery for Cervical Spine Pathologies: Determining a Valid and Responsive Metric of Health State Utility. Estimating a preference-based index for a menopause specific health quality of life questionnaire Multiattribute and Single‐Attribute Utility Functions for the Health Utilities Index Mark 3 System An Economic Evaluation of a Video- and Text-Based Computer-Tailored Intervention for Smoking Cessation: A Cost-Effectiveness and Cost-Utility Analysis of a Randomized Controlled Trial Development of the EXACT-U: a preference-based measure to report COPD exacerbation utilities. Development of an EORTC-8D Utility Algorithm for Sri Lanka Valuing SF-6D Health States Using a Discrete Choice Experiment More Informative Abstracts of Articles Describing Clinical Practice Guidelines The impact of preoperative patient characteristics on the cost-effectiveness of total hip replacement: a cohort study 3L, 5L, What the L? A NICE Conundrum Elicitation of utility scores in Canada for immune thrombocytopenia treated with romiplostim or watch and rescue An empirical comparison of EQ-5D and SF-6D in liver transplant patients. Putting the ‘Q’ in depression QALYs: a comparison of utility measurement using EQ-5D and SF-6D health related quality of life measures Assessing outcomes for cost-utility analysis in mental health interventions: mapping mental health specific outcome measure GHQ-12 onto EQ-5D-3L US Valuation of the EQ-5D Health States: Development and Testing of the D1 Valuation Model
In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults , to date relatively few studies have utilised ordinal methods to generate health state values from adolescents . This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm  for the Child Health Utility 9D ( CHU9D ) , a generic preference based instrument that has been specifically design ed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people . A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11 - 17 years ( N = 1982 ) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system . The data were analyzed using latent class conditional logit models to estimate values ( part worth utilities ) for each level of the nine attributes relating to the CHU9D . A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm  on the full health = 1 and dead = 0 scale required for the calculation of QALYs . It was evident that different decision processes were being used in the best and worst choices . Whilst respondents appeared readily able to choose ' best ' attribute levels for the CHU9D health states , a large amount of r and om variability and indeed different decision rules were evident for the choice of ' worst ' attribute levels , to the extent that the best and worst data should not be pooled from the statistical perspective . The optimal  adolescent-specific scoring algorithm  was therefore derived using data obtained from the best choices only . The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent population BACKGROUND Condition-specific measures are frequently used to assess the health-related quality of life of people with multiple sclerosis ( MS ) . Such measures are unsuitable for use in economic evaluations that require estimates of cost per quality -adjusted life-year because they are not based on preferences . OBJECTIVES To report the estimation of a preference-based single index for an eight-dimensional instrument for MS , the Multiple Sclerosis Impact Scale - Eight Dimensions ( MSIS-8D ) , derived from an MS-specific measure of health-related quality of life , the 29-item Multiple Sclerosis Impact Scale ( MSIS-29 ) . METHODS We elicited preferences for a sample of MSIS-8D states ( n = 169 ) from a sample ( n = 1702 ) of the UK general population . Preferences were elicited using the time trade-off technique via an Internet-based survey . We fitted regression models to these data to estimate values for all health states described by the MSIS-8D . Estimated values were assessed against MSIS-29 scores and values derived from generic preference-based measures in a large , representative sample of people with MS . RESULTS Participants reported that the time trade-off questions were easy to underst and . Observed health state values ranged from 0.08 to 0.89 . The best-performing model was a main effects , r and om effects model ( mean absolute error = 0.04 ) . Validation analyses support the performance of the MSIS-8D index : it correlated more strongly than did generic measures with MSIS-29 scores , and it discriminated effectively between subgroups of people with MS . CONCLUSIONS The MSIS-8D enables health state values to be estimated from the MSIS-29 , adding to the methods available to assess health outcomes and to estimate quality -adjusted life-years for MS for use in health technology assessment and decision-making context Purpose Condition-specific preference-based measures can offer utility data where they would not otherwise be available or where generic measures may lack sensitivity , although they lack comparability across conditions . This study aim ed to develop an algorithm for estimating utilities from the short bowel syndrome health-related quality of life scale ( SBS-QoL ™ ) . Methods SBS-QoL ™ items were selected based on factor and item performance analysis of a European SBS-QoL ™ data set and consultation with 3 SBS clinical experts . Six-dimension health states were developed using 8 SBS-QoL ™ items ( 2 dimensions combined 2 SBS-QoL ™ items ) . SBS health states were valued by a UK general population sample ( N = 250 ) using the lead-time time trade-off method . Preference weights or ‘ utility decrements ’ for each severity level of each dimension were estimated by regression models and used to develop the scoring algorithm . Results Mean utilities for the SBS health states ranged from −0.46 ( worst health state , very much affected on all dimensions ) to 0.92 ( best health state , not at all affected on all dimensions ) . The r and om effects model with maximum likelihood estimation regression had the best predictive ability and lowest root mean squared error and mean absolute error , and was used to develop the scoring algorithm . Conclusions The preference-weighted scoring algorithm for the SBS-QoL ™ developed is able to estimate a wide range of utility values from patient-level SBS-QoL ™ data . This allows estimation of SBS HRQL impact for the purpose of economic evaluation of SBS treatment benefits BACKGROUND Conventionally , parametric models were used for health state valuation data . Recently , research ers started to explore the use of nonparametric Bayesian methods in this area . OBJECTIVES We present a nonparametric Bayesian model to estimate a preference-based index for two condition-specific five-dimensional health state classifications , one for asthma ( five-dimensional Asthma Quality of Life Utility Index ) and the other for overactive bladder ( five-dimensional Overactive Bladder Quality of Life-Utility Index ) . METHODS Sample s of 307 and 311 members of the UK general population valued 99 health states selected from a total of 3125 health states defined by each of the measures using the time trade-off technique . The article presents the results of the nonparametric model and compares it with the original model estimated using a conventional parametric r and om-effects model . The different methods are compared theoretically and in terms of empirical performance across the two data sets . It also reports the effect of respondent characteristics on health state valuations . RESULTS The nonparametric models were found to be better at predicting health state values within the estimation sample than without in terms of root mean square error and the patterns of st and ardized residuals . Some respondent characteristics were found to explain variation in health state values , but these did not have a significant effect on the health states values when estimates were adjusted for sample differences with the general population . CONCLUSIONS The nonparametric Bayesian models are theoretically more appropriate than previously used parametric models and provide better utility estimates from the two condition-specific measures . Furthermore , the model is more flexible in estimating the effect of covariates OBJECTIVES SF-6D is a preference-based measure of health developed to estimate utility values from the SF-36 . The aim of this study was to estimate a weighting system for the SF-6D health states representing the preferences of a sample of the Southern Brazilian general population . METHODS A sample of 248 health states defined by the SF-6D was valued by a sample of the southern Brazilian population using the st and ard gamble . Mean and individual level multivariate regression models were fitted to the st and ard gamble valuation data to estimate preference weights for all SF-6D health states . The models were compared with those estimated in the UK study . RESULTS Five hundred twenty-eight participants were interviewed , but 58 ( 11 % ) were excluded for failing to value the worst state . Data from 469 subjects producing 2696 health states valuations were used in the regression analysis . In contrast to the best performing model for the UK data , the best performing model for the Brazilian data was a r and om effects model using only the main effects variables , highlighting the importance of adopting a country-specific algorithm to derive SF-6D health states values . Inconsistent coefficients were merged to produce the final recommended model , which has all significant coefficients and a mean absolute difference between observed and predicted st and ard gamble values of 0.07 . CONCLUSIONS The results provide the first population -based value set for Brazil for SF-6D health states , making it possible to generate quality -adjusted life years for cost-utility studies using regional data . Besides , utility weights derived using the preferences of a sample from a southern Brazilian population can be derived from existing SF-36 data sets Background . Generic preference-based measures of health may not adequately cover the impact of some conditions . There is therefore increasing interest in developing condition-specific preference-based measures . Objectives . The purpose of this study was to estimate a preference-based measure from a condition-specific measure of health for urinary incontinence , the 21-item King 's Health Question naire , for use in economic evaluation . Methods . The King 's Health Question naire ( KHQ ) was revised into a 5-dimensional health state classification amenable to valuation using items selected using psychometric evidence . Forty-nine states were valued using st and ard gamble by a representative sample of patients with urinary incontinence attending UK hospital outpatient clinics . Each respondent was asked to value 9 health states . Models have been estimated for predicting health state valuations for all 1024 states defined by the KHQ classification . The modeling had to cope with the clustering of data by respondent and its skewed distribution . Results . In total , 110 usable interviews were obtained from 169 patients approached to participate in the study . These responders generated 959 health state valuations . Mean health state values ranged from 0.77 to 0.98 . Models were estimated using mean health state values and r and om effects models of individual-level health state values . These models generated robust estimates of the ` main effects , ` and in general , the results support the ordinality of the KHQ health state classification . There were problems modeling interaction effects , and a number of alternatives were explored . Conclusion . The recommended model for estimating a preference-based measure from the condition-specific KHQ is presented Purpose To derive children and adolescents ’ preferences for health states defined by the Chinese version of Child Health Utility 9D ( CHU9D-CHN ) instrument in China that can be used to estimate quality -adjusted life years ( QALYs ) for economic evaluation . Methods A profile case best – worst scaling ( BWS ) and a time trade-off ( TTO ) method were combined to derive a Chinese-specific tariff for the CHU9D-CHN . The BWS survey  recruited students from primary and high schools using a multi-stage r and om sampling method and was administered in a classroom setting , whilst the TTO survey adopted an interviewer-administrated conventional TTO task and was administered to a convenience sample of undergraduate students . A latent class modelling framework was adopted for analysing the BWS data . Results Two independent surveys were conducted in Nanjing , China , including a valid sample of 902 students ( mean age 13 years ) from the BWS survey and a valid sample of 38 students ( mean age 18 years ) from the TTO survey . The poolability of the best and the worst responses was rejected and the optimal result based on the best responses only . The optimal model suggests the existence of two latent classes . The BWS estimates were further re-anchored onto the QALY scale using the TTO generated health state values via a mapping approach . Conclusion This study provides further insights into the use of the BWS method to generate health state values with young people and highlights the potential different decision rules that young people may employ for determining best vs. worst choices in this context Civilization and modern medicine now allow an unprecedented number of people to survive to old age . Not only has the definition of old evolved , but the aims of medical care for the elderly have progressed far beyond survival to maximizing functional and cognitive status , improving quality of life , and preserving productivity . Although many health problems in older age can be prevented or postponed by medical interventions , the quality of medical care for older persons remains largely unexamined . Previous work on quality -of-care measurement has focused on specific diseases or aspects of care , and in some cases targeted only a slice of the older population ( 16 ) . Such targeted approaches may not present a fair picture of overall quality and may lead to unintended incentives ( gaming the system ) ( 7 ) . Although broad systems of quality -of-care evaluation are less susceptible to gaming , they seldom include quality indicators focused on aspects of care important to older sick persons ( 8 , 9 ) . Substantial variation in preferences may make quality of care for older adults particularly difficult to measure . For example , older adults do not consistently prefer care that prolongs life , particularly if this care occurs at the expense of comfort . Furthermore , many ill , older adults can not advocate for themselves and may have no family members or friends to do so on their behalf . Thus , objective measures of the care they receive are particularly important . The Assessing Care of Vulnerable Elders ( ACOVE ) project endeavored to develop a comprehensive set of  quality - assessment tools for ill older persons . Because ill older persons constitute a heterogeneous cohort that is not easily delineated , we created a system to identify high-risk , community-dwelling individuals and targeted the most important clinical conditions affecting them . Specifically , our goals for this project were the following : 1 . Develop a definition of vulnerable elders that delineates a group of community-living persons 65 years of age and older who are at high risk for death or functional decline , and develop a system to identify them . 2 . Identify important medical conditions that affect vulnerable elders and for which effective methods of prevention or management exist . 3 . Develop a set of evidence -based , quality -of-care indicators that are relevant to vulnerable elders using systematic literature review s , expert opinion , and the guidance of expert groups and stakeholders . 4 . Design a chart abstract ion tool , interview instrument , and administrative data analytic methods to implement the quality -of-care indicator system . Project Structure : Advisory Boards To make the ACOVE tools as clinical ly useful as possible and to enhance their value to those who may use them , two advisory committees oversaw the project . The Clinical Committee directed the definition of the target population and identified the target conditions . It also review ed the full set of quality indicators assembled by the expert panels ( see the Methods paper ) . The Policy Advisory Committee directed the structure of the quality indicator system and the measurement approach . This committee review ed the sampling system , considered the value of a community surveybased sample versus a sample drawn from administrative data , advised the project on methods of data collection , and advocated the use of process measures rather than outcomes .Project Overview The Appendix lists the members and institutional affiliations of the Clinical Committee and the Policy Advisory Committee and ACOVE investigators and staff . Definition and Selection of the Patient Sample We termed our target sample vulnerable elders . With the assistance of the Clinical Committee , we defined vulnerable elders as persons 65 years of age and older who are at increased risk for death or functional decline . We considered several methods of identifying a community-based sample for measurement , including use of administrative data and survey sampling . The Policy Advisory Committee advised against using a sampling method based solely on administrative data because , by definition , using utilization data to define health status and disease burden disproportionately sample s persons with higher utilization of health services and may not identify persons who are undertreated or underdiagnosed . We selected a survey method that would identify vulnerable elders regardless of the amount of health care they used and that would be versatile for use across venues and health insurance systems . Using the Medicare Current Beneficiary Survey , we determined that functional status is a more important predictor of death and functional decline than are specific clinical conditions . A parsimonious set of factors that could be asked about in a brief interview , including age , self-rated health , and functional disabilities and limitations , predicted functional decline and death . Using these factors , we developed a scoring system that identified 32 % of a nationally representative sample as vulnerable : This group had more than four times the risk for death or functional decline over a 2-year period compared to the lower-scoring majority of the sample ( 10 ) . When we applied this definition to a r and om sample of persons 65 years of age and older enrolled in managed care plans , 21 % of elders were classified as vulnerable . Target Conditions for Assessing Quality of Care We selected the target conditions for quality -of-care assessment using literature review s and a formal group-judgment process . Target conditions were conceptualized as prevalent , important to health status and quality of life , having outcomes affected by medical care , feasible to measure , and geriatric . The Clinical Committee used these criteria to select 21 diseases , geriatric syndromes , physiologic impairments , and clinical situations ( see the Methods article for a list of these 21 conditions ) . The Policy Advisory Committee added a 22nd condition , continuity and coordination of care . According to national survey estimates , the prevalence of the selected conditions ranged from 10 % to 50 % among community-dwelling elders . These conditions accounted for at least 43 % of acute hospital admissions and 33 % of office visits for persons 65 years of age and older . The process of selecting the conditions is discussed in detail elsewhere ( 11 ) . Developing  Quality -of-Care Indicators  for Vulnerable Elders Clinical experts developed potential quality indicators for each target condition using existing guidelines and clinical opinion . Indicators covered the domains of prevention , diagnosis , treatment , and follow-up . Structured literature review s were then performed to assess the evidence supporting a link between the proposed processes of care and improved outcomes in older adults . We subsequently presented the proposed quality indicators and the literature syntheses to panels of experts in geriatric care and medical specialists . These expert panels considered the indicators ' validity and feasibility . On the basis of the panels ' ratings and a comprehensive review of the set by the Clinical Committee , we developed a set of 236  quality indicators  covering the 22 conditions . The Methods article describes this process in detail . This set of indicators was review ed and modified by the American College of PhysiciansAmerican Society of Internal Medicine ( ACPASIM ) Task Force on Aging . The full set of quality indicators with the literature substantiation for 11 of the conditions constitute the remainder of this supplement . A review of the accepted quality indicators reveals their decidedly geriatric nature . Many of them focus on the transfer of information between the health care provider and the patient or patient 's proxy . In addition to disease management , the indicators pay attention to detecting classically underidentified conditions , such as depression , dementia , and functional impairment . Consideration of proportionate care , informed consent , and end-of-life issues permeates the set . Implementing the Quality Indicators We developed instruments to implement the quality indicators using information collected from three sources : the medical record , a patient ( or , if necessary , proxy ) interview , and administrative data . Medical records include all inpatient and outpatient care over a 1-year period . The interview is design ed to be conducted by telephone with the patient to identify aspects of care that are not well captured in the medical record . The  quality indicators  presented here are currently undergoing feasibility testing in two managed care plans . Scoring the quality indicators accounts for clinical circumstances , patient preferences , and patient prognosis . The scoring of each quality indicator accounts for these factors at the following five levels : 1 . Any chart documentation indicating that a care process does not apply to the patient excludes application of this indicator to the patient . 2 . Any care process that is contraindicated because of comorbidity , allergy , or intolerance ( such as contraindications to medication ) excludes application of the indicator to the patient . 3 . If an indicator states that a care process should be offered to a patient , this indicator is satisfied if the care process occurs , if the medical record ( or patient interview ) indicates that the patient rejected the care process , or if the medical record ( or patient interview ) documents that the care process was offered to the patient . 4 . If the medical record contains documentation or if during an interview a patient states that he or she would not want , under any circumstances , to be hospitalized , then the 11 indicators that would require hospitalization do not apply . Similarly , a preference not to accept surgery would exclude from application to that patient the 9 indicators that prescribe surgery or procedures that would lead to surgery . 5 . If a patient has advanced dementia or a chart-documented poor prognosis ( expected survival < 6 months ) , then BACKGROUND As part of the Affordable Care Act , health utility metrics are being investigated to define a cost-effective , value-based health care model . EuroQOL-5D ( EQ-5D ) and Short Form-6D ( SF-6D ) are commonly used quality -of-life instruments . Domains in the EQ-5D question naire are thought to be less responsive in measuring quality of life after cervical surgery . OBJECTIVE To evaluate the validity and responsiveness of SF-6D and EQ-5D in determining health and quality of life after elective cervical spine surgery . METHODS A total of 420 patients undergoing elective cervical spine surgery over a period of 2 years were enrolled in a prospect i ve longitudinal registry . Patient-reported outcomes Neck Disability Index ( NDI ) , EQ-5D , and SF-12 were recorded . Based on previously published equations , SF-6D was calculated using NDI and SF-12 scores . Patients were asked whether " surgery met their expectations " ( meaningful improvement ) . The validity and relative responsiveness of SF-6D ( NDI ) , SF-6D ( SF-12 ) , and EQ-5D to discriminate between meaningful and nonmeaningful improvement were calculated . RESULTS Sixty-six percent of patients ( 277 ) reported a level of improvement after surgery that met their expectations ( meaningful improvement ) . SF-6D ( NDI ) ( area under the curve [ AUC ] = 0.69 ) was a more valid discriminator of meaningful improvement compared with the SF-6D ( SF-12 ) ( AUC = 0.65 ) and EQ-5D ( AUC = 0.62 ) . SF-6D ( NDI ) was also a more responsive measure compared with SF-6D ( SF-12 ) and EQ-5D ( st and ardized response mean difference : 0.66 , 0.48 , and 0.44 , respectively ) . CONCLUSION SF-6D is a more valid and responsive measure of general health and quality of life compared with EQ-5D . SF-6D derived from disease-specific disability scores was more valid and responsive than that derived from the generic preference-based SF-12 . Cost-effective studies should use SF-6D as a measure of QALY after cervical spine surgery Background The aim of the study was to develop a menopause-specific , preference-based health-related quality -of-life ( HRQoL ) index reflecting both menopausal symptoms and potential side-effects of Hormone Replacement Therapy ( HRT ) . Methods The study had three phases : the development of a health state classification , a prospect i ve valuation survey and the estimation of a model to interpolate HRQoL indices for all remaining health states as defined by the classification . A menopausal health state classification was developed with seven dimensions : hot flushes , aching joints/muscles , anxious/frightened feelings , breast tenderness , bleeding , vaginal dryness and undesirable and rogenic signs . Each dimension contains between three and five levels and defines a total of 6,075 health states . A sample of 96 health states was selected for the valuation survey . These states were valued by a sample of 229 women aged 45 to 60 , r and omly selected from 6 general practice lists in Sheffield , UK . Respondents were asked to complete a time trade-off ( TTO ) task for nine health states , result ing in an average of 16.5 values for each health state . Results Mean health states valued range from 0.48 to 0.98 ( where 1.0 is full health and zero is for states regarded as equivalent to death ) . Symptoms , as described by the classification system , can be rank-ordered in terms of their impact ( from high to low ) on menopausal HRQoL as follows : aching joints and muscles , bleeding , breast tenderness , anxious or frightened feelings , vaginal dryness , and rogenic signs . Hot flushes did not significantly contribute to model fit . The preferred model produced a mean absolute error of 0.053 , but suffered from bias at both ends of the scale . Conclusion This article presents an attempt to directly value a condition specific health state classification . The overall fit was disappointing , but the results demonstrate that menopausal symptoms are perceived by patients to have a significant impact on utility . The overall effect is modest compared to the more generic health state descriptions such as the EQ-5D . The result ant algorithm generates a preference-based index that can be used economic evaluation and that reflects the impact of this condition Background . The Health Utilities Index Mark 3 ( HUI3 ) is a generic multiattribute preference‐based measure of health status and health‐related quality of life that is widely used as an outcome measure in clinical studies , in population health surveys , in the estimation of quality ‐adjusted life years , and in economic evaluations . HUI3 consists of eight attributes ( or dimensions ) of health status : vision , hearing , speech , ambulation , dexterity , emotion , cognition , and pain with 5 or 6 levels per attribute , varying from highly impaired to normal . Objectives . The objectives are to present a multiattribute utility function and eight single‐attribute utility functions for the HUI3 system based on community preferences . Study Design . Two preference surveys were conducted . One , the modeling survey , collected preference scores for the estimation of the utility functions . The other , the direct survey , provided independent scores to assess the predictive validity of the utility functions . Measures . Preference measures included value scores obtained on the Feeling Thermometer and st and ard gamble utility scores obtained using the Chance Board . Respondents . A r and om sample of the general population ( ≥16 years of age ) in Hamilton , Ontario , Canada . Results . Estimates were obtained for eight single‐attribute utility functions and an overall multiattribute utility function . The intraclass correlation coefficient between directly measured utility scores and scores generated by the multiattribute function for 73 health states was 0.88 . Conclusions . The HUI3 scoring function has strong theoretical and empirical foundations . It performs well in predicting directly measured scores . The HUI3 system provides a practical way to obtain utility scores based on community preferences Background Although evidence exists for the effectiveness of web-based smoking cessation interventions , information about the cost-effectiveness of these interventions is limited . Objective The study investigated the cost-effectiveness and cost-utility of two  web-based computer-tailored ( CT ) smoking cessation interventions ( video- vs. text-based CT ) compared to a control condition that received general text-based advice . Methods In a r and omized controlled trial , respondents were allocated to the video-based condition ( N = 670 ) ,  the text-based condition ( N = 708 ) or  the control condition ( N = 721 ) . Societal costs , smoking status , and quality -adjusted life years ( QALYs ; EQ-5D-3L ) were assessed at baseline , six- and twelve-month follow-up . The incremental costs per abstinent respondent and per QALYs gained were calculated . To account for uncertainty , bootstrapping techniques and sensitivity analyses were carried out . Results No significant differences were found in the three conditions regarding demographics , baseline values of outcomes and societal costs over the three months prior to baseline . Analyses using prolonged abstinence as outcome measure indicated that from a willingness to pay of € 1,500 , the video-based intervention was likely to be the most cost-effective treatment , whereas from a willingness to pay of € 50,400 , the text-based intervention was likely to be the most cost-effective . With regard to cost-utilities , when quality of life was used as outcome measure , the control condition had the highest probability of being the most preferable treatment . Sensitivity analyses yielded comparable results . Conclusion The video-based CT smoking cessation intervention was the most cost-effective treatment for smoking abstinence after twelve months , varying the willingness to pay per abstinent respondent from € 0 up to € 80,000 . With regard to cost-utility , the control condition seemed to be the most preferable treatment . Probably , more time will be required to assess changes in quality of life . Future studies with longer follow-up periods are needed to investigate whether cost-utility results regarding quality of life may change in the long run . Trial Registration Nederl and s Trial Register BACKGROUND The exacerbations of chronic obstructive pulmonary disease tool ( EXACT ) is a condition-specific daily diary recently developed to evaluate the frequency , severity , and duration of chronic obstructive pulmonary disease ( COPD ) exacerbations . A preference-based algorithm for the EXACT would allow utilities to be reported from patients during an exacerbation when EQ-5D data are not available . OBJECTIVE To develop the exacerbations of chronic obstructive pulmonary disease tool-utility ( EXACT-U ) , a condition-specific preference-based measure to report utilities from the EXACT for use in cost-effectiveness studies . METHODS Five items with three to five levels comprise the EXACT-U. Two groups of health states and respondents were constructed to allow for model development ( Development group ) and predictive validity testing ( Validation group ) using independent sample s. Members of the UK general public each valued 11 r and omized health states using time trade-offs ( TTOs ) scaled from full health/dead with 10-year duration s. Regression models estimated from the Development group using individual data , mean data , and panel design s. Models assessed by number of inconsistent coefficients estimated and R(2 ) and tested against observed utilities from the Validation group using mean absolute error ( MAE ) and root mean squared error ( RMSE ) . RESULTS A total of 55 health states , including the best and worst states , were valued in TTO interviews conducted with 400 respondents . Ten models were developed . The final preferred model contained no logical inconsistencies and found MAE = 0.04 and RMSE = 0.05 with a predicted utility range from 0.09 to 0.95 . CONCLUSIONS The EXACT-U is a condition-specific preference-based measure with strong predictive validity to report daily utilities during an exacerbation Background . Currently there are no reported cancer-specific health state valuations in low- and middle-income countries using a vali date d preference-based measure . The EORTC-8D , a cancer-specific preference-based measure , has 81,920 health states and is useful for economic evaluations in cancer care . The aim of this study was to develop a utility algorithm to value EORTC-8D health states using preferences derived from a representative population sample in Sri Lanka . Methods . The time-tradeoff method was used to elicit preferences from a general population sample of 780 in Sri Lanka . A block design of 85 health states , with a time horizon of 10 years , was used for the direct valuation . Data were analyzed using generalized least squares with r and om effects . All respondents with at least one logical inconsistency were excluded from the analysis . Results . After logical inconsistencies were excluded , 4520 observations were available from 717 respondents for the analysis . The preferred model specified main effects with an interaction term for any level 4 or worse descriptor within a health state . Worsening of physical functioning had a substantially greater utility decrement than any other dimension in this population . Limitations are that the data collection could not include the whole country and that females formed a large part of the sample . Conclusions . Preference weights for EORTC-8D health states for Sri Lanka have been derived : These will be very useful in economic evaluations of cancer-related interventions in a range of low- and middle-income countries Background . SF-6D utility weights are conventionally produced using a st and ard gamble ( SG ) . SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques . Recent advances in discrete choice methods have allowed estimation of utility weights . The objective was to produce Australian utility weights for the SF-6D and to explore the application of discrete choice experiment ( DCE ) methods in this context . We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D . Methods . We design ed an online DCE and administered it to an Australia-representative online panel ( n = 1017 ) . A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a r and om-effects probit model . The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0 , respectively , to provide an algorithm for Australian cost-utility analyses . Results . Physical functioning , pain , mental health , and vitality were the largest drivers of utility weights . Combining levels to remove illogical orderings did not lead to a poorer model fit . Relative to international SG-derived weights , the range of utility weights was larger with 5 % of health states valued below zero . Conclusion s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments . Australian cost-utility analyses can now use domestic SF-6D weights . The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality -adjusted life-year calculations should be further investigated Patients hope that their doctors will correctly identify health problems , clearly articulate sensible options for managing each problem , wisely interpret the best available evidence about probable outcomes associated with each option , sensitively solicit patient preferences for each outcome , and thereby implement practice s that are likely to achieve the results patients care about . Groups of patients hope that doctors will work to prevent , detect , and treat health problems in a way that maximizes the public good achieved with available health care re sources . To meet these dual expectations , doctors face forbidding tasks of clinical decision making . Improved methods for preparing , appraising , and abstract ing overviews of available evidence about a clinical topic can bring a broader range of high- quality information within the busy practitioner 's grasp [ 1 - 4 ] . However , even well- design ed overviews often do not synthesize information about benefits , harms , and costs of medical practice s in a way that facilitates clinical decision making . Clinical practice guidelines have been defined as systematic ally developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances [ 5 ] . Guidelines that are based on sound scientific evidence , and a trustworthy process for judging the value of alternative practice s , could help physicians deal with the information overload they face . Existing practice guidelines , however , vary widely in their quality , and clinicians should carefully consider the validity and applicability of each guideline they read . We propose a structured format for abstract s of articles describing practice guidelines that is based on emerging principles of guideline development and evaluation [ 6 - 13 ] . The proposal is design ed to help readers obtain the key information needed to assess the applicability , importance , and validity of any guideline . The proposed structure for more informative abstract s of articles describing clinical practice guidelines has been review ed and pilot-tested by guideline developers , evaluators , and implementors and by groups of practicing clinicians . Early versions of the proposal were sent to consultants at the National Library of Medicine , the Institute of Medicine , the American College of Physicians ( ACP ) , and the Agency for Health Care Policy and Research . Subsequent versions of the proposal were pilot tested on various published guidelines and were assessed by selected health care organizations for applicability to guidelines under development . An improved proposal was sent to 82 persons active in guideline development , evaluation , and implementation . The Internal Medicine Center to Advance Research and Education circulated the proposal to volunteer practitioners , and the authors solicited feedback from physician and health administrator participants at three national workshops about practice guidelines . The proposal that follows represents an integration of the collective wisdom of our external review ers . ( A glossary of terms used in articles on clinical practice guidelines is supplied in the Appendix . ) Proposed Structure and Content for Abstract s of Articles Describing Clinical Practice Guidelines The proposed structure for abstract s of papers describing clinical practice guidelines is summarized in Table 1 . Key elements of the guideline development process are captured with statements about the principal objective and target of the guideline , the main practice options and outcomes that were considered , the nature of the evidence and values that were analyzed , the main benefits and harms expected from guideline implementation , the key recommendations , and a statement of whether the recommendations have been tested and by whom they have been endorsed . If details about guideline development methods or supporting evidence are published separately , then the abstract should indicate the existence of supporting documents and the most relevant parts of those should be summarized in the abstract . To be included in their entirety in the online bibliographic data bases of the National Library of Medicine , structured abstract s of clinical practice guidelines should not exceed 4096 characters ( approximately 500 words ) in length . Individual journals may have different requirements . Table 1 . Proposed Format for Structured Abstract s of Clinical Practice Guidelines Objective The abstract should state the objective of the guideline by identifying the targeted health problem , the targeted patients and physicians , and the main reason or reasons for developing new recommendations concerning this problem for this population . For guidelines primarily concerned with the management of health conditions , the stage of illness and intent to prevent , diagnose , treat , or palliate the disorder should be specified . For guidelines primarily concerned with health procedures , an intervention and its role in patient management should be defined . Targeted providers , setting s , and patients should be specified so that readers can tell who should do what , where it should be done , and to whom . A statement of objective also may specify why a new guideline is needed and how it should be used . Practice guidelines may be written to clarify or resolve clinical controversies , to communicate important new findings from clinical studies , or to promote more effective , efficient , or consistent physician practice s at less cost or risk to patients . Guidelines may be used concurrently to help physicians and patients make choices ( for example , clinical algorithms ) , retrospectively to compare physician choices to pre-established criteria ( for example , utilization review ) , or prospect ively to set limits on physician choices ( for example , preprocedure approval ) . Options Clinical practice guidelines pertain to clinical decisions . The nature of those decisions should be made explicit by disclosing the main management options considered . The abstract should list principal alternative preventive , diagnostic , or therapeutic strategies available to targeted clinicians and patients . Outcomes The abstract also should specify which health outcomes , such as death , morbidity , quality of life , economic costs , or changes in the process of health care were considered . Evidence To estimate the probable effect of a health intervention on an outcome , relevant data must be defined , gathered , and appraised . Readers need to know what kind of evidence was considered and what methods were used to select and combine evidence from different sources . Potential sources of evidence about clinical outcomes include the results of clinical studies ( published or unpublished ) , expert opinion , information contained in public or private health data bases , and direct input from patients or providers . Potential sources of evidence about economic outcomes include the results of cost-effectiveness studies , various fee schedules , and information contained in public or private health cl aims data bases . Potential methods of gathering evidence include computerized search es for articles that satisfy predetermined criteria , review s of bibliographies of relevant articles , and surveys of relevant persons or organizations [ 1 ] . The abstract should state whether explicit methods were used to combine results from scientific studies ( for example , meta- analysis ) or opinions of experts ( for example , Delphi technique ) [ 14 , 15 ] . The abstract may also indicate whether specific criteria were used to gauge the quality of information from different sources [ 16 , 17 ] . Scientific evidence regarding the likelihood of certain outcomes is often missing or conflicting . It is desirable for the strength of evidence supporting a guideline to be gauged in some way [ 16 ] . If gaps in the evidence were bridged formally ( for example , through use of decision- analysis models ) or informally ( for example , best guess of clinical experts ) , then major assumptions or areas of uncertainty should be acknowledged [ 11 ] . Because of the extraordinary time required to properly assemble and review evidence and then to achieve consensus about practice recommendations , the abstract of a practice guideline should specify the publication date s of the most recent evidence considered . If a sensitivity analysis was performed , then any major vulnerability of the guideline to weaknesses in the evidence should be acknowledged . These considerations may be summarized by qualifying a guideline as provisional by specifying date s for expiration or review , by listing studies in progress that could affect the guideline , or by identifying key research priorities . Values The declaration of a recommended practice presumes an implicit or explicit process for judging the relative desirability of the health , economic , and process outcomes associated with alternative practice s. These are matters of opinion and value . For example , in developing guidelines for breast cancer screening , it is necessary to weigh the value of preventing a death from breast cancer against the value of avoiding unnecessary harm or anxiety in women who do not have breast cancer . A high value placed on maximizing detection of asymptomatic malignancy may motivate an organization to recommend teaching self-examination of breasts to young women [ 18 ] . A relatively greater value placed on avoiding false-positive results may motivate others to not promote such programs [ 19 ] . Consequently , the principal sources of such values and the method by which judgments were made should be reported in guideline abstract s. At a minimum , the abstract should identify the major groups that participated in the process of assigning values to outcomes ( for example , generalist or specialist physicians , other health care providers , insurers , representatives of health organizations , and special-interest groups ) and whether patient preferences were represented . The abstract may also indicate the method used to synthesize opinions from Background To facilitate the discussion on the increasing number of total hip replacements ( THR ) and their effectiveness , we apply a joint evaluation of hospital case costs and health outcomes at the patient level to enable comparative effectiveness research ( CER ) based on the preoperative health state . Methods In 2012 , 292 patients from a German orthopedic hospital participated in health state evaluation before and 6 months after THR , where health-related quality of life ( HRQoL ) and disease specific pain and dysfunction were analyzed using EQ-5D and WOMAC scores . Costs were measured with a patient-based DRG costing scheme in a prospect i ve observation of a cohort . Costs per quality -adjusted life year ( QALY ) were calculated based on the preoperative WOMAC score , as preoperative health states were found to be the best predictors of QALY gains in multivariate linear regressions . Results Mean inpatient costs of THR were 6,310 Euros for primary replacement and 7,730 Euros for inpatient lifetime costs including revisions . QALYs gained using the U.K. population preference-weighted index were 5.95 . Lifetime costs per QALY were 1,300 Euros . Conclusions The WOMAC score and the EQ-5D score before operation were the most important predictors of QALY gains . The poorer the WOMAC score or the EQ-5D score before operation , the higher the patient benefit . Costs per QALY were far below common thresholds in all preoperative utility score groups and with all underlying calculation method ologies For many years , the National Institute for Health and Care Excellence ( NICE ) has recommended use of the EQ-5D-3L ( 3L ) [ 1 ] and its value set for the UK [ 2 ] . Since 2011 , an exp and ed-level instrument , the EQ-5D-5L ( 5L ) , has been available [ 3 ] and value sets now exist to support its use , including a value set for Engl and [ 4 , 5 ] . This poses a challenge for NICE . Should it recommend the 5L rather than the 3L ? This is neither a trivial nor merely academic matter : the choice of whether to use the 5L ( and English value set ) or the 3L ( and UK value set ) is likely to impact estimates of quality -adjusted life-years ( QALYs ) and incremental cost-effectiveness ratios ( ICERs ) . The size and direction of that impact will depend on the disease and the nature of the health problems . In general , where technologies improve self-reported health , estimates of QALY gains will often be smaller with the 5L [ 6 ] . In contrast , where technologies extend the length of life , estimates of QALY gains will be higher ( to varying degrees ) : each year of additional life is assigned a higher utility . The ultimate impact on health technology assessment ( HTA ) will depend on whether the differences between the 3L and 5L push ICERs from one side of the cost-effectiveness threshold to the other . Given the implication s for NICE ’s technology appraisal process , and other decisions informed by EQ-5D data , the Department of Health for Engl and has called for an independent validation of the 5L value set , given its relevance to policy [ 7 ] . In 2017 , NICE released a ‘ position statement ’ [ 8 ] stating that : The 3L value set continues to be used for reference-case analyses . Where 5L data have been collected , reference-case analyses should calculate utilities by mapping the 5L descriptive system data onto the 3L value set , using the van Hout et al. [ 9 ] mapping function . NICE supports sponsors of prospect i ve clinical studies continuing to use the 5L to collect data on quality of life . A further position statement is planned for August 2018 , to be informed by evidence from various studies underway . These include studies commissioned by the English Department of Health to investigate the implication s for past NICE technology appraisal s had the 5L been used , and to collect 3L and 5L data in parallel to further improve functions for mapping from one to the other . Other studies , funded by the EuroQol Group , are also underway , investigating various aspects of the relationship between the 3L and 5L across disease areas . The 3L and its UK value set has occupied a special place in NICE ’s technology appraisal process since its inception , therefore any transition will inevitably pose challenges ; for example , reconciling potential inconsistencies between past and future decisions . Given that evidence will continue to be su bmi tted using both the 3L and 5L for years to come , if both value sets are able to be used , there is a risk of inconsistency between decisions being made in the future . HTA in other countries may also face similar issues . Given the difficulties with any transition away from the 3L , is there a case for NICE to adopt the 5L as its preferred instrument ? Papers in this issue of Pharmacoeconomics , which are cited in this commentary , address that question by investigating comparative performance of the 3L and 5L Abstract Objectives : The objective of this study was to obtain utilities , or preference-based quality -of-life values , from the Canadian general public , for potential health states experienced by immune thrombocytopenia ( ITP ) patients receiving either romiplostim ( a new thrombopoietin mimetic agent ) or ‘ watch and rescue ’ therapy .  Utilities are needed to conduct a cost-utility analysis of romiplostim for formulary and reimbursement decisions . Methods : An electronic Time Trade-off ( TTO ) survey was developed and administered to a sample of the general public in Canada , with 12 distinct health states derived from two r and omized clinical trials of romiplostim vs watch and rescue treatment . Two pilot tests assessed interpretability and respondent burden . In the final survey , each subject was administered the TTO for four r and omly-selected health states . Descriptive statistics were computed for utility scores , and differences between health states were evaluated with an analysis of variance model . Results : Eight hundred and twenty-one adults completed the TTO survey . Mean age was 36.4 ( SD = 15 ) years ; 63 % were female .  Mean ( SD ) utility scores ranged from 0.476 ( 0.271 ) for the most severe health state ( significant bleeding ) to 0.633 ( 0.282 ) for the least severe health state depicting successful treatment with romiplostim . Statistical significance was found on the mean difference between the most severe health state and five other health states ( p < 0.05 ) . After adjusting utilities for matching Canadian demographic parameters , no substantial difference was found between original utility scores and adjusted scores . Conclusions : This study provides evidence of the Canadian general public ’s preference for 12 ITP health states pertaining to romiplostim treatment or watch and rescue . This study had a number of limitations , the main ones being the lack of perfect match in demographics between this sample and the Canadian population , as well as the fact that the scenario descriptions were based on both published literature and expert opinion . Despite those limitations , the obtained utility scores may be used in cost-utility models of romiplostim as a treatment for ITP patients in Canada There remains disagreement about the preferred utility-based measure of health-related quality of life for use in constructing quality -adjusted life years ( QALYs ) . The recent development of a new measure , the SF-6D , has highlighted this issue . The SF-6D and EuroQol EQ-5D measure health-related utilities on a scale where 0 represents death and 1 represents full health , and both have utility scores generated from r and om sample s of the general UK population . This study explored whether , in a large sample of liver transplant patients , the two instruments provide similar results . The empirical data highlight important variation in the results generated from the use of the two instruments . The data are consistent with a view that the SF-6D does not describe health states at the lower end of the utility scale but is more sensitive than EQ-5D in detecting small changes towards the top of the scale Background Generic health preference measures that capture quality of life improvements in depression are important for economic analysis of new technologies . Aim To compare two widely used preference measures , the EQ-5D and SF-6D , in terms of their dimensions and health utility values . Method EQ-5D and SF-6D data collected from 114 patients with depression , who participated in a cluster , r and omised controlled trial to evaluate a collaborative care intervention in UK , primary care practice s.  Utilities were examined across the whole sample and by level of depression severity using the PHQ-9 . Results Depression was associated with disutility at baseline . At 3 month follow-up mean utility increased 0.147 for EQ-5D and 0.082 for SF-6D . Health gains were observed in patients in remission from depression and those with the mildest level of depression severity . Conclusions Both generic preference measures were sensitive to health gains in depression within a relatively short follow-up period ; larger health gain was observed for the EQ-5D Background Many intervention-based studies aim ing to improve mental health do not include a multi-attribute utility instrument ( MAUI ) that produces quality -adjusted life-years ( QALYs ) and it limits the applicability of the health economic analyses . This study aims to develop ‘ crosswalk ’ transformation algorithm between a measure for psychological distress General Health Question naire ( GHQ-12 ) and MAUI EuroQoL ( EQ-5D-3L ) . Methods The study is based on a survey question  naire sent to a r and om sample in four counties in Sweden in 2012 . The survey included  GHQ-12 and EQ-5D instruments  , as well as a question about self-rated health . The EQ-5D index was calculated using the UK and the Swedish tariff values . Two OLS models were used to estimate the EQ-5D health state values using the GHQ-12 as exposure , based on the respondents ( n = 17 , 101 ) of two counties . The algorithms were applied to the data from two other counties , ( n = 15 , 447 ) to check the predictive capacity of the models . Results The final models included gender , age , self-rated health and GHQ-12 scores as a quantitative variable . The regression equations explained 40 % ( UK tariff ) and 46 % ( Swedish tariff ) of the variances . The model showed a satisfying predictive capacity between the observed and the predicted EQ-5D index score , with Pearson correlation = 0.65 and 0.69 for the UK and Swedish models , respectively . Conclusion The algorithms developed in this study can be used to determine cost-effectiveness of services or interventions that use GHQ-12 as a primary outcome where the utility measures are not collected Purpose : The EQ-5D is a brief , multiattribute , preference-based health status measure . This article describes the development of a statistical model for generating US population -based EQ-5D preference weights . Methods : A multistage probability sample was selected from the US adult civilian noninstitutional population . Respondents valued 13 of 243 EQ-5D health states using the time trade-off ( TTO ) method . Data for 12 states were used in econometric modeling . The TTO valuations were linearly transformed to lie on the interval [ −1 , 1 ] . Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions . Several alternative model specifications ( eg , pooled least squares , r and om effects ) also were considered . A modified split- sample approach was used to evaluate the predictive accuracy of the models . All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design . Results : Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects . A r and om effects specification of the D1 model yielded a good fit for the observed TTO data , with an overall R2 of 0.38 , a mean absolute error of 0.025 , and 7 prediction errors exceeding 0.05 in absolute magnitude . Conclusions : The D1 model best predicts the values for observed health states . The result ing preference weight estimates represent a significant enhancement of the EQ-5D 's utility for health status assessment and economic analysis in the US
Conclusion Various methods have been used at all stages of CSPBM development .
The choice between developing a classification system de novo or from an existing instrument may depend on the availability of a suitable existing measure , while the choice between a decomposed or composite approach appears to be determined primarily by the purpose for which the instrument is design ed .
Background Health state utility values ( HSUVs ) are required to calculate quality -adjusted life-years ( QALYs ) .
They are frequently derived from generic preference-based measures of health .
However , such generic measures may not capture health attributes of relevance to specific conditions .
In such cases , a condition-specific preference-based measure ( CSPBM ) may be more appropriate .
Objective This systematic review aim ed to identify all published accounts of developing CSPBMs to describe and appraise the methods used .