Health State Classification System Research Articles

623 Validating the Bladder Utility Symptom Scale (BUSS): A multi attribute health state classification system for bladder cancer

623 Validating the Bladder Utility Symptom Scale (BUSS): A multi attribute health state classification system for bladder cancer

The DEP-6D, a new preference-based measure to assess health states of dependency

In medical literature there are numerous multidimensional scales to measure health states for dependence in activities of daily living. However, these scales are not preference-based and are not able to yield QALYs. On the contrary, the generic preference-based measures are not sensitive enough to measure changes in dependence states. The objective of this paper is to propose a new dependency health state classification system, called DEP-6D, and to estimate its value set in such a way that it can be used in QALY calculations. DEP-6D states are described as a combination of 6 attributes (eat, incontinence, personal care, mobility, housework and cognition problems), with 3–4 levels each. A sample of 312 Spanish citizens was surveyed in 2011 to estimate the DEP-6D preference-scoring algorithm. Each respondent valued six out of the 24 states using time trade-off questions. After excluding those respondents who made two or more inconsistencies (6% out of the sample), each state was valued between 66 and 77 times. The responses present a high internal and external consistency. A random effect model accounting for main effects was the preferred model to estimate the scoring algorithm. The DEP-6D describes, in general, more severe problems than those usually described by means of generic preference-based measures. The minimum score predicted by the DEP-6D algorithm is −0.84, which is considerably lower than the minimum value predicted by the EQ-5D and SF-6D algorithms. The DEP-6D value set is based on community preferences. Therefore it is consistent with the so-called ‘societal perspective’. Moreover, DEP-6D preference weights can be used in QALY calculations and cost-utility analysis.

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30.

To derive a health state classification system (HSCS) from the cancer-specific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multi-attribute utility instrument. The conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori selection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75 % observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions. CFA supported the conceptual model and its generalisability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems. The HSCS created from QLQ-C30 items is known as the EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The next phase of the QLU-C10D's development involves valuation studies, currently planned or being conducted across the globe.

Estimating a Preference-Based Index for an Eight-Dimensional Health State Classification System for Multiple Sclerosis

BackgroundCondition-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. ObjectivesTo 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). MethodsWe 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. ResultsParticipants reported that the time trade-off questions were easy to understand. Observed health state values ranged from 0.08 to 0.89. The best-performing model was a main effects, random 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. ConclusionsThe 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 contexts.

Deriving a Preference-Based Measure for Myelofibrosis from the EORTC QLQ-C30 and the MF-SAF

BackgroundUtility values are required for economic evaluation using cost-utility analyses. Often, generic measures such as the EuroQol five-dimensional questionnaire are used, but this may not appropriately reflect the health-related quality of life of patients with cancer including myelofibrosis. ObjectiveTo derive a condition-specific preference-based measure for myelofibrosis using appropriate existing measures, the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire. MethodsData from the Controlled Myelofibrosis Study with Oral JAK Inhibitor Treatment trial (n = 309) were used to derive the health state classification system. Psychometric and factor analyses were used to determine the dimensions of the classification system. Psychometric and Rasch analyses were then used to select an item to represent each dimension. Item selection was validated with experts. A selection of health states was valued by members of the general population using time trade-off. Finally, health state values were modeled using regression analysis to produce utility values for every state. ResultsThe Myelofibrosis 8 dimensions has eight dimensions: physical functioning, emotional functioning, fatigue, itchiness, pain under ribs on the left side, abdominal discomfort, bone or muscle pain, and night sweats. Regression models were estimated using time trade-off data from 246 members of the general population valuing a total of 33 states. The best performing model was a random effects maximum likelihood model producing utility values ranging from 0.089 to 1. ConclusionsThe Myelofibrosis 8 dimensions is a condition-specific preference-based measure for myelofibrosis. This measure can be used to generate utility values for myelofibrosis for any data set containing the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire data.

A Quality-Adjusted Life-Year Measure for Multiple Sclerosis: Developing a Patient-Reported Health State Classification System for a Multiple Sclerosis-Specific Preference-Based Measure

BackgroundIncreasingly, generic preference-based measures of health-related quality of life (HRQOL) are used to estimate quality-adjusted life-years to inform resource allocation decisions. Evidence suggests that generic measures may not be appropriate for multiple sclerosis (MS). ObjectivesTo report the first stage in the development of an MS-specific preference-based measure to quantify the impact of MS and its treatment: deriving a health state classification system, which is amenable to valuation, from the 29-item Multiple Sclerosis Impact Scale (MSIS-29), a widely used patient-reported outcome measure in MS. MethodsThe dimensional structure of the MSIS-29 was determined using factor analysis and a conceptual framework of HRQOL in MS. Item performance was assessed, using Rasch analysis and psychometric criteria, to enable the selection of one item to represent each dimension of HRQOL covered by the MSIS-29. Analysis was undertaken using a sample (N = 529) from a longitudinal study of people with MS. Results were validated by repeating the analysis with a second sample (N = 528). ResultsFactor analysis confirmed the two-subscale structure of the MSIS-29. Both subscales covered several conceptually independent dimensions of HRQOL. Following Rasch and psychometric analysis, an eight-dimensional classification system named the MSIS-8D was developed. Each dimension was represented by one item with four response levels. ConclusionsCombining factor analysis with conceptual mapping, and Rasch analysis with psychometric criteria, provides a valid method of constructing a classification system for an MS-specific preference-based measure. The next stage is to obtain preference weights so that the measure can be used in studies investigating MS.

The EQ-5D-5L index score is more discriminative than the EQ-5D-3L index score in diabetes patients.

To compare the discriminative power of the index scores of EQ-5D-5L (5L) and EQ-5D-3L (3L) in diabetes patients in China. A consecutive sample of type 2 diabetes mellitus (T2DM) patients in the clinics self-completed the two versions of EQ-5D. The 3L index score was obtained from the Chinese 3L value set; the 5L index score was predicted from the 3L index score using an interim scoring. Relative efficiency (RE) of the 5L and 3L index scores was calculated to compare their ability in differentiating between T2DM patients with and without one of ten clinical conditions. The efficiency of the 5L and 3L health state classification systems was assessed using the Shannon index (H') and in terms of ceiling effects. A total of 289 T2DM patients participated in this study. The 5L score was systematically lower than the 3L score for T2DM patients with and without a condition (range -0.36 to -0.06). The 5L score exhibited higher discriminative power in nine of ten conditions, with the mean RE value being 1.92. 5L had higher H' values than 3L in all the five EQ-5D dimensions: mobility (1.14 vs. 0.70), self-care (0.44 vs. 0.33), usual activities (0.72 vs. 0.47), pain/discomfort (1.58 vs. 1.10), and anxiety/depression (1.03 vs. 0.67). The overall ceiling effects decreased from 56.7 % (3L) to 36.7 % (5L). The 5L index score is more discriminative than the 3L index score in T2DM patients and therefore is preferable for use in this population.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30: a confirmatory versus exploratory approach.

BackgroundMulti attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure.AimTo determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure.MethodsQLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis.ResultsCFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA.ConclusionCFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

131: Health and Quality of Life Outcomes for a Cohort of Children with Severe Traumatic Brain Injury

The Multiattribute Health Status Classification System (MHSCS) is a parent/proxy administered tool including a series of health-specific domains that give levels of functioning between 1 (normal health) to 4 or 5 (most severe). Previous studies have used the MHSCS to document chronic health impairment and child morbidity (Gemke et al., 1995; 1996; Feeny et al., 1992; 1993; Saigal et al., 1994). There is a high inter-rater reliability exceeding 70% between caregivers and physiatrists with sensitivity and specificity from acute injury predictors at 75% and 70% (Robertson et al., 2001). The MHSCS is a useful parent-report surveillance tool to audit outcome after severe Traumatic Brain Injury (TBI). To assess the post-injury health outcomes of young children sustaining a severe TBI using the MHSCS. This is a retrospective chart review of children who sustained severe TBI before 12 years of age & were followed in the Brain Injury program. Severe TBI was defined as Glasgow Coma Scale =<8/15. Ethics approval was granted. Demographic variables were collected and expressed as mean (± SD) (range). A multivariate analysis of variance was conducted to compare scores on the MHSCS. A total of 18 children (10 male, eight female) mean age 6.84±3.4 years, (range 1.5 to 11.8 years) were reviewed at three years (n=8) or ≥5 years (n=10) post-injury. An independent t test indicated no significant difference between the Total scores at three and ≥5 years (P=0.23) (Chart 1). The overall difference across subscales was not significantly different (Chart 2). The Cognition Subscale scores increased significantly between three and ≥5 years (P=0.038) and the Emotional Subscale scores increased marginally (P=0.073). Outcomes of severe TBI persist beyond the acute recovery stages. Children experience significantly more difficulties with cognition and emotion at ≥5 years post injury than at three years. This may reflect emerging deficits as they face more complex material at school. Children also experience marginally more emotional difficulties at ≥5 years than at three years. This may reflect developmental maturity making them more aware of their TBI-related limitations. Although it was not significantly different, there was a trend towards lower scores for Mobility and Self-Care Subscales. The MHSCS was a useful parent-report surveillance tool to audit outcome after severe TBI.

MP15-01 DEVELOPING THE BLADDER UTILITY SYMPTOM SCALE: A MULTIATTRIBUTE HEALTH STATE CLASSIFICATION FOR BLADDER CANCER

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Quality of Life1 Apr 2014MP15-01 DEVELOPING THE BLADDER UTILITY SYMPTOM SCALE: A MULTIATTRIBUTE HEALTH STATE CLASSIFICATION FOR BLADDER CANCER Nathan Perlis, Murray Krahn, Shabbir Alibhai, Antonio Finelli, Paul Ritvo, Karen Bremner, Kirstin Boehme, and Girish Kulkarni Nathan PerlisNathan Perlis More articles by this author , Murray KrahnMurray Krahn More articles by this author , Shabbir AlibhaiShabbir Alibhai More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Paul RitvoPaul Ritvo More articles by this author , Karen BremnerKaren Bremner More articles by this author , Kirstin BoehmeKirstin Boehme More articles by this author , and Girish KulkarniGirish Kulkarni More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.554AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES In bladder cancer (BCa), treatment-related decision making is heavily preference-based. This is particularly important given multiple treatment options for several stages of the disease and the lack of randomized controlled trials to inform guidelines. Thus, a measure is required that adequately captures patient preference-based health-related quality of life (HRQOL). Such an instrument would facilitate patient-oriented care by providing physicians with quantitative information of patients’ experiences. METHODS We created a novel HRQOL questionnaire for BCa (the Bladder Utility Symptom Scale (BUSS) in a stepwise, iterative fashion with conceptual framework development, item generation, item reduction, question design and pilot testing. A systematic literature search in Medline, Embase, CINAHL and PsychInfo up to January 2013 was performed to generate initial items on the HRQOL effects of BCa and its treatments. Thematic synthesis was used to group related items into overarching themes (domains) and create a provisional conceptual framework. Purposive sampling was used to accrue patients of different ages, genders, disease severities and treatment histories to generate items important to all BCa patients. The clinically oriented “impact method” was used for item reduction. The questionnaire was pilot tested for face and content validity. RESULTS After a comprehensive literature review of 1275 citations, 170 full text articles were identified from which 169 unique items relevant to BCa patients were abstracted by two reviewers. In consultation with 47 BCa patients and 12 BCa experts various BCa-specific and generic domains and items important to their HRQOL were confirmed. A questionnaire with 10 multiple-choice questions and one visual analogue scale was created to address the most important items as rated and approved by BCa patients and experts. Twenty iterations of the BUSS were generated through pilot testing and interviews with 5 BCa experts and 40 BCa patients. The final BUSS questionnaire included 5 BCa-specific domains (storage urinary problems, urine leakage, sexual problems, bowel problems and impaired body image) and 5 generic domains (pain, vigor, impaired social relationships, impaired medical relationships and psychological problems). CONCLUSIONS The BUSS multiattribute health state classification system is a comprehensible instrument designed to measure global HRQOL for BCa patients. The next steps in its development are field testing, followed by generation of weights to calculate BUSS utility scores. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e144 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Nathan Perlis More articles by this author Murray Krahn More articles by this author Shabbir Alibhai More articles by this author Antonio Finelli More articles by this author Paul Ritvo More articles by this author Karen Bremner More articles by this author Kirstin Boehme More articles by this author Girish Kulkarni More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Using existing data to identify candidate items for a health state classification system in multiple sclerosis

In multiple sclerosis (MS), the use of preference-based measures is limited to generic measures such as Health Utilities Index Mark 2 and 3, the EQ-5D and the SF-6D. However, the challenge of using such generic preference-based measures in people with MS is that they may not capture all domains of health relevant to the disease. Therefore, the main aim of this paper is to describe the development of a health state classification system for MS patients. The specific objectives are: (1) to identify items best reflecting the domains of quality of life important to people with MS and (2) to provide evidence for the discriminative capacity of the response options by cross-walking onto a visual analog scale of health rating. The data come from an epidemiologically sampled population of people with MS diagnosed post-1994. The dataset consisted of 206 items relating to impairments, activity limitations, participation restrictions, health perception and quality of life. Important domains were identified from the responses to the Patient Generated Index, an individualized measure of quality of life. The extent to which the items formed a uni-dimensional, linear construct was estimated using Rasch analysis, and the best item was selected using the threshold map. The sample was young (mean age 43) and predominantly female (n = 140/189; 74%). The P-PBMSI classification system consisted of five items, with three response levels per item, producing a total of 243 possible health states. Regression coefficient values consistently decreased between response levels and the linear test for trend were statistically significant for all items. The linear test for trend indicated that for each item the response options provided the same discriminative ability within the magnitude of their capacity. A scoring algorithm was estimated using a simple additive formula. The classification system demonstrated convergent validity against other measures of similar constructs and known-groups validity between different clinical subgroups. This study produced a health state classifier system based on items impacted upon by MS, and demonstrated the potential to discriminate the health impact of the disease.

Validity of utility measures for women with urge, stress, and mixed urinary incontinence

We sought to evaluate the construct validity of 3 health status classification system instruments-Health Utilities Index Mark 3 (HUI-3), EuroQol (EQ-5D), and Short Form 6D (SF-6D)-and a visual analog scale (VAS) for measuring utility scores in women with urge, stress, and mixed urinary incontinence. Utility scores were measured in 202 women with urinary incontinence. Pelvic floor symptom severity and quality of life were measured using the Pelvic Floor Distress Inventory and Pelvic Floor Impact Questionnaire, respectively. Construct, discriminant, and concurrent validity were evaluated. Significant correlations were noted between utility scores and the Pelvic Floor Distress Inventory (r= -0.22 to -0.42, P < .05) and the Pelvic Floor Impact Questionnaire (r= -0.32 to -0.50, P < .05). Mean utility scores were significantly lower for women with urge or mixed incontinence compared to stress incontinence for the EQ-5D (0.71 ± 0.23, 0.73 ± 0.26, and 0.81 ± 0.16, respectively, P= .02) and the SF-6D (0.76 ± 0.12, 0.74 ± 0.12, and 0.81 ± 0.11, respectively, P= .02) but not the HUI-3 or the VAS. There was a clinically important difference in utility scores (>0.03) between women with urge or mixed incontinence as compared to stress incontinence for the HUI-3, EQ-5D, and SF-6D but not the VAS. Utility preference scores were significantly lower for women with combined urinary and fecal incontinence (0.69-0.73) than urinary incontinence alone (0.77-0.84, P < .01). The HUI-3, EQ-5D, and SF-6D, but not the VAS, provide valid measurements for utility scores in women with stress, urge, and mixed urinary incontinence.

Development of DEMQOL-U and DEMQOL-PROXY-U: generation of preference-based indices from DEMQOL and DEMQOL-PROXY for use in economic evaluation

Dementia is one of the most common and serious disorders in later life and the economic and personal cost of caring for people with dementia is immense. There is a need to be able to evaluate interventions in dementia using cost-effectiveness analyses, but the generic preference-based measures typically used to measure effectiveness do not work well in dementia. Existing dementia-specific measures can effectively measure health-related quality of life but in their current form cannot be used directly to inform cost-effectiveness analysis using quality-adjusted life-years as the measure of effectiveness. The aim was to develop two brief health-state classifications, one from DEMQOL and one from DEMQOL-Proxy, to generate health states amenable to valuation. These classification systems consisted of items taken from DEMQOL and DEMQOL-Proxy so they can be derived from any study that has used these instruments. In the first stage of the study we used a large, clinically representative sample aggregated from two sources: a sample of patients and carers attending a memory service in south London and a sample of patients and carers from other community services in south London. This included 644 people with a diagnosis of mild/moderate dementia and 689 carers of those with mild/moderate dementia. For the valuation study, the general population sample of 600 respondents was drawn to be representative of the UK general population. Households were sampled in urban and rural areas in northern England and balanced to the UK population according to geodemographic profiles. In the patient/carer valuation study we interviewed a sample of 71 people with mild dementia and 71 family carers drawn from a memory service in south London. Finally, the instruments derived were applied to data from the HTA-SADD (Study of Antidepressants for Depression in Dementia) trial. This was a complex multiphase study with four linked phases: phase 1 - derivation of the health-state classification system; phase 2 - general population valuation survey and modelling to produce values for every health state; phase 3 - patient/carer valuation survey; and phase 4 - application of measures to trial data. All four phases were successful and this report details this development process leading to the first condition-specific preference-based measures in dementia, an important new development in this field. The first limitation relates to the lack of an external data set to validate the DEMQOL-U and DEMQOL-Proxy-U classification systems. Throughout the development process we have made decisions about which methodology to use. There are other valid techniques that could be used and it is possible to criticise the choices that we have made. It is also possible that the use of a mild to moderate dementia sample has resulted in classification systems that do not fully reflect the challenges of severe dementia. The results presented are sufficiently encouraging to recommend that the DEMQOL instruments be used alongside a generic measure such as the European Quality of Life-5 Dimensions (EQ-5D) in future studies of interventions in dementia as there was evidence that they can be more sensitive for patients at the milder end of disease and some limited evidence that the person with dementia measure may be able to reflect deterioration. The National Institute for Health Research Health Technology Assessment programme.

Estimating a Preference-Based Index from the Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM)

Background. The Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders. The objective of this study was to estimate a preference-based index for this population using CORE-6D, a health state classification system derived from the CORE-OM consisting of a 5-item emotional component and a physical item, and to demonstrate a novel method for generating states that are not orthogonal. Methods. Rasch analysis was used to identify 11 emotional health states from CORE-6D that were frequently observed in the study population and are, thus, plausible (in contrast, conventional statistical design might generate implausible states). Combined with the 3 response levels of the physical item of CORE-6D, they generate 33 plausible health states, 18 of which were selected for valuation. A valuation survey of 220 members of the public in South Yorkshire, United Kingdom, was undertaken using the time tradeoff (TTO) method. Regression analysis was subsequently used to predict values for all possible states described by CORE-6D. Results. A number of multivariate regression models were built to predict values for the 33 health states of CORE-6D, using the Rasch logit value of the emotional state and the response level of the physical item as independent variables. A cubic model with high predictive value (adjusted R2 = 0.990) was selected to predict TTO values for all 729 CORE-6D health states. Conclusion. The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospective CORE-OM data sets. The new method for generating states may be useful for other instruments with highly correlated dimensions.

The development of a QALY measure for epilepsy: NEWQOL-6D

Cost-utility analysis is used to inform the allocation of healthcare resources, using the quality-adjusted life year (QALY) as the outcome measure. We report the development of an epilepsy-specific QALY measure (NEWQOL-6D) derived from the NEWQOL measure of health-related quality of life. Firstly, psychometric and Rasch analyses established the dimension structure of NEWQOL and generated a reduced health state classification system including one item per dimension. Secondly, health states generated by the classification system were valued using Time Trade Off, and the results were modeled to generate a utility score for every health state. A classification system with 6 dimensions (worry about attacks; depression; memory; concentration; stigma; control) was produced, and generalized least squares regression was used to generate utility scores for every health state. This study is the first attempt to derive an epilepsy-specific QALY measure, and the utility values can be used in the economic evaluation of emerging technologies for epilepsy.

The association between neurodevelopmental disability and economic outcomes during mid‐childhood

There is a paucity of evidence on the association between childhood disability and economic outcomes. This study estimates the economic costs and health utilities associated with neurodevelopmental impairment or serious neurodevelopmental disability during the 11th year of life. Standardized tests of cognitive ability and clinical assessments of functional abilities were performed on participants of a whole population study of extremely preterm children and term-born controls (EPICure). Profiles of children's use of health, social and education services, and descriptions of their health status using the Health Utilities Index Mark 2 and Mark 3 health status classification systems, were extracted from detailed questionnaires completed by the main parent. Descriptive and multiple regression techniques were used to explore the association between neurodevelopmental impairment or serious neurodevelopmental disability and two sets of economic outcomes; economic costs and health utilities. Mean health and social service costs over the 12-month period were estimated at £577 [standard error (SE): £141] among children without neurodevelopmental impairment, £831 (£112) among children with a mild neurodevelopmental impairment, £1053 (£176) among children with a moderate neurodevelopmental impairment and £1662 (£316) among children with a severe neurodevelopmental impairment. Mean health and social service costs were estimated at £1223 (£157) and £695 (£92) among children with and without serious neurodevelopmental disability, respectively. After controlling for clinical and sociodemographic confounders, mild, moderate and severe neurodevelopment impairment increased health and social service costs by an average of £397 (95% CI: £86, £707; P= 0.012), £655 (95% CI: £167, £1144; P= 0.009) and £1065 (95% CI: -£113, £2243; P= 0.076), respectively. Cost differences were accentuated when broader public sector costs were considered. The mean (SE) Health Utilities Index Mark 3 multi-attribute utility score for the children with neurodevelopmental disability was 0.652 (0.039), compared with 0.923 (0.009) for children without neurodevelopmental disability, generating a mean difference in utility score of 0.272 (95% CI: 0.220, 0.323) that was statistically significant (P < 0.001). Our data should act as a significant new resource for economic analysts modelling the overall economic impact of childhood neurodevelopment impairment or disability or the cost-effectiveness of interventions targeted at neurodevelopmentally impaired or disabled children.

Development of the multi-attribute Adolescent Health Utility Measure (AHUM)

ObjectiveObtain utilities (preferences) for a generalizable set of health states experienced by older children and adolescents who receive therapy for chronic health conditions.MethodsA health state classification system, the Adolescent Health Utility Measure (AHUM), was developed based on generic health status measures and input from children with Hunter syndrome and their caregivers. The AHUM contains six dimensions with 4–7 severity levels: self-care, pain, mobility, strenuous activities, self-image, and health perceptions. Using the time trade off (TTO) approach, a UK population sample provided utilities for 62 of 16,800 AHUM states. A mixed effects model was used to estimate utilities for the AHUM states. The AHUM was applied to trial NCT00069641 of idursulfase for Hunter syndrome and its extension (NCT00630747).ResultsObservations (i.e., utilities) totaled 3,744 (12*312 participants), with between 43 to 60 for each health state except for the best and worst states which had 312 observations. The mean utilities for the best and worst AHUM states were 0.99 and 0.41, respectively. The random effects model was statistically significant (p < 0.0001; adjusted R2 = 0.361; RMSE = 0.194). When AHUM utilities were applied to the idursulfase trial, mean utilities in the idursulfase weekly and placebo groups improved +0.087 and +0.006, respectively, from baseline to week 53. In the extension, when all patients received idursulfase, the utilities in the treatment group remained stable and the placebo group improved +0.039.DiscussionThe AHUM health state classification system may be used in future research to enable calculation of quality-adjust life expectancy for applicable health conditions.

The development of a model for translation of the Neck Disability Index to utility scores for cost-utility analysis in cervical disorders

The Neck Disability Index (NDI) is a commonly used disease-specific instrument for cervical spine disorders with good responsiveness and psychometric properties compared with general health status measures. However, NDI scores are unitless and do not have an intrinsic value that is comparable to other health status measures, and these scores have limited value in cost-utility analysis. The translation of disease-specific measures to Short Form-6 Dimensions (SF-6D) utility scores may be useful in cost-utility analysis. The purpose of this study is to present a model for translating the NDI to SF-6D utility scores, permitting the use of NDI scores in the cost-utility analysis of cervical disorders. A secondary analysis of a multicenter prospective clinical trial of the Synthes ProDisc-C (Synthes, West Chester, PA, USA) was performed. Patients included were randomized to receive either a total disc arthroplasty or anterior cervical discectomy and fusion for treatment of symptomatic cervical disc disease involving one vertebral level between C3 and C7. All subjects completed NDI and 36-Item Short Form Health Survey (SF-36) self-assessments at preoperative and postoperative follow-ups of 6 weeks, 3, 6, 12, 18, and 24 months. The NDI is a validated and widely used self-reported questionnaire designed to assess patient-determined disability resulting from neck pain, including pain level and effects on activities of daily living. The SF-6D is a preference-based health state classification system derived from six health dimensions of the SF-36 self-reported questionnaire, including the domains of physical functioning, role limitation, social functioning, bodily pain, mental health, and vitality. The collected data points were divided into two cohorts: one for model formation and one for the assessment of model validity. SF-36 scores were converted to SF-6D utilities via three previously published methods. Correlation analyses and linear regression modeling between SF-6D and NDI created the models for translating scores. For validation, Spearman and Pearson correlations were calculated between the observed and predicted SF-6D utilities, and prediction errors were calculated. Four hundred thirty patients with 2,137 time points were used for creation and validation of the model. Pearson and Spearman correlation coefficients between the NDI and the SF-6D derived from each conversion method were found to be between -0.8255 and -0.8504 (p<.01). R(2) values ranged from 0.68 to 0.71 and root mean squared error (RMSE) from 0.092 to 0.084. Correlations between estimated and observed SF-6D scores ranged from 0.8325 to 0.8372 (p<.01). The mean prediction error was less than 0.006, with standard deviation (SD) between 0.082 and 0.093. Correlations between NDI and SF-6D utility scores are strong and statistically significant. The model has a large R(2) and small RMSE. The prediction models produce a small mean prediction error, but the SD of the prediction errors is large. High correlations between NDI and SF-6D permit these models to be used to calculate overall utilities, changes in utilities, and quality-adjusted life-years for large data samples. However, the relatively large observed prediction error SDs may limit the accuracy of translation of individual data points or small sample sizes.

Improving the Measurement of QALYs in Dementia: Developing Patient- and Carer-Reported Health State Classification Systems Using Rasch Analysis

ObjectivesCost-utility analysis is increasingly used to inform resource allocation. This requires a means of valuing health states before and after intervention. Although generic measures are typically used to generate values, these do not perform well with people with dementia. We report the development of a health state classification system amenable to valuation for use in studies of dementia, derived from the DEMQOL system, measure of health-related quality of life in dementia by patient self-report (DEMQOL) and carer proxy-report (DEMQOL-Proxy). MethodsFactor analysis was used to determine the dimensional structure of DEMQOL and DEMQOL-Proxy. Rasch analysis was subsequently used to investigate item performance across factors in terms of item-level ordering, functioning across subgroups, model fit, and severity-range coverage. This enabled the selection of one item from each factor for the classification system. A sample of people with a diagnosis of mild/moderate dementia (n = 644) and a sample of carers of those with mild/moderate dementia (n = 683) were used. ResultsFactor analysis found different five-factor solutions for DEMQOL and DEMQOL-Proxy. Following item reduction and selection by using Rasch analysis, a five-dimension classification for DEMQOL and a four-dimension classification for DEMQOL-Proxy were developed. Each item contained four health state levels. ConclusionCombining Rasch and classical psychometric analysis is a valid method of selecting items for dementia health state classifications from both the patient and carer perspectives. The next stage is to obtain preference weights so that the measure can be used in the economic evaluation of treatment, care, and support arrangements for dementia.

Rasch analysis in the development of a simplified version of the national eye institute visual-function questionnaire-25 for utility estimation

Preference-based health measures value how people feel about the desirability of a health state. Generic measures may not effectively capture the impact of vision loss from ocular diseases. Disease-targeted measures could address this limitation. This study developed a vision-targeted health state classification system based on the National Eye Institute Visual Function Questionnaire-25 (NEI VFQ-25). Secondary analysis of NEI VFQ-25 data from studies of patients with central (n=932)- and peripheral-vision loss (n=2,451) were used to develop a health state classification system. Classical test theory and Rasch analyses were used to identify a smaller set of NEI VFQ-25 items suitable for the central- and peripheral-vision-loss groups. Rasch analysis of the NEI VFQ-25 items using the peripheral vision-loss data indicated that 11 items fit a unidimensional model, while 14 NEI VFQ-25 items fit using the central-vision-loss data. Combining peripheral-vision-loss data and central-vision-loss data resulted in 9 items fitting a unidimensional model. Six items covering near vision, distance vision, social vision, role difficulties, vision dependency, and vision-related mental health were selected for the health-state classification. The derived health-state classification system covers relevant domains of vision-related functioning and well-being.