RESULTS ( 1 ) EQ-5D was valid and responsive for skin conditions and most cancers ; in vision , its performance varied according to aetiology ; and performance was poor for hearing impairments .
The HUI3 performed well for hearing and vision disorders .
It also performed well in cancers although evidence was limited and there was no evidence in skin conditions .
Response mapping using all the domain scores was the best performing model for the EORTC QLQ-C30 .
In an exploratory analysis , a limited dependent variable mixture model performed better than an equivalent linear model .
3 ) The exploratory valuation study found that bolt-on items for vision , hearing and tiredness had a significant impact on values of the health states , but the direction and magnitude of differences depended on the severity of the health state .
The vision bolt-on item had a statistically significant impact on EQ-5D health state values and a full valuation model was estimated .
EQ-5D performs well in studies of cancer and skin conditions .
Mapping techniques provide a solution to predict EQ-5D values where EQ-5D has not been administered .
For conditions where EQ-5D was found to be inappropriate , including some vision disorders and for hearing , bolt-ons provide a promising solution .
BACKGROUND The National Institute for Health and Care Excellence recommends the use of generic preference-based measures ( GPBMs ) of health for its Health Technology Assessment s ( HTAs ) .
However , these data may not be available or appropriate for all health conditions .
OBJECTIVES To determine whether GPBMs are appropriate for some key conditions and to explore alternative methods of utility estimation when data from GPBMs are unavailable or inappropriate .
( 2 ) Potential modelling approaches to ' map ' EQ-5D values from condition-specific and clinical measures of health [ European Organisation for Research and Treatment of Cancer Quality -of-life Question naire Core 30 ( EORTC QLQ-C30 ) and Functional Assessment of Cancer Therapy - General Scale ( FACT-G ) ] are compared for predictive ability and goodness of fit using two separate data sets . (
Lower visual acuity predicts worse utility values among patients with type 2 diabetes
Objective We estimated the quality of life impact of vision loss in a community-based population with diabetes . Design and methods We r and omly surveyed 4,000 members of a large health maintenance organization with type 2 diabetes to assess  quality of life using the EQ-5D instrument .  Visual acuity was obtained by automated text processing of clinical notes recorded during the two years preceding subjects ’ surveys . Natural language processing was used to collect data from electronic medical records and to read clinical notes to determine the stage of retinopathy . Linear regression was used to model quality of life scores . Results Of the 4,000 surveys sent , approximately 55 % of patients responded . Patients with ≥20/20 acuity reported the highest mean utility ( mean = 0.82 ) , which declined as the visual angle doubled to 20/40 ( mean = 0.75 ) , and then doubled again to ≤20/80 ( mean = 0.71 ) . Perfect utility ( 1.0 ) was reported by 28 % of the sample . Only 7 % of patients suffered visual impairment ( ≤20/40 ) , and lower levels of visual acuity rarely occurred . Compared with patients with ≥20/20 acuity , the first doubling of the visual angle ( 20/40 ) lowered utility by three points ( 95 % confidence interval [ CI ] , −0.05 to −0.01 ) , and the second doubling of the visual angle ( ≤20/80 ) lowered utility by six points ( 95%CI , −0.10 to −0.02 ) . Conclusions This study aim ed to estimate disutility associated with visual loss in patients with diabetes using a community-based sample and controlled for many characteristics associated with quality of life . We found a smaller utility decrement compared to other studies , suggesting that visual acuity ’s impact on the quality of life for patients with diabetes in the community setting differs from more selected population
RESULTS ( 1 ) EQ-5D was valid and responsive for skin conditions and most cancers ; in vision , its performance varied according to aetiology ; and performance was poor for hearing impairments .
The HUI3 performed well for hearing and vision disorders .
It also performed well in cancers although evidence was limited and there was no evidence in skin conditions .
Response mapping using all the domain scores was the best performing model for the EORTC QLQ-C30 .
In an exploratory analysis , a limited dependent variable mixture model performed better than an equivalent linear model .
3 ) The exploratory valuation study found that bolt-on items for vision , hearing and tiredness had a significant impact on values of the health states , but the direction and magnitude of differences depended on the severity of the health state .
The vision bolt-on item had a statistically significant impact on EQ-5D health state values and a full valuation model was estimated .
EQ-5D performs well in studies of cancer and skin conditions .
Mapping techniques provide a solution to predict EQ-5D values where EQ-5D has not been administered .
For conditions where EQ-5D was found to be inappropriate , including some vision disorders and for hearing , bolt-ons provide a promising solution .
BACKGROUND The National Institute for Health and Care Excellence recommends the use of generic preference-based measures ( GPBMs ) of health for its Health Technology Assessment s ( HTAs ) .
However , these data may not be available or appropriate for all health conditions .
OBJECTIVES To determine whether GPBMs are appropriate for some key conditions and to explore alternative methods of utility estimation when data from GPBMs are unavailable or inappropriate .
( 2 ) Potential modelling approaches to ' map ' EQ-5D values from condition-specific and clinical measures of health [ European Organisation for Research and Treatment of Cancer Quality -of-life Question naire Core 30 ( EORTC QLQ-C30 ) and Functional Assessment of Cancer Therapy - General Scale ( FACT-G ) ] are compared for predictive ability and goodness of fit using two separate data sets . (