Using health state utility values from the general population to approximate baselines in decision analytic models when condition specific data are not available

Decision analytic models in healthcare require baseline health related quality of life (HRQoL) data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per QALY thresholds. The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition specific data are not available. Methods: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status. Results: Over 45% of respondents (n=41,174) reported at least one health condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one health condition. In these instances, if condition specific data are not available, data from respondents who report they do not have a prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on HRQoL may not be constant across ages for all conditions and these relationships may be condition specific. Additional research is required to validate our findings

Using the SF-36 with older adults: a cross-sectional community-based survey

OBJECTIVES: To assess the practicality and validity of using the SF-36 in a community-dwelling population over 65 years old, and obtain population scores in this age group. DESIGN: Postal survey, using a questionnaire booklet containing the SF-36 and other health related items, of all those aged 65 or over registered with twelve general practices. Non-respondents received up to two reminders at three-weekly intervals. SETTING: Twelve randomly selected general practices in Sheffield. SAMPLE: 9897 subjects aged 65 to 104. MAIN OUTCOME MEASURES: Scores for the eight dimensions of the SF-36 and a modified version of the physical functioning dimension. RESULTS: The SF-36 achieved a response rate of 82% (n=8117) and dimension completion rates of 86.4% to 97.7%. Internal consistency measured by Cronbach’s a exceeded 0.80 for all dimensions except social functioning. These results compare favourably with postal surveys of younger adults. Scores for older adults were calculated by age and sex. Comparison with data from younger people showed how physical health declines steeply with age, in marked contrast with mental health. CONCLUSIONS: The SF-36 is a practical and valid instrument to use in postal surveys of older people living in the community. The population scores provided here may facilitate its use in future surveys of older adults

A review of studies mapping (or cross walking) from non-preference based measures of health to generic preference-based measures

This paper presents a systematic review of current practice in mapping between nonpreference based measures and generic preference-based measures. It reviews the studies identified by a systematic search of the published literature and the grey literature. This review seeks to address the feasibility and overall validity of this approach, the circumstances when it should be considered and to bring together any lessons for future mapping studies

Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems

A comparison of the EQ-5D and the SF-6D across seven patient groups

As the number of preference-based instruments grows, it becomes increasingly important to compare different preference-based measures of health in order to inform an important debate on the choice of instrument. This paper presents a comparison of two of them, the EQ-5D and the SF-6D (recently developed from the SF-36) across seven patient/population groups (chronic obstructive airways disease, osteoarthritis, irritable bowel syndrome, lower back pain, leg ulcers, post menopausal women and elderly). The mean SF-6D index value was found to exceed the EQ-5D by 0.045 and the intraclass correlation coefficient between them was 0.51. Whilst this convergence lends some support for the validity of these measures, the modest difference at the aggregate level masks more significant differences in agreement across the patient groups and over severity of illness, with the SF-6D having a smaller range and lower variance in values. There is evidence for floor effects in the SF-6D and ceiling effects in the EQ-5D. These discrepancies arise from differences in their health state classifications and the methods used to value them. Further research is required to fully understand the respective roles of the descriptive systems and the valuation methods and to examine the implications for estimates of the impact of health care interventions

Estimating a preference-based index for a menopause specific health quality of life questionnaire

BACKGROUND: The aim of the study was to develop a menopause-specific, preference-based healthrelated quality-of-life (HRQoL) index reflecting both menopausal symptoms and potential sideeffects of Hormone Replacement Therapy (HRT). METHODS: The study had three phases: the development of a health state classification, a prospective 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 androgenic 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, randomly selected from 6 general practice lists in Sheffield, UK. Respondents were asked to complete a time trade-off (TTO) task for nine health states, resulting 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, androgenic 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 resultant algorithm generates a preference-based index that can be used economic evaluation and that reflects the impact of this condition

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

Cost effectiveness of a community based exercise programme in over 65 year olds: cluster randomised trial

OBJECTIVE: To assess the cost effectiveness of a community based exercise programme as a population wide public health intervention for older adults. DESIGN: Pragmatic, cluster randomised community intervention trial. Setting: 12 general practices in Sheffield; four randomly selected as intervention populations, and eight as control populations. PARTICIPANTS: All those aged 65 and over in the least active four fifths of the population responding to a baseline survey. There were 2283 eligible participants from intervention practices and 4137 from control practices. INTERVENTION: Eligible subjects were invited to free locally held exercise classes, made available for two years. MAIN OUTCOME MEASURES: All cause and exercise related cause specific mortality and hospital service use at two years, and health status assessed at baseline, one, and two years using the SF-36. A cost utility analysis was also undertaken. RESULTS: Twenty six per cent of the eligible intervention practice population attended one or more exercise sessions. There were no significant differences in mortality rates, survival times, or admissions. After adjusting for baseline characteristics, patients in intervention practices had a lower decline in health status, although this reached significance only for the energy dimension and two composite scores (p,0.05). The incremental average QALY gain of 0.011 per person in the intervention population resulted in an incremental cost per QALY ratio of J17 174 (95% CI =J8300 to J87 120). CONCLUSIONS: Despite a low level of adherence to the exercise programme, there were significant gains in health related quality of life. The programme was more cost effective than many existing medical interventions, and would be practical for primary care commissioning agencies to implement