Populating an economic model with health state utility values: moving towards better practice

Background: When estimating health state utility values (HSUV) for multiple health conditions, the alternative models used to combine these data can produce very different values. Results generated using a baseline of perfect health are not comparable with those generated using a baseline adjusted for not having the health condition taking into account age and gender. Despite this, there is no guidance on the preferred techniques that should be used and very little research describing the effect on cost per QALY results. Methods: Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health state utility profiles (perfect health, individuals with no history of CVD, general population) in conjunction with three models (minimum, additive, multiplicative) frequently used to estimate proxy scores for multiple health conditions. Results: Assuming a baseline of perfect health ignores the natural decline in quality of life associated with co-morbidities, over-estimating the benefits of treatment to such an extent it could potentially influence a threshold policy decision. The minimum model biases results in favour of younger aged cohorts while the additive and multiplicative technique produced similar results. Although further research in additional health conditions is required to support our findings, this pilot study highlights the urgent need for analysts to conform to an agreed reference case and provides initial recommendations for better practice. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation

Populating an economic model with health state utility values: moving towards better practice

Background: When estimating health state utility values (HSUV) for multiple health conditions, the alternative models used to combine these data can produce very different values. Results generated using a baseline of perfect health are not comparable with those generated using a baseline adjusted for not having the health condition taking into account age and gender. Despite this, there is no guidance on the preferred techniques that should be used and very little research describing the effect on cost per QALY results. Methods: Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health state utility profiles (perfect health, individuals with no history of CVD, general population) in conjunction with three models (minimum, additive, multiplicative) frequently used to estimate proxy scores for multiple health conditions. Results: Assuming a baseline of perfect health ignores the natural decline in quality of life associated with co-morbidities, over-estimating the benefits of treatment to such an extent it could potentially influence a threshold policy decision. The minimum model biases results in favour of younger aged cohorts while the additive and multiplicative technique produced similar results. Although further research in additional health conditions is required to support our findings, this pilot study highlights the urgent need for analysts to conform to an agreed reference case and provides initial recommendations for better practice. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation

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.health state utility values; baseline; quality of life; EQ-5D; age-adjusted

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

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

It's all in the name, or is it? The impact of labelling on health state values

Many descriptions of health used in vignettes and condition-specific measures refer to the medical condition. This paper assesses the impact of referring to the medical condition in the descriptions of health states valued by members of the general population. A sample of 241 members of the UK general population each valued 8 health states using time trade-off. All respondents valued essentially the same health states, but for each respondent the descriptions featured either an irritable bowel syndrome label, a cancer label or no label. Regression techniques were used to estimate the impact of each label and experience of the condition on health state values. We find that the inclusion of a cancer label in health state descriptions affects health state values and that the impact is dependent upon the severity of the state. A condition label can affect health state values, but this is dependent upon the specific condition and severity. It is recommended to avoid condition labels in health state descriptions (where possible) to ensure that values are not affected by prior knowledge or preconception of the condition that may distort the health state being valued

Comparison of outcome measures for patients with chronic obstructive pulmonary disease (COPD) in an outpatient setting

BACKGROUND: To assist clinicians and researchers in choosing outcome measures for patients with chronic obstructive pulmonary disease attending routine outpatient clinics, a comparative assessment was undertaken of four questionnaires designed to reflect the patients' perception of their physical and emotional health in terms of their feasibility, validity, reliability, and responsiveness to health change. METHODS: Two condition specific questionnaires, the St George's Respiratory Questionnaire (SGRQ) and Guyatt's Chronic Respiratory Questionnaire (CRQ), and two generic questionnaires, the Short Form-36 Health Survey (SF-36) and Euroqol (EQ), were compared for their discriminative and evaluative properties. Spirometric tests and a walking test were also performed. One hundred and fifty six adults who were clinically judged to have COPD and who attended an outpatient chest clinic were assessed at recruitment and six and 12 months later. Patients were also asked whether their health had changed since their last assessment. RESULTS: Completion rates and consistency between items for dimensions of the SGRQ were lower than for dimensions of the other questionnaires. The distributions of responses were skewed for certain dimensions in all questionnaires except the CRQ. Validity was supported for all instruments insofar as patients' scores were associated with differences in disease severity. The generic questionnaires better reflected other health problems. All instruments were reliable over time. The condition specific questionnaires were more responsive between baseline and first follow up visit but this difference did not persist. While certain dimensions of the SF-36 were responsive to patient perceived changes, this did not apply to the derived single index of the EQ. The rating scale of the EQ, however, provided a quick and easy indicator of change. CONCLUSIONS: Evidence from this study supports the CRQ and the SF-36 as comprehensive outcome measures for patients with longstanding COPD