Utilities Should Not Be Multiplied: Evidence From the Preference-Based Scores in the United States

Adjusting Health State Utility Values for Multiple Conditions: Real-World EQ-5D-3L Data Modeling in Brazil

BACKGROUND AND OBJECTIVE

Decision analytical models are typically included in health economic evaluations to represent clinical pathways and enable the estimation of clinical and economic outcomes of health technologies. Clinical effects are frequently measured in terms of health-related quality of life and expressed as utility values. It is not rare that a health state in an analytical model simultaneously comprises more than one health condition. In this situation, the utility of each coexisting health condition could be combined using the additive, multiplicative, minimum, or adjusted decrement estimator (ADE) methods. However, there is no consensus about the best approach. This study aimed to compare different methods to estimate utility values for health states in which patients carry more than one health condition using data from the Brazilian population.

METHODS

Data were obtained from a multicentric cross-sectional evaluation study conducted in Brazil. Individuals completed the EQ-5D-3L questionnaire, a generic preference-based instrument that is used to obtain utility values, and were requested to disclose if they had any health conditions. Utilities were obtained according to the Brazilian value set. Four methods for adjusting joint utilities were tested: additive, multiplicative, minimum, and ADE. Observed and estimated utility values were compared for accuracy and bias.

RESULTS

A total of 5774 individuals were included in the analysis. The utility score (mean ± SE) was 0.8235 ± 0.1717. Lower utility scores were associated with an increased number of comorbidities, reaching 0.467 ± 0.192 for individuals with seven conditions. The minimum method produced accurate utility estimates for individuals with two simultaneous health conditions. For health states with more than two conditions, the multiplicative method presented more accurate estimates. Overall, fixing the baseline utility equal to the mean utility of healthy individuals produced less biased estimates compared with a baseline utility equal to 1.

CONCLUSION

Depending on the utility data available and the number of concomitant conditions, different adjustment methods could be used that produce accurate estimates. For the adjustment of Brazilian utility values for health states with comorbidities, the minimum and multiplicative methods should be preferred if two or more than two conditions are present, respectively.

Quality of life in patients with HBV infection: A systematic review and meta-analysis

Background & Aims

Despite nearly 250 million people worldwide estimated to have chronic HBV infection, health-related quality of life (HRQOL) in HBV-related disease has not been well characterised. Here, we summarise existing data on HBV-related HRQOL and quantify summary utility values by stage of disease.

Methods

Embase, Global Health, PubMed, and Web of Science were searched for articles investigating HBV HRQOL. Meta-analyses for utility scores were pooled by stage of disease and utility instrument; meta-regression was further adjusted for the effect of current health expenditure as a percentage of gross domestic product (CHE/GDP), as a proxy of the importance of healthcare perceived by different countries.

Results

Twenty-two articles from 19 studies, comprising 10,311 patients, were included. Of these studies, 74% were performed in the Western Pacific Region, and 47% used the EuroQoL-5D-3L instrument. HRQOL was found to decrease with advancing stages of HBV-related disease. Meta-regression showed the following predicted mean utility scores for the different stages of chronic HBV infection: non-cirrhotic, 0.842; compensated cirrhosis, 0.820 (p = 0.474 compared with non-cirrhotic); decompensated cirrhosis, 0.722 (p = 0.001); and hepatocellular carcinoma, 0.749 (p = 0.008). The type of tool affected HRQOL and populations with a higher CHE/GDP were associated with higher predicted utility values.

Conclusions

Chronic HBV infection impairs the HRQOL of patients, even when there is no evidence of cirrhosis. HRQOL is particularly impaired in the advanced stages of decompensated cirrhosis and hepatocellular carcinoma. These results have important implications for global hepatitis elimination efforts and are useful for economic analyses. However, further research is needed, particularly in high-burden, low-income settings where data are lacking.

Impact and implications

This study, based on 22 articles and 10,311 patients, provides a comprehensive synthesis of data on the impact of chronic hepatitis B virus (HBV) infection on patients' health-related quality of life (HRQOL) worldwide. These findings, of how HRQOL is affected in people living with HBV, highlight the importance of patient-centred care and holistic approaches to management, even at the early stages of disease. These results are useful for cost-effectiveness analyses and may help inform decision-making in improving public health policy towards the elimination of viral hepatitis. The study also underscores the need for further data from low-to middle-income settings, and on the effects of treatment on HRQOL.

A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights

BACKGROUND

Understanding comorbidity and its burden characteristics is essential for policymakers and healthcare providers to allocate resources accordingly. However, several definitions of comorbidity burden can be found in the literature. The main reason for these differences lies in the available information about the analyzed diseases (i.e., the target population studied), how to define the burden of diseases, and how to aggregate the occurrence of the detected health conditions.

METHODS

In this manuscript, we focus on data from the Italian surveillance system PASSI, proposing an index of comorbidity burden based on the disability weights from the Global Burden of Disease (GBD) project. We then analyzed the co-presence of ten non-communicable diseases, weighting their burden thanks to the GBD disability weights extracted by a multi-step procedure. The first step selects a set of GBD weights for each disease detected in PASSI using text mining. The second step utilizes an additional variable from PASSI (i.e., the perceived health variable) to associate a single disability weight for each disease detected in PASSI. Finally, the disability weights are combined to form the comorbidity burden index using three approaches common in the literature.

RESULTS

The comorbidity index (i.e., combined disability weights) proposed allows an exploration of the magnitude of the comorbidity burden in several Italian sub-populations characterized by different socioeconomic characteristics. Thanks to that, we noted that the level of comorbidity burden is greater in the sub-population characterized by low educational qualifications and economic difficulties than in the rich sub-population characterized by a high level of education. In addition, we found no substantial differences in terms of predictive values of comorbidity burden adopting different approaches in combining the disability weights (i.e., additive, maximum, and multiplicative approaches), making the Italian comorbidity index proposed quite robust and general.

Catalogues of EQ-5D-3L Health-Related Quality of Life Scores for 199 Chronic Conditions and Health Risks for Use in the UK and the USA

BACKGROUND

Health-related quality of life (HRQoL) measures are essential in economic evaluation, but sometimes primary sources are unavailable, and information from secondary sources is required. Existing HRQoL UK/US catalogues are based on earlier diagnosis classification systems, amongst other issues. A recently published Danish catalogue merged EQ-5D-3L data from national health surveys with national registers containing patient information on ICD-10 diagnoses, healthcare activities and socio-demographics.

AIMS

To provide (1) UK/US EQ-5D-3L-based HRQoL utility population catalogues for 199 chronic conditions on the basis of ICD-10 codes and health risks and (2) regression models controlling for age, sex, comorbidities and health risks to enable predictions in other populations.

METHODS

UK and US EQ-5D-3L value sets were applied to the EQ-5D-3L responses of the Danish dataset and modelled using adjusted limited dependent variable mixture models (ALDVMMs).

RESULTS

Unadjusted mean utilities, percentiles and adjusted disutilities based on two ALDVMMs with different control variables were provided for both countries. Diseases from groups M, G, and F consistently had the smallest utilities and the largest negative disutilities: fibromyalgia (M797), sclerosis (G35), rheumatism (M790), dorsalgia (M54), cerebral palsy (G80-G83), post-traumatic stress disorder (F431), dementia (F00-2), and depression (F32, etc.). Risk factors, including stress, loneliness, and BMI30+, were also associated with lower HRQoL.

CONCLUSIONS

This study provides comprehensive catalogues of UK/US EQ-5D-3L HRQoL utilities. Results are relevant in cost-effectiveness analysis, for NICE submissions, and for comparing and identifying facets of disease burden.

Estimating Joint Health State Utility Algorithms Under Partial Information

OBJECTIVES

We explored the performance of existing joint health state utility estimators when data are not available on utilities that isolate single-condition health states excluding any co-occurring condition.

METHODS

Using data from the National Epidemiologic Survey on Alcohol and Related Conditions-III, we defined 2 information sets: (1) a full-information set that includes the narrowly defined health state utilities used in most studies that test the performance of joint health state utility estimators, and (2) a limited information set that includes only the more broadly defined health state utilities more commonly available to researchers. We used an example of alcohol use disorder co-occurring with cirrhosis of the liver, depressive disorder, or nicotine use disorder to illustrate our analysis.

RESULTS

We found that the performance of joint health state utility estimators is appreciably different under limited information than under full information. Full-information estimators typically overestimate the joint state utility, whereas limited-information estimators underestimate the joint state utility, except for the minimum estimator, which is overestimated in all cases.

CONCLUSIONS

Researchers using joint health state utility estimators should understand the information set available to them and use methodological guidance appropriate for that information set. We recommend the minimum estimator under limited information based on its ease of use, consistency (and therefore a predictable direction of bias), and lower root mean squared error.

Catalog of EQ-5D-3L Health-Related Quality-of-Life Scores for 199 Chronic Conditions and Health Risks in Denmark

Background. Assessments of health-related quality of life (HRQoL) are essential in estimating quality-adjusted life-years. It is sometimes not feasible to collect primary HRQoL data, and reliable secondary sources are necessary. Current "off-the-shelf" HRQoL catalogs are based on older diagnosis classifications and include a limited number of diseases. This article aims to provide 1) a Danish EQ-5D-3L-based HRQoL catalog for 199 nationally representative chronic conditions based on ICD-10 codes and 2) a complementary model-based catalog controlling for age, sex, comorbidities, lifestyle, and health risks. Design. A total of 55,616 respondents from 3 national health survey samples were pooled and combined with 7 national registers containing patient-level information on diagnoses, health care activity, and sociodemographics. EQ-5D-3L data were converted to utility scores using the Danish EQ-5D-3L value set to estimate the mean utility for each chronic disease population. Adjusted limited dependent variable mixture models were estimated and used to provide a regression-based catalog of utilities/disutilities. Results. Diseases with the lowest mean EQ-5D score in the Danish population were systemic sclerosis (M34; score = 0.432), fibromyalgia (M797; score = 0.490), rheumatism (M790; score = 0.515), dementia (F00, G30; score = 0.546), posttraumatic stress syndrome (F431; score = 0.557), and systemic atrophies (G10-G14; score = 0.583. Based on the estimated models, the largest estimated disutilities were cystic fibrosis, cerebral palsy, depression, dorsalgia, sclerosis, and fibromyalgia. Lifestyle factors, including perceived stress, loneliness, and body mass index, were also significantly associated with low HRQoL. Conclusions. This study provides a comprehensive nationally representative catalog and a model-based catalog of EQ-5D-3L-based HRQoL scores for Denmark that can be used to describe aspects of disease burden and allocate resources within health care. Additional Stata programs are also provided to facilitate predictions in other populations.

Highlights

A Danish national representative catalog of health-related quality-of-life scores for 199 chronic conditions is presented, which provides population estimates for chronic conditions subgroups that can be used for health economic evaluation.Two separate regression models of EQ-5D-3L utility scores with different sets of control variables are estimated to allow researchers to adjust for differences in the composition of the subgroups and provide a tool that can be used in other settings.Results indicate that health-related quality of life varies across disease groups but is lowest for renal disease, mental and behavioral disorders, benign neoplasms and diseases of the blood, digestive systems, and nervous systems.Health risks and lifestyle factors such as perceived stress, loneliness, and a large body mass index are highly correlated with health-related quality of life, and, in many cases, the correlation is higher than with individual diseases.

Catalog of EQ-5D-3L Health-Related Quality-of-Life Scores for 199 Chronic Conditions and Health Risks in Denmark

Background. Assessments of health-related quality of life (HRQoL) are essential in estimating quality-adjusted life-years. It is sometimes not feasible to collect primary HRQoL data, and reliable secondary sources are necessary. Current "off-the-shelf" HRQoL catalogs are based on older diagnosis classifications and include a limited number of diseases. This article aims to provide 1) a Danish EQ-5D-3L-based HRQoL catalog for 199 nationally representative chronic conditions based on ICD-10 codes and 2) a complementary model-based catalog controlling for age, sex, comorbidities, lifestyle, and health risks. Design. A total of 55,616 respondents from 3 national health survey samples were pooled and combined with 7 national registers containing patient-level information on diagnoses, health care activity, and sociodemographics. EQ-5D-3L data were converted to utility scores using the Danish EQ-5D-3L value set to estimate the mean utility for each chronic disease population. Adjusted limited dependent variable mixture models were estimated and used to provide a regression-based catalog of utilities/disutilities. Results. Diseases with the lowest mean EQ-5D score in the Danish population were systemic sclerosis (M34; score = 0.432), fibromyalgia (M797; score = 0.490), rheumatism (M790; score = 0.515), dementia (F00, G30; score = 0.546), posttraumatic stress syndrome (F431; score = 0.557), and systemic atrophies (G10-G14; score = 0.583. Based on the estimated models, the largest estimated disutilities were cystic fibrosis, cerebral palsy, depression, dorsalgia, sclerosis, and fibromyalgia. Lifestyle factors, including perceived stress, loneliness, and body mass index, were also significantly associated with low HRQoL. Conclusions. This study provides a comprehensive nationally representative catalog and a model-based catalog of EQ-5D-3L-based HRQoL scores for Denmark that can be used to describe aspects of disease burden and allocate resources within health care. Additional Stata programs are also provided to facilitate predictions in other populations.

HIGHLIGHTS

A Danish national representative catalog of health-related quality-of-life scores for 199 chronic conditions is presented, which provides population estimates for chronic conditions subgroups that can be used for health economic evaluation.Two separate regression models of EQ-5D-3L utility scores with different sets of control variables are estimated to allow researchers to adjust for differences in the composition of the subgroups and provide a tool that can be used in other settings.Results indicate that health-related quality of life varies across disease groups but is lowest for renal disease, mental and behavioral disorders, benign neoplasms and diseases of the blood, digestive systems, and nervous systems.Health risks and lifestyle factors such as perceived stress, loneliness, and a large body mass index are highly correlated with health-related quality of life, and, in many cases, the correlation is higher than with individual diseases.

A National Catalog of Mapped Short-Form Six-Dimension Utility Scores for Chronic Conditions in the United States From 2010 to 2015

OBJECTIVES

This study examined health preference utility weights and utility decrements associated with different types of chronic conditions in the United States.

METHODS

We used the 2010-2015 Medical Expenditure Panel Survey data for persons aged ≥ 18 years with 12-Item Short-Form Survey Physical and Mental Component Summary scores. 12-Item Short-Form Survey scores were converted to Short-Form Six-Dimension (SF-6D) preference scores to measure utilities of different chronic diseases. We used the Clinical Classification Code to identify 30 chronic diseases from 12 categories, such as cardiovascular diseases, cerebrovascular diseases, hypertension, hyperlipidemia, obesity, cancers, musculoskeletal diseases, endocrine or metabolic diseases, oral diseases, respiratory diseases, and mental disorders. A generalized linear model was used to quantify the utility decrements for 30 chronic diseases, controlling for demographic characteristics.

RESULTS

We identified 132 737 adults (mean age 47.2 years, 52.2% female, 80% white); 73% had at least one identified chronic disease, and the mean SF-6D was 0.786. Among 30 chronic diseases, the unadjusted mean SF-6D scores of patients with cognitive disorder (0.607) were the lowest, followed by congestive heart failure (0.629), rheumatoid arthritis (0.654), and lung cancer (0.662). After controlling for demographic variables (ie, age, sex) and comorbidities, cognitive disorders (-0.116), mood disorders (-0.099), rheumatoid arthritis (-0.090), liver cancer (-0.078), and stroke (-0.063) showed the highest decrements in the SF-6D scores (P < .05).

CONCLUSIONS

This study provides a nationally representative catalog of utility weights for major chronic diseases in the US general population. The utility decrements will enable researchers to calculate the health utilities of patients with multiple comorbid diseases.

Cost burden and net monetary benefit loss of neonatal hypoglycaemia

BACKGROUND

Neonatal hypoglycaemia is a common but treatable metabolic disorder that affects newborn infants and which, if not identified and treated adequately, may result in neurological sequelae that persist for the lifetime of the patient. The long-term financial and quality-of-life burden of neonatal hypoglycaemia has not been previously examined.

METHODS

We assessed the postnatal hospital and long-term costs associated with neonatal hypoglycaemia over 80 year and 18 year time horizons, using a health-system perspective and assessing impact on quality of life using quality-adjusted life year (QALYs). A decision analytic model was used to represent key outcomes in the presence and absence of neonatal hypoglycaemia.

RESULTS

The chance of developing one of the outcomes of neonatal hypoglycaemia in our model (cerebral palsy, learning disabilities, seizures, vision disorders) was 24.03% in subjects who experienced neonatal hypoglycaemia and 3.56% in those who do did not. Over an 80 year time horizon a subject who experienced neonatal hypoglycaemia had a combined hospital and post-discharge cost of NZ$72,000 due to the outcomes modelled, which is NZ$66,000 greater than a subject without neonatal hypoglycaemia. The net monetary benefit lost due to neonatal hypoglycaemia, using a value per QALY of NZ$43,000, is NZ$180,000 over an 80 year time horizon.

CONCLUSIONS

Even under the most conservative of estimates, neonatal hypoglycaemia contributes a significant financial burden to the health system both during childhood and over a lifetime. The combination of direct costs and loss of quality of life due to neonatal hypoglycaemia means that this condition warrants further research to focus on prevention and effective treatment.

Cost-Effectiveness of Providing the Depression Care for People With Cancer Program to Patients With Prostate Cancer in the United States

OBJECTIVES

The Depression Care for People with Cancer program (DCPC) is a cost-effective depression care model for UK patients with cancer. However, DCPC's cost-effectiveness in the United States is unknown, particularly for patients with prostate cancer in the United States. This study evaluates the health and economic impact of providing DCPC to patients with prostate cancer.

METHODS

DCPC was compared with usual care in a mathematical model that simulates depression and its outcomes in a hypothetical cohort of US patients with prostate cancer. DCPC was modeled as a sequential combination of universal depression screening, post-screening evaluations, and first-line combination therapy. Primary outcomes were lifetime direct costs of depression care, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Secondary outcomes included life expectancy, number of depression-free months and lifetime depressive episodes, duration of depressive episodes, cumulative incidence of depression, lifetime depression diagnoses/misdiagnoses, and the cumulative incidence of maintenance therapy for depression. Sensitivity analyses were used to examine uncertainty.

RESULTS

In the base case, DCPC dominated usual care by offering 0.11 more QALYs for $2500 less per patient (from averted misdiagnoses). DCPC also offered 5 extra depression-free months, shorter depressive episodes, and a lower chance of maintenance therapy. DCPC's trade-offs were a higher cumulative incidence of depression and more lifetime depressive episodes. Life expectancy was identical under usual care and DCPC. Sensitivity analyses indicate that DCPC was almost always preferable to usual care.

CONCLUSION

Compared with usual care, DCPC may offer more value to US patients with prostate cancer. DCPC should be considered for inclusion in prostate cancer survivorship care guidelines.

Computerized Medical Evidence-Based Decision Assistance System "MEBDAS®" improves in-hospital outcome after pancreatoduodenectomy for pancreatic cancer

BACKGROUND

Indication for pancreatoduodenectomy for pancreatic cancer can be challenging. Wrong decisions in indication processes lead to significant health impairments. Computerized decision support systems can take over parts of decision-making processes, making them more accurate. MEBDAS® is a decision-supporting software that predicts outcomes of proposed treatments.

AIM

to determine the decision concordance between MEBDAS® and multidisciplinary tumour board (MTB) and the impact of MEBDAS® on in-hospital outcome at different indication thresholds.

METHODS

126 patients with pancreatoduodenectomy from a high-volume university hospital were included. Outcome indicators were in-hospital mortality, Comprehensive Complication Index (CCI®), therapy-related loss of "Quality-Adjusted-Life-Day" (QALD-loss) and prognostic gain of treatment-related "Quality-Adjusted-Life-Year" (QALY-gain).

RESULTS

The concordance of decisions was 94.4% at the indication threshold of 0. By raising the indication threshold to 1 year, the concordance decreased to 0%, the in-hospital-mortality dropped from 2.52% to 0%, the CCI® decreased from 26.47 to 13.90, the therapy-related QALD-loss declined from 21.53 to 16.22 days and the prognostic QALY-gain increased from 0.374 to 0.906 years. At IT = 0.250 years, the concordance was 61.11% and differences between MTB and MEBDAS®-group were highly significant (p < 0.001) for all outcome parameters: mortality (3.97% vs. 1.30%), CCI® (28.96 vs. 18.29), therapy-related QALD-loss (24.41 vs. 15.19 days) and QALY-gain (0.351 vs. 0.501 years).

CONCLUSION

MEBDAS® decisions are superior to those of MTB in terms of in-hospital-outcome. The inclusion of MEBDAS® in decision procedure makes the indication more accurate and reduces morbidity and mortality. In addition, MEBDAS® can increase patients' competence by involving them in decision-making process.

Burden of nonalcoholic fatty liver disease in Canada, 2019-2030: a modelling study

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) account for a growing proportion of liver disease cases, and there is a need to better understand future disease burden. We used a modelling framework to forecast the burden of disease of NAFLD and NASH for Canada.

METHODS

We used a Markov model to forecast fibrosis progression from stage F0 (no fibrosis) to stage F4 (compensated cirrhosis) and subsequent progression to decompensated cirrhosis, hepatocellular carcinoma, liver transplantation and liver-related death among Canadians with NAFLD from 2019 to 2030. We used historical trends for obesity prevalence among adults to estimate longitudinal changes in the number of incident NAFLD cases.

RESULTS

The model projected that the number of NAFLD cases would increase by 20% between 2019 and 2030, from an estimated 7 757 000 cases to 9 305 000 cases. Increases in advanced fibrosis cases were relatively greater, as the number of model-estimated prevalent stage F3 cases would increase by 65%, to 357 000, and that of prevalent stage F4 cases would increase by 95%, to 195 000. Estimated incident cases of hepatocellular carcinoma and decompensated cirrhosis would increase by up to 95%, and the number of annual NAFLD-related deaths would double, to 5600.

INTERPRETATION

Increasing rates of obesity translate into increasing NAFLD-related cases of cirrhosis and hepatocellular carcinoma and related mortality. Prevention efforts should be aimed at reducing the incidence of NAFLD and slowing fibrosis progression among those already affected.

Estimating Joint Health Condition Utility Values

OBJECTIVES

To predict health state utility values (HSUVs) for individuals with up to 4 conditions simultaneously.

METHODS

Person-level data were taken from the General Practice Patient Survey, a national survey of adult patients registered with general practices in England. Individuals reported whether they had any 1 of 16 chronic conditions and completed the 3-level EuroQol 5-dimensional questionnaire. Four nonparametric methods (additive, multiplicative, minimum, and the adjusted decrement estimator) and 1 parametric estimator (the linear index) were used to predict HSUVs for individuals with a joint health condition (JHC). Predicted and actual utility scores were compared for precision using root mean square error and mean absolute error. Bias was assessed using mean error.

RESULTS

The analysis included 929,565 individuals, of which 30.5% had at least 2 conditions. Of the nonparametric estimators, the multiplicative approach produced estimates with the lowest bias and most precision for 2 JHCs. For populations with a long-term mental health condition within the JHC, the multiplicative approach overestimated utility scores. All nonparametric methods produced biased results when estimating HSUVs for 3 or 4 JHCs. The linear index generally produced unbiased results with the highest precision.

CONCLUSIONS

The multiplicative approach was the best nonparametric estimator when estimating HSUVs for 2 JHCs. None of the nonparametric approaches for estimating HSUVs can be recommended with more than 2 JHCs. The linear index was found to have good predictive properties but needs external validation before being recommended for routine use.

Individualized Glycemic Control for U.S. Adults With Type 2 Diabetes: A Cost-Effectiveness Analysis

BACKGROUND

Intensive glycemic control in type 2 diabetes (glycated hemoglobin [HbA1c] level <7%) is an established, cost-effective standard of care. However, guidelines recommend individualizing goals on the basis of age, comorbidity, diabetes duration, and complications.

OBJECTIVE

To estimate the cost-effectiveness of individualized control versus uniform intensive control (HbA1c level <7%) for the U.S. population with type 2 diabetes.

DESIGN

Patient-level Monte Carlo-based Markov model.

DATA SOURCES

National Health and Nutrition Examination Survey 2011-2012.

TARGET POPULATION

The approximately 17.3 million persons in the United States with diabetes diagnosed at age 30 years or older.

TIME HORIZON

Lifetime.

PERSPECTIVE

Health care sector.

INTERVENTION

Individualized versus uniform intensive glycemic control.

OUTCOME MEASURES

Average lifetime costs, life-years, and quality-adjusted life-years (QALYs).

RESULTS OF BASE-CASE ANALYSIS

Individualized control saved $13 547 per patient compared with uniform intensive control ($105 307 vs. $118 854), primarily due to lower medication costs ($34 521 vs. $48 763). Individualized control decreased life expectancy (20.63 vs. 20.73 years) due to an increase in complications but produced more QALYs (16.68 vs. 16.58) due to fewer hypoglycemic events and fewer medications.

RESULTS OF SENSITIVITY ANALYSIS

Individualized control was cost-saving and generated more QALYs compared with uniform intensive control, except in analyses where the disutility associated with receiving diabetes medications was decreased by at least 60%.

LIMITATION

The model did not account for effects of early versus later intensive glycemic control.

CONCLUSION

Health policies and clinical programs that encourage an individualized approach to glycemic control for U.S. adults with type 2 diabetes reduce costs and increase quality of life compared with uniform intensive control. Additional research is needed to confirm the risks and benefits of this strategy.

PRIMARY FUNDING SOURCE

National Institute of Diabetes and Digestive and Kidney Diseases.

Estimating Health State Utility Values for Comorbidities

A comorbidity is defined as the presence of at least one additional health condition co-occurring with a primary health condition. Decision analytic models in healthcare depict the typical clinical pathway of patients in general clinical practice and frequently include health states defined to represent comorbidities such as sequelae or adverse events. Health state utility values (HSUVs) are often not available for these and analysts generally estimate them. This article provides a summary of the methodological literature on estimating methods frequently used together with worked examples. The three main methods used (minimum, multiplicative and additive) can produce a wide range in the values estimated. In general, the minimum method overestimates observed HSUVs and the magnitude of error tends to increase as the observed values decrease. Conversely, the additive and multiplicative methods generally underestimate observed values and the magnitude of the errors is generally greater for the additive method. HSUVs estimated using the multiplicative method tend to decrease for lower HSUVs and the largest errors are in observed HSUVs >0.6. Differences in estimated values can produce substantial differences in the resulting incremental cost effectiveness ratio. Based on the current evidence, the multiplicative method is advocated but additional research is required to determine appropriate methods when estimating values for additional comorbidities.

Societal preferences for adjuvant melanoma health states: UK and Australia

BACKGROUND

No studies have measured preference-based utility weights for specific toxicities and outcomes associated with approved and investigational adjuvant treatments for patients with resected high-risk melanoma.

METHODS

A cross-sectional study was conducted in the United Kingdom and Australia to obtain utilities for 14 adjuvant melanoma health states. One-on-one interviews were conducted using standard gamble; utility weights range from 0.0, dead, to 1.0, full health. Supplemental risk questions also were asked.

RESULTS

Among 155 participants (52% male; mean age, 46 years) "adjuvant treatment no toxicities" (0.89) was most preferred, followed by "induction treatment" (0.88), and "no treatment" (0.86). Participants least preferred "cancer recurrence" (0.62); the utility for "cancer recurrence and 10-year survival with treatment" was 0.70. Disutilities for grade 2 toxicities ranged from -0.06 for fatigue to -0.13 for hypophysitis. The mean maximum acceptable risk of a life-threatening event ranged from 30% for a 6% increase in the chance of remaining cancer free over 3 years to 40% for an 18% increase; Australian respondents were willing to take higher risks.

CONCLUSION

Reproducible health utilities for adjuvant melanoma health states were obtained from the general population in two countries. These utilities can be incorporated into treatment-specific cost-effectiveness evaluations.

Joint Utility Estimators in Substance Use Disorders

BACKGROUND

Although co-occurring conditions are common with substance use disorders (SUDs), estimation methods for joint health state utilities have not yet been tested in this context.

OBJECTIVES

To compare joint health state utility estimators in SUD to inform economic evaluation.

METHODS

We conducted two Internet-based surveys of US adults to collect community perspective standard gamble utilities for SUD and common co-occurring conditions. We evaluated six conditions as they occur individually and four combinations of these as they occur in tandem. We applied joint utility estimators using the six individual conditions' utilities to compare their performance relative to the observed combination states' utilities. We assessed performance with bias (estimated utility minus observed utility) and root mean square error (RMSE).

RESULTS

Using 3892 utilities from 1502 respondents, the minimum estimator was statistically unbiased (i.e., the 95% confidence interval included 0) for all combination states that we measured. The maximum estimator was unbiased for two states and the linear index and adjusted decrement estimators were unbiased for one state. The maximum estimator had the smallest RMSE for two combination states (back pain and prescription opioid misuse [0.0004] and injection crack and injection opioid use [0.0007]); the linear index and minimum estimators had the smallest RMSE for one combination state each. The additive and multiplicative estimators had the largest RMSE for all states.

CONCLUSIONS

Our results demonstrate the usefulness of the minimum estimator in this context, and confirm the inadequacy of the additive and multiplicative estimators. Further research is needed to extend these results to other SUD states.

Accounting for multimorbidity can affect the estimation of the Burden of Disease: a comparison of approaches

Background

Various Burden of Disease (BoD) studies do not account for multimorbidity in their BoD estimates. Ignoring multimorbidity can lead to inaccuracies in BoD estimations, particularly in ageing populations that include large proportions of persons with two or more health conditions. The objective of this study is to improve BoD estimates for the Netherlands by accounting for multimorbidity. For this purpose, we analyzed different methods for 1) estimating the prevalence of multimorbidity and 2) deriving Disability Weights (DWs) for multimorbidity by using existing data on single health conditions.

Methods

We included 25 health conditions from the Dutch Burden of Disease study that have a high rate of prevalence and that make a large contribution to the total number of Years Lived with a Disability (YLD). First, we analyzed four methods for estimating the prevalence of multimorbid conditions (i.e. independent, independent age- and sex-specific, dependent, and dependent sex- and age-specific). Secondly, we analyzed three methods for calculating the Combined Disability Weights (CDWs) associated with multimorbid conditions (i.e. additive, multiplicative and maximum limit). A combination of these two approaches was used to recalculate the number of YLDs, which is a component of the Disability-Adjusted Life Years (DALY).

Results

This study shows that the YLD estimates for 25 health conditions calculated using the multiplicative method for Combined Disability Weights are 5 % lower, and 14 % lower when using the maximum limit method, than when calculated using the additive method. Adjusting for sex- and age-specific dependent co-occurrence of health conditions reduces the number of YLDs by 10 % for the multiplicative method and by 26 % for the maximum limit method. The adjustment is higher for health conditions with a higher prevalence in old age, like heart failure (up to 43 %) and coronary heart diseases (up to 33 %). Health conditions with a high prevalence in middle age, such as anxiety disorders, have a moderate adjustment (up to 13 %).

Conclusions

We conclude that BoD calculations that do not account for multimorbidity can result in an overestimation of the actual BoD. This may affect public health policy strategies that focus on single health conditions if the underlying cost-effectiveness analysis overestimates the intended effects. The methodology used in this study could be further refined to provide greater insight into co-occurrence and the possible consequences of multimorbid conditions in terms of disability for particular combinations of health conditions.

Measuring benefits of opioid misuse treatment for economic evaluation: health-related quality of life of opioid-dependent individuals and their spouses as assessed by a sample of the US population

AIMS

To understand how the general public views the quality of life effects of opioid misuse and opioid use disorder on an individual and his/her spouse, measured in terms used in economic evaluations.

DESIGN

Cross-sectional internet survey of a US population-representative respondent panel conducted December 2013-January 2014.

SETTING

United States.

PARTICIPANTS

A total of 2054 randomly selected adults; 51.1% male (before weighting).

MEASUREMENTS

Mean (95% confidence interval) and median health 'utility' for six opioid misuse and treatment outcomes: active injection misuse; active prescription misuse; methadone maintenance therapy at initiation and when stabilized in treatment; and buprenorphine therapy at initiation and when stabilized. Utility is a numerical representation of health-related quality of life used in economic evaluations to 'adjust' estimated survival to include peoples' preferences for health states. Utilities are determined by surveying the general population to estimate the value they assign to particular health states on a scale where 0 = the value of being dead and 1.0 = the value of being in perfect health. Spouse spillover utility is assigned to a spouse of an individual who is in a particular health state.

FINDINGS

Mean individual utility ranged from 0.574 [95% confidence interval (CI) = 0.538, 0.611] for active injection opioid misuse to 0.766 for stabilized buprenorphine therapy (95% CI = 0.738, 0.795), with other states in between. Female respondents assigned higher utility to the active prescription misuse and buprenorphine therapy at initiation states than did males (P < 0.05); all other states did not differ by respondent gender. Mean spousal utilities were significantly lower than 1.0 but mostly higher than individual utility, and were similar between male and female respondents.

CONCLUSIONS

In the opinion of the US public, injection opioid misuse results in worse health-related quality of life than prescription misuse, and methadone therapy results in worse health-related quality of life than buprenorphine therapy. Spouses are negatively affected by their partner's opioid misuse and early treatment.

Examining the relationship between health-related quality of life and increasing numbers of diagnoses

Purpose

Little is known about estimating utilities for comorbid (or ‘joint’) health states. Several joint health state prediction models have been suggested (for example, additive, multiplicative, best-of-pair, worst-of-pair, etc.), but no general consensus has been reached. The purpose of the study is to explore the relationship between health-related quality of life (HRQoL) and increasing numbers of diagnoses.

Methods

We analyzed a large dataset containing respondents’ ICD-9 diagnoses and preference-based HRQoL (EQ-5D and SF-6D). Data were stratified by the number of diagnoses, and mean HRQoL values were estimated. Several adjustments, accounting for the respondents’ age, sex, and the severity of the diagnoses, were carried out. Our analysis fitted additive and multiplicative models to the data and assessed model fit using multiple standard model selection methods.

Results

A total of 39,817 respondents were included in the analyses. Average HRQoL values were represented well by both linear and multiplicative models. Although results across all analyses were similar, adjusting for severity of diagnoses, age, and sex strengthened the linear model’s performance measures relative to the multiplicative model. Adjusted R² values were above 0.99 for all analyses (i.e., all adjusted analyses, for both HRQoL instruments), indicating a robust result.

Conclusions

Additive and multiplicative models perform equally well within our analyses. A practical implication of our findings, based on the presumption that a linear model is simpler than an additive model, is that an additive model should be preferred unless there is compelling evidence to the contrary.

Sourcing quality-of-life weights obtained from previous studies: theory and reality in Korea

BACKGROUND

The quality-of-life weights obtained in previous studies are frequently used in cost-utility analyses. The purpose of this study is to describe how the values obtained in previous studies are incorporated into the industry submissions requesting listing at the Korean National Health Insurance (NHI), focusing on the issues discussed in theoretical studies and national guidelines.

METHODS

The industry submissions requesting listing at the Korean NHI from January 2007 until December 2009 were evaluated by two independent researchers at the Health Insurance Review and Assessment Service (HIRA). Specifically, we observed the methods that were used to pool, predict joint health state utilities, and retain consistency within submissions in terms of the issues discussed in methodological research papers and recommendations from national guidelines.

RESULTS

More than half of the submissions used QALY as an outcome measure, and most of these submissions were sourced from prior studies. Heterogeneous methodologies were frequently used within a submission, with the inconsistent use of upper and lower anchors being prevalent. Assumptions behind measuring joint health state utilities or pooling multiple values for single health states were omitted in all submissions. Most national guidelines were rather vague regarding how to predict joint health states, how to select the best available value, how to maintain consistency within a submission, and how to generalize values obtained from prior studies.

CONCLUSIONS

Previously-generated values were commonly sourced, but this practice was frequently related to inconsistencies within and among submissions. Attention should be paid to the consistency and transparency of the value, especially if the value is sourced from prior studies.

A systematic review of utility values for chemotherapy-related adverse events

BACKGROUND

Chemotherapy offers cancer patients the potential benefits of improved mortality and morbidity but may cause detrimental outcomes due to adverse drug events (ADEs), some of which requiring time-consuming, resource-intensive and costly clinical management. To appropriately assess chemotherapy agents in an economic evaluation, ADE-related parameters such as the incidence, (dis)utility and cost of ADEs should be reflected within the model parameters. To date, there has been no systematic summary of the existing literature that quantifies the utilities of ADEs due to healthcare interventions in general and chemotherapy treatments in particular.

OBJECTIVE

This review aimed to summarize the current evidence base of reported utility values for chemotherapy-related ADEs.

METHODS

A structured electronic search combining terms for utility, utility valuation methods and generic terms for cancer treatment was conducted in MEDLINE and EMBASE in June 2011. Inclusion criteria were: (1) elicitation of utility values for chemotherapy-related ADEs and (2) primary data. Two reviewers identified studies and extracted data independently. Any disagreements were resolved by a third reviewer.

RESULTS

Eighteen studies met the inclusion criteria from the 853 abstracts initially identified, collectively reporting 218 utility values for chemotherapy-related ADEs. All 18 studies used short descriptions (vignettes) to obtain the utility values, with nine studies presenting the vignettes used in the valuation exercises. Of the 218 utility values, 178 were elicited using standard gamble (SG) or time trade-off (TTO) approaches, while 40 were elicited using visual analogue scales (VAS). There were 169 utility values of specific chemotherapy-related ADEs (with the top ten being anaemia [34 values], nausea and/or vomiting [32 values], neuropathy [21 values], neutropenia [12 values], diarrhoea [12 values], stomatitis [10 values], fatigue [8 values], alopecia [7 values], hand-foot syndrome [5 values] and skin reaction [5 values]) and 49 of non-specific chemotherapy-related adverse events. In most cases, it was difficult to directly compare the utility values as various definitions and study-specific vignettes were used for the ADEs of interest.

LIMITATIONS

This review was designed to provide an overall description of existing literature reporting utility values for chemotherapy-related ADEs. The findings were not exhaustive and were limited to publications that could be identified using the search strategy employed and those reported in the English language.

CONCLUSIONS

This review identified wide ranges in the utility values reported for broad categories of specific chemotherapy-related ADEs. There were difficulties in comparing the values directly as various study-specific definitions were used for these ADEs and most studies did not make the vignettes used in the valuation exercises available. It is recommended that a basic minimum requirement be developed for the transparent reporting of study designs eliciting utility values, incorporating key criteria such as reporting how the vignettes were developed and presenting the vignettes used in the valuation tasks as well as valuing and reporting the utility values of the ADE-free base states. It is also recommended, in the future, for studies valuing the utilities of chemotherapy-related ADEs to define the ADEs according to the National Cancer Institute (NCI) definitions for chemotherapy-related ADEs as the use of the same definition across studies would ease the comparison and selection of utility values and make the overall inclusion of adverse events within economic models of chemotherapy agents much more straightforward.

Evidence of spillover of illness among household members: EQ-5D scores from a US sample

BACKGROUND/OBJECTIVES

The effects of illness extend beyond the individual to caregivers and family members. This study identified evidence of spillover of illness onto household members' health-related quality of life.

METHODS

Medical Expenditures Panel Survey (MEPS) data from 2000-2003 were analyzed using multivariable regression to identify spillover of household members' chronic conditions onto individuals' health-related quality of life as measured by the EuroQol-5D (EQ-5D) score (N = 24,188). Spillover was assessed by disease category, timing of occurrence (preexisting or new conditions), and age of the household member (adult or child).

RESULTS

Controlling for an individual's own health conditions and other known predictors of EQ-5D scores, the authors found that the odds of an individual reporting full health (an EQ-5D score of 1.0, relative to <1.0) were lower with the presence of existing mental (odds ratio 0.71; 95% confidence interval, 0.64-0.79), respiratory (0.85; 0.75-0.97), and musculoskeletal (0.83; 0.75-0.93) conditions among adults and with mental (0.72; 0.62-0.82) and respiratory (0.80; 0.81-0.96) conditions among children in the household. The odds of an individual reporting full health were also lower for newly occurring chronic conditions in the household, including adults' mental (0.79; 0.65-0.97), nervous/sensory system (0.76; 0.61-0.96), and musculoskeletal (0.78; 0.65-0.95) conditions and children's mental conditions (0.64; 0.48-0.86). EQ-5D dimensions may be unsuited to fully capture spillover utility among household members, and MEPS lacks condition severity and caregiver status among household members.

CONCLUSIONS

Evidence from a US sample suggests that individuals who live with chronically ill household members have lower EQ-5D scores than those who live either alone or with healthy household members. Averting spillover effects may confer substantial additional benefit at the population level for interventions that prevent or alleviate conditions that incur such effects.

Using health state utility values in models exploring the cost-effectiveness of health technologies

BACKGROUND

To improve comparability of economic data used in decision making, some agencies recommend that a particular instrument should be used to measure health state utility values (HSUVs) used in decision-analytic models. The methods used to incorporate HSUVs in models, however, are often methodologically poor and lack consistency. Inconsistencies in the methodologies used will produce discrepancies in results, undermining policy decisions informed by cost per quality-adjusted life-years.

OBJECTIVE

To provide an overview of the current evidence base relating to populating decision-analytic models with HSUVs.

FINDINGS

Research exploring suitable methods to accurately reflect the baseline or counterfactual HSUVs in decision-analytic models is limited, and while one study suggested that general population data may be appropriate, guidance in this area is poor. Literature describing the appropriateness of different methods used to estimate HSUVs for combined conditions is growing, but there is currently no consensus on the most appropriate methodology. While exploratory analyses suggest that a statistical regression model might improve accuracy in predicted values, the models require validation and testing in external data sets. Until additional research has been conducted in this area, the current evidence suggests that the multiplicative method is the most appropriate technique. Uncertainty in the HSUVs used in decision-analytic models is rarely fully characterized in decision-analytic models and is generally poorly reported.

CONCLUSIONS

A substantial volume of research is required before definitive detailed evidence-based practical advice can be provided. As the methodologies used can make a substantial difference to the results generated from decision-analytic models, the differences and lack of clarity and guidance will continue to lead to inconsistencies in policy decision making.

Estimating Health State Utility Values for Joint Health Conditions

Background. Analysts frequently estimate the health state utility values (HSUVs) for joint health conditions (JHCs) using data from cohorts with single health conditions. The methods can produce very different results, and there is currently no consensus on the most appropriate technique. Objective. To conduct a detailed critical review of existing empirical literature to gain an understanding of the reasons for differences in results and identify where uncertainty remains that may be addressed by further research. Results. Of the 11 studies identified, 10 assessed the additive method, 10 the multiplicative method, 7 the minimum method, and 3 the combination model. Two studies evaluated just 1 of the techniques, whereas the others compared results generated using 2 or more. The range of actual HSUVs can influence general findings, and methods are sometimes compared using descriptive statistics that may not be appropriate for assessing predictive ability. None of the methods gave consistently accurate results across the full range of possible HSUVs, and the values assigned to normal health influence the accuracy of the methods. Conclusions. Within the limitations of the current evidence base, we would advocate the multiplicative method, conditional on adjustment for baseline utility, as the preferred technique to estimate HSUVs for JHCs when using mean values obtained from cohorts with single conditions. We would recommend that a range of sensitivity analyses be performed to explore the effect on results when using the estimated HSUVs in economic models. Although the linear models appeared to give more accurate results in the studies we reviewed, these models require validating in external data before they can be recommended.

Comparing EQ-5D scores for comorbid health conditions estimated using 5 different methods

BACKGROUND

There is currently no consensus on the most appropriate method to estimate health state utility values (HSUVs) for comorbid health conditions.

OBJECTIVE

The objective of the study was to assess the accuracy by applying 5 different methods to an EQ-5D dataset.

METHODS

EQ-5D data (n=41,174) from the Health Survey for England were used to compare HSUVs generated using the additive, multiplicative and minimum methods, the adjusted decrement estimator, and a linear regression.

RESULTS

The additive and multiplicative methods underestimated the majority of HSUVs and the magnitude of the errors increased as the actual HSUV increased. Conversely, the minimum and adjusted decrement estimator methods overestimated the majority of HSUVs and the magnitude of errors increased as the actual HSUV decreased. Although the simple linear model produced the most accurate results, there was a tendency to underpredict higher HSUVs and overpredict lower HSUVs. The magnitude and direction of mean errors could be driven by the actual scores being estimated in addition to the technique used and the HSUVs estimated using an adjusted baseline were generally more accurate.

CONCLUSIONS

The additive and minimum methods performed very poorly in our data. Although the simple linear model gave the most accurate results, the model requires validating in external data obtained from the EQ-5D and other preference-based measures. Based on the current evidence base, we would recommend the multiplicative method is used together with a range of univariate sensitivity analyses.

Perspective on the cost-effectiveness of transapical aortic valve implantation in high-risk patients: Outcomes of a decision-analytic model

BACKGROUND

The incremental cost-effectiveness of transapical transcatheter aortic valve implantation (TAVI) is ill-defined in high-risk patients where aortic valve replacement (AVR) is an option, and has not been ascertained outside a randomized controlled trial.

METHODS

We developed a Markov model to examine the progression of patients between health states, defined as peri- and post-procedural, post-complication, and death. The mean and variance of risks, transition probabilities, utilities and cost of transapical TAVI, high-risk AVR, and medical management were derived from analysis of relevant registries. Outcome and cost were derived from 10,000 simulations. Sensitivity analyses further evaluated the impact of mortality, stroke, and other commonly observed outcomes.

RESULTS

In the reference case, both transapical TAVI and high-risk AVR and TAVI were cost-effective when compared to medical management ($44,384/QALY and $42,637/QALY, respectively). Transapical TAVI failed to meet accepted criteria for incremental cost-effectiveness relative to AVR, which was the dominant strategy. In sensitivity analyses, the mortality rates related to the two strategies, the utilities post-AVR and post-transapical TAVI, and the cost of transapical TAVI, were the main drivers of model outcome.

CONCLUSION

Transapical TAVI did not satisfy current metrics of incremental cost-effectiveness relative to high-risk AVR in the reference case. However, it may provide net health benefits at acceptable cost in selected high-risk patients among whom AVR is the standard intervention.

Cost-effectiveness of sensor-augmented pump therapy in adults with type 1 diabetes in the United States

OBJECTIVES

A recent randomized trial demonstrated significant reductions in hemoglobin A(1c) levels with sensor-augmented pump therapy (SAPT) compared with multiple daily injections of insulin (MDI) in type 1 diabetes. We analyzed resource use in the trial and estimated the long-term cost-effectiveness of SAPT from the perspective of the US health care system.

METHODS

We undertook a cost-effectiveness analysis combining estimates from the trial and the literature to populate the previously validated Center for Outcomes Research (CORE) Diabetes Model. Results represent the use of 3-day sensors, as in the trial, and 6-day sensors, approved in most markets but not yet approved in the United States.

RESULTS

Within-trial hospital days, emergency department visits, and outpatient visits did not differ significantly between the treatment groups. Assuming 65% use of 3-day sensors, treatment-related costs in year 1 were an estimated $10,760 for SAPT and $5072 for MDI. Discounted lifetime estimates were $253,493 in direct medical costs and 10.794 quality-adjusted life-years (QALYs) for SAPT and $167,170 in direct medical costs and 10.418 QALYs for MDI. For 3-day and 6-day sensors, the incremental cost-effectiveness ratios were $229,675 per QALY (95% confidence interval $139,071-$720,865) and $168,104 per QALY (95% confidence interval $102,819-$523,161), respectively. The ratios ranged from $69,837 to $211,113 per QALY with different strategies for incorporating utility benefits resulting from less fear of hypoglycemia with SAPT.

CONCLUSION

Despite superior clinical benefits of SAPT compared with MDI, SAPT does not appear to be economically attractive in the United States for adults with type 1 diabetes in its current state of development. However, further clinical developments reducing disposable costs of the system could significantly improve its economic attractiveness.

Estimating health state utility values for comorbid health conditions using SF-6D data

INTRODUCTION

When health state utility values for comorbid health conditions are not available, data from cohorts with single conditions are used to estimate scores. The methods used can produce very different results and there is currently no consensus on which is the most appropriate approach.

OBJECTIVE

The objective of the current study was to compare the accuracy of five different methods within the same dataset.

METHOD

Data collected during five Welsh Health Surveys were subgrouped by health status. Mean short-form 6 dimension (SF-6D) scores for cohorts with a specific health condition were used to estimate mean SF-6D scores for cohorts with comorbid conditions using the additive, multiplicative, and minimum methods, the adjusted decrement estimator (ADE), and a linear regression model.

RESULTS

The mean SF-6D for subgroups with comorbid health conditions ranged from 0.4648 to 0.6068. The linear model produced the most accurate scores for the comorbid health conditions with 88% of values accurate to within the minimum important difference for the SF-6D. The additive and minimum methods underestimated or overestimated the actual SF-6D scores respectively. The multiplicative and ADE methods both underestimated the majority of scores. However, both methods performed better when estimating scores smaller than 0.50. Although the range in actual health state utility values (HSUVs) was relatively small, our data covered the lower end of the index and the majority of previous research has involved actual HSUVs at the upper end of possible ranges.

CONCLUSIONS

Although the linear model gave the most accurate results in our data, additional research is required to validate our findings.

Utility values in National Institute for Health and Clinical Excellence (NICE) Technology Appraisals

OBJECTIVE

To review the selection and use of health-related utility values for economic models included in National Institute for Health and Clinical Excellence (NICE) Technology Appraisals.

METHOD

A cross-sectional review of reports of economic models submitted to the Technology Appraisals program was undertaken to review the health-related utility data included. Data reviewed included identification and selection of data and the methods used for utility elicitation. The methods used were compared with those from the 2004 Methods Guide issued by NICE.

RESULTS

Appraisals conducted after the implementation of the NICE 2004 Methods Guide were reviewed. After exclusion of documents that did not include a de novo cost-utility analysis, 71 submissions (53 manufacturer submissions, 18 assessment group reports) from 39 appraisals were identified, containing 284 unique utility values. Variation was found in the selection, elicitation, valuation, and use of the utility values. Thirty-nine submissions (55%) took utility values from published studies, of which only 31% were identified through a systematic review. Forty-seven (66%) submissions contained health state descriptions reported by patients, and 55 (77%) submissions applied a valuation set derived from the general population. The EQ-5D was used in 35 (49%) submissions, and mapping to a generic health-related quality of life measure was performed in 19 (27%) submissions.

CONCLUSIONS

Only 56% of submissions to NICE and assessment reports included utility values that met the NICE 2004 reference case. This highlights variation in the methods used to select and incorporate utility values in economic models for NICE Technology Appraisals.

Cost effectiveness of atazanavir-ritonavir versus lopinavir-ritonavir in treatment-naïve human immunodeficiency virus-infected patients in the United States

OBJECTIVE

To evaluate lifetime cost effectiveness of atazanavir-ritonavir (ATV + r) versus lopinavir-ritonavir (LPV/r), both with tenofovir-emtricitabine, in US HIV-infected patients initiating first-line antiretroviral therapy.

METHODS

A Markov microsimulation model was developed to calculate quality-adjusted life-years (QALYs) based on CD4 and HIV RNA levels, coronary heart disease (CHD), AIDS, opportunistic infections (OIs), diarrhea, and hyperbilirubinemia. A million-member cohort of HIV-1-infected, treatment-naïve adults progressed at 3-month intervals through eight health states. Baseline characteristics, virologic suppression, cholesterol changes, and diarrhea and hyperbilirubinemia rates were based on 96-week CASTLE trial results. HIV mortality, OI rates, adherence, costs, utilities, and CHD risk were from literature and experts.

LIMITATIONS

The incremental cost-effectiveness ratio (ICER) may be overestimated because the ATV + r treatment effect was based on an intention-to-treat analysis. The QALY weights used for diarrhea, hyperbilirubinemia, and CHD events are uncertain; however, the ICER remained < $50,000/QALY when these values were varied in sensitivity analyses.

RESULTS

ATV + r patients received first-line therapy longer than LPV/r patients (97.3 vs. 70.7 months), had longer quality-adjusted survival (11.02 vs. 10.76 years), similar overall survival (18.52 vs. 18.51 years), and higher costs ($275,986 vs. 269,160). ATV+r [corrected] patients had lower rates of AIDS (19.08 vs. 20.05 cases/1000 patient-years), OIs (0.44 vs.0.52), diarrhea (1.27 vs. 6.26), and CHD events(5.44 vs. 5.51), but higher hyperbilirubinemia rates (6.99 vs. 0.25. ATV + r added 0.26 QALYs at a cost of $6826, for $26,421/QALY.

CONCLUSIONS

By more effectively reducing viral load with less gastrointestinal toxicity and a better lipid profile, ATV + r lowered rates of AIDS and CHD, increased quality-adjusted survival, and was cost effective (< $50,000/QALY) compared with LPV/r.

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

BACKGROUND

The methods used to estimate health-state utility values (HSUV) for multiple health conditions can produce very different values. Economic results generated using baselines of perfect health are not comparable with those generated using baselines adjusted to reflect the HSUVs associated with the health condition. Despite this, there is no guidance on the preferred techniques and little research describing the effect on cost per quality adjusted life-year (QALY) results when using the different methods.

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, no history of CVD, general population) in conjunction with models (minimum, additive, multiplicative) frequently used to approximate scores for health states with multiple health conditions. HSUVs are calculated using the EQ-5D UK preference-based algorithm.

RESULTS

Assuming a baseline of perfect health ignores the natural decline in quality of life associated with age, overestimating the benefits of treatment. The results generated using baselines from the general population are comparable to those obtained using baselines from individuals with no history of CVD. The minimum model biases results in favor of younger-aged cohorts. The additive and multiplicative models give similar results.

CONCLUSION

Although further research in additional health conditions is required to support our findings, our results highlight the need for analysts to conform to an agreed reference case. 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.

Predicting Utility for Joint Health States: A General Framework and a New Nonparametric Estimator

Measuring utility is important in clinical decision making and cost-effectiveness analysis because utilities are often used to compute quality-adjusted life expectancy, a metric used in measuring the effectiveness of health care programs and medical interventions. Predicting utility for joint health states has become an increasingly valuable research topic because of the aging of the population and the increasing prevalence of comorbidities. Although multiplicative, minimum, and additive estimators are commonly used in practice, research has shown that they are all biased. In this study, the authors propose a general framework for predicting utility for joint health states. This framework includes these 3 nonparametric estimators as special cases. A new simple nonparametric estimator, the adjusted decrement estimator, [Uij = Umin - Umin (1 - Ui )(1 - Uj )], is introduced under the proposed framework. When applied to 2 independent data sources, the new nonparametric estimator not only generated unbiased prediction of utilities for joint health states but also had the least root mean squared error and highest concordance when compared with other nonparametric and parametric estimators. Further research and validation of this new estimator are needed.

Population preference values for treatment outcomes in chronic lymphocytic leukaemia: a cross-sectional utility study

Background

Given that treatments for chronic lymphocytic leukaemia (CLL) are palliative rather than curative, evaluating the patient-perceived impacts of therapy is critical. To date, no utility (preference) studies from the general public or patient perspective have been conducted in CLL. The objective of this study was to measure preferences for health states associated with CLL treatment.

Methods

This was a cross-sectional study of 89 members of the general population in the UK (England and Scotland). Using standard gamble, each participant valued four health states describing response status, six describing treatment-related toxicities based on Common Toxicity Criteria, and two describing line of treatment. The health states incorporated standardized descriptions of treatment response (symptoms have "improved," "stabilized," or "gotten worse"), swollen glands, impact on daily activities, fatigue, appetite, and night sweats. Utility estimates ranged from 0.0, reflecting dead, to 1.0, reflecting full health.

Results

Complete response (CR) was the most preferred health state (mean utility, 0.91), followed by partial response (PR), 0.84; no change (NC), 0.78; and progressive disease (PD), 0.68. Among the toxicity states, grade I/II nausea and nausea/vomiting had the smallest utility decrements (both were -0.05), and grade III/IV pneumonia had the greatest decrement (-0.20). The utility decrements obtained for toxicity states can be subtracted from utilities for CR, PR, NC, and PD, as appropriate. The utilities for second- and third-line treatments, which are attempted when symptoms worsen, were 0.71 and 0.65, respectively. No significant differences in utilities were observed by age, sex, or knowledge/experience with leukaemia.

Conclusions

This study reports UK population utilities for a universal set of CLL health states that incorporate intended treatment response and unintended toxicities. These utilities can be applied in future cost-effectiveness analyses of CLL treatment.

Societal preference values for advanced melanoma health states in the United Kingdom and Australia

Background:

No studies measure preference-based utilities in advanced melanoma that capture both intended clinical response and unintended toxicities associated with treatment.

Methods:

Using standard gamble, utilities were elicited from 140 respondents in the United Kingdom and Australia for 13 health states.

Results:

Preferences decreased with reduced treatment responsiveness and with increasing toxicity.

Conclusions:

These general population utilities can be incorporated into treatment-specific cost-effectiveness evaluations.

Joint assessment of intended and unintended effects of medications: an example using vascular endothelial growth factor inhibitors for neovascular age-related macular degeneration

Objective. To estimate the net health benefits of pegaptanib and ranibizumab by considering the impact of visual acuity and unintended effects (cardiovascular and hemorrhagic events) on quality-of-life among persons with neovascular age-related macular degeneration. Methods. We designed a probabilistic decision-analytic model using published data. It employed 17 visual health states and three for unintended effects. We calculated incremental net health benefits by subtracting the harms of each medication from the benefit using the quality-adjusted life year (QALY). Results. In a hypothetical cohort of 1,000 75-year olds with new-onset bilateral age-related macular degeneration followed for ten years, the mean QALYs per patient is 3.7 for usual care, 4.2 for pegaptanib, and 4.3 for ranibizumab. Net benefits decline with increasing baseline rates of unintended effects. Interpretation. Net health benefits present a quantitative, potentially useful tool to assist patients and ophthalmologists in balancing the benefits and harms of interventions for age-related macular degeneration.