Caring for individuals with spinal muscular atrophy (SMA), a rare genetic disorder, poses tremendous challenges for the economy and healthcare system. This study evaluated the cost-utility of onasemnogene abeparvovec-xioi gene therapy and risdiplam for SMA in Thailand.
Methods
A Markov model was used to analyze the lifetime costs and outcomes of these treatments compared with standard of care for symptomatic SMA types 1 and 2–3. SMA type 1 patients were treated with one of either onasemnogene or risdiplam, while SMA types 2–3 patients received risdiplam. Data on disease progression and medical costs were sourced from hospital databases, while treatment efficacy was based on clinical trials. Interviews with patients and caregivers provided data on non-medical costs and utilities. Base case cost-effectiveness and sensitivity analyses were conducted, with the incremental cost-effectiveness ratio (ICER) calculated in US dollars (USD) per quality-adjusted life year (QALY) gained, against a willingness-to-pay threshold of 4444 USD/QALY gained.
Results
For SMA type 1, the ICERs for onasemnogene and risdiplam were 163,102 and 158,357 USD/QALY gained, respectively. For SMA types 2–3, the ICER for risdiplam was 496,704 USD/QALY gained.
Conclusions
While onasemnogene and risdiplam exceeded the value-for-money threshold of the Thai healthcare system, they yielded the highest QALY gains among all approved medications. Policy-makers should incorporate various pieces of evidence alongside the cost-effectiveness results for rare diseases with costly drugs. Additionally, cost-effectiveness findings are useful for price negotiations and alternative financial funding, which allows policy-makers to seek solutions to ensure patient access, aligning with universal health coverage principles in Thailand.
{"title":"Onasemnogene Abeparvovec Gene Therapy and Risdiplam for the Treatment of Spinal Muscular Atrophy in Thailand: A Cost-Utility Analysis","authors":"Sarayuth Khuntha, Juthamas Prawjaeng, Kunnatee Ponragdee, Oranee Sanmaneechai, Varalak Srinonprasert, Pattara Leelahavarong","doi":"10.1007/s40258-024-00915-y","DOIUrl":"10.1007/s40258-024-00915-y","url":null,"abstract":"<div><h3>Objectives</h3><p>Caring for individuals with spinal muscular atrophy (SMA), a rare genetic disorder, poses tremendous challenges for the economy and healthcare system. This study evaluated the cost-utility of onasemnogene abeparvovec-xioi gene therapy and risdiplam for SMA in Thailand.</p><h3>Methods</h3><p>A Markov model was used to analyze the lifetime costs and outcomes of these treatments compared with standard of care for symptomatic SMA types 1 and 2–3. SMA type 1 patients were treated with one of either onasemnogene or risdiplam, while SMA types 2–3 patients received risdiplam. Data on disease progression and medical costs were sourced from hospital databases, while treatment efficacy was based on clinical trials. Interviews with patients and caregivers provided data on non-medical costs and utilities. Base case cost-effectiveness and sensitivity analyses were conducted, with the incremental cost-effectiveness ratio (ICER) calculated in US dollars (USD) per quality-adjusted life year (QALY) gained, against a willingness-to-pay threshold of 4444 USD/QALY gained.</p><h3>Results</h3><p>For SMA type 1, the ICERs for onasemnogene and risdiplam were 163,102 and 158,357 USD/QALY gained, respectively. For SMA types 2–3, the ICER for risdiplam was 496,704 USD/QALY gained.</p><h3>Conclusions</h3><p>While onasemnogene and risdiplam exceeded the value-for-money threshold of the Thai healthcare system, they yielded the highest QALY gains among all approved medications. Policy-makers should incorporate various pieces of evidence alongside the cost-effectiveness results for rare diseases with costly drugs. Additionally, cost-effectiveness findings are useful for price negotiations and alternative financial funding, which allows policy-makers to seek solutions to ensure patient access, aligning with universal health coverage principles in Thailand.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 2","pages":"277 - 290"},"PeriodicalIF":3.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40258-024-00915-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142339797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1007/s40258-024-00916-x
Jacob Koris, Elizabeth Ojelade, Hasina Begum, Maria Van-Hove, Tim W. R. Briggs, William K. Gray
Background
The National Health Service (NHS) in England has set a target to be net zero for carbon emissions by 2045. The aim of this study was to investigate how changes in key aspects of clinical practice over the last 8 years have contributed towards reducing the per-patient carbon footprint of elective total hip arthroplasty (THA).
Methods
This was a retrospective analysis of administrative data. Data were extracted from the Hospital Episode Statistics database for all adult (≥ 17 years), primary, elective THA procedures conducted in England from 1 April, 2014 to 31 March, 2022. The estimated carbon footprint for key elements of the surgical pathway were calculated based on data from Greener NHS and the Sustainable Healthcare Coalition.
Results
Data were available for 537,441 THA procedures conducted during the study period. The per-patient carbon footprint associated with the primary THA (index) procedure fell by around 25% from 2014/15 to 2021/22. Length of stay was by far the largest contributor to this decline, falling from 169.1 kgCO2e to 117.6 kgCO2e per patient from 2014/15 to 2021/22. Absolute declines in the carbon footprint associated with emergency readmissions, revisions and outpatient attendances were more modest. If all patients in all years had the 2021/22 average carbon footprint, then carbon equivalent to powering 19,976 UK homes for 1 year would have been saved.
Conclusions
Improving per-patient efficiency of surgery is likely to contribute towards meeting the NHS's net-zero target whilst also helping to improve patient outcomes, reduce costs and cut waiting lists.
{"title":"Estimated Carbon Savings from Changing Surgical Trends in Primary Elective Total Hip Arthroplasty in England: A Retrospective Observational Study","authors":"Jacob Koris, Elizabeth Ojelade, Hasina Begum, Maria Van-Hove, Tim W. R. Briggs, William K. Gray","doi":"10.1007/s40258-024-00916-x","DOIUrl":"10.1007/s40258-024-00916-x","url":null,"abstract":"<div><h3>Background</h3><p>The National Health Service (NHS) in England has set a target to be net zero for carbon emissions by 2045. The aim of this study was to investigate how changes in key aspects of clinical practice over the last 8 years have contributed towards reducing the per-patient carbon footprint of elective total hip arthroplasty (THA).</p><h3>Methods</h3><p>This was a retrospective analysis of administrative data. Data were extracted from the Hospital Episode Statistics database for all adult (≥ 17 years), primary, elective THA procedures conducted in England from 1 April, 2014 to 31 March, 2022. The estimated carbon footprint for key elements of the surgical pathway were calculated based on data from Greener NHS and the Sustainable Healthcare Coalition.</p><h3>Results</h3><p>Data were available for 537,441 THA procedures conducted during the study period. The per-patient carbon footprint associated with the primary THA (index) procedure fell by around 25% from 2014/15 to 2021/22. Length of stay was by far the largest contributor to this decline, falling from 169.1 kgCO<sub>2</sub>e to 117.6 kgCO<sub>2</sub>e per patient from 2014/15 to 2021/22. Absolute declines in the carbon footprint associated with emergency readmissions, revisions and outpatient attendances were more modest. If all patients in all years had the 2021/22 average carbon footprint, then carbon equivalent to powering 19,976 UK homes for 1 year would have been saved.</p><h3>Conclusions</h3><p>Improving per-patient efficiency of surgery is likely to contribute towards meeting the NHS's net-zero target whilst also helping to improve patient outcomes, reduce costs and cut waiting lists.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 1","pages":"85 - 92"},"PeriodicalIF":3.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1007/s40258-024-00918-9
Dawn Lee, Rose Hart, Darren Burns, Grant McCarthy
<div><h3>Background</h3><p>The method used to model general population mortality estimates in cohort models can make a meaningful difference in appraisals; particularly in scenarios involving potentially curative treatments where a prior National Institute for Health and Care Excellence (NICE) appraisal demonstrated that this assumption alone could make a difference of ~£10,000 to the incremental cost-effectiveness ratio.</p><h3>Objective</h3><p>Our objective was to evaluate the impact of different methods for calculating general population mortality estimates on the predicted total quality-adjusted life expectancy (QALE) as well as absolute and proportional quality-adjusted life year (QALY) shortfall calculations.</p><h3>Methods</h3><p>We employed three distinct methods for deriving general population mortality estimates: firstly, utilizing the population mean age at baseline; secondly, modelling the distribution of mean age at baseline by fitting a parametric distribution to patient-level data sourced from the Health Survey for England (HSE); and thirdly, modelling the empirical age distribution. Subsequently, we simulated patient age distributions to explore the effects of mean starting age and variance levels on the predicted QALE and applicable severity modifiers. Provided sample code in R and Visual Basic for Applications (VBA) facilitates the utilization of individual patient age and sex data to generate weighted average survival and health-related quality of life (utility) outputs.</p><h3>Results</h3><p>We observed differences of up to 10.4% (equivalent to a difference of 1.01 QALYs in quality-adjusted life-expectancy) between methods using the HSE dataset. In our simulation study, increasing variance in baseline age diminished the accuracy of predictions relying solely on mean age estimation. Differences of −0.30 to 2.24 QALYs were found at a standard deviation of 20%; commonly observed in trials. For potentially curative treatments this would represent a difference in economically justifiable price of -£4,500–+£33,600 at a cost-effectiveness threshold of £30,000 per QALY for a treatment with a 50% cure rate. For lower baseline ages, the population mean method tended to overestimate QALE, whereas for higher baseline ages, it tended to underestimate QALE compared with individual patient age-based approaches. The severity modifier assigned did not vary, however, apart from simulations with means at the extremes of the age distribution or with very high variance.</p><h3>Conclusions</h3><p>Our analysis underscores the necessity of accounting for the distribution of mean age at baseline, as failure to do so can lead to inaccurate QALE estimates, thereby affecting calculations of incremental costs and QALYs in models, which base survival and quality of life predictions on general population expectations. We would recommend that patient age and sex distribution should be accounted for when incorporating general population mortality in economic
{"title":"The Impact of the Approach to Accounting for Age and Sex in Economic Models on Predicted Quality-Adjusted Life-Years","authors":"Dawn Lee, Rose Hart, Darren Burns, Grant McCarthy","doi":"10.1007/s40258-024-00918-9","DOIUrl":"10.1007/s40258-024-00918-9","url":null,"abstract":"<div><h3>Background</h3><p>The method used to model general population mortality estimates in cohort models can make a meaningful difference in appraisals; particularly in scenarios involving potentially curative treatments where a prior National Institute for Health and Care Excellence (NICE) appraisal demonstrated that this assumption alone could make a difference of ~£10,000 to the incremental cost-effectiveness ratio.</p><h3>Objective</h3><p>Our objective was to evaluate the impact of different methods for calculating general population mortality estimates on the predicted total quality-adjusted life expectancy (QALE) as well as absolute and proportional quality-adjusted life year (QALY) shortfall calculations.</p><h3>Methods</h3><p>We employed three distinct methods for deriving general population mortality estimates: firstly, utilizing the population mean age at baseline; secondly, modelling the distribution of mean age at baseline by fitting a parametric distribution to patient-level data sourced from the Health Survey for England (HSE); and thirdly, modelling the empirical age distribution. Subsequently, we simulated patient age distributions to explore the effects of mean starting age and variance levels on the predicted QALE and applicable severity modifiers. Provided sample code in R and Visual Basic for Applications (VBA) facilitates the utilization of individual patient age and sex data to generate weighted average survival and health-related quality of life (utility) outputs.</p><h3>Results</h3><p>We observed differences of up to 10.4% (equivalent to a difference of 1.01 QALYs in quality-adjusted life-expectancy) between methods using the HSE dataset. In our simulation study, increasing variance in baseline age diminished the accuracy of predictions relying solely on mean age estimation. Differences of −0.30 to 2.24 QALYs were found at a standard deviation of 20%; commonly observed in trials. For potentially curative treatments this would represent a difference in economically justifiable price of -£4,500–+£33,600 at a cost-effectiveness threshold of £30,000 per QALY for a treatment with a 50% cure rate. For lower baseline ages, the population mean method tended to overestimate QALE, whereas for higher baseline ages, it tended to underestimate QALE compared with individual patient age-based approaches. The severity modifier assigned did not vary, however, apart from simulations with means at the extremes of the age distribution or with very high variance.</p><h3>Conclusions</h3><p>Our analysis underscores the necessity of accounting for the distribution of mean age at baseline, as failure to do so can lead to inaccurate QALE estimates, thereby affecting calculations of incremental costs and QALYs in models, which base survival and quality of life predictions on general population expectations. We would recommend that patient age and sex distribution should be accounted for when incorporating general population mortality in economic ","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 1","pages":"131 - 140"},"PeriodicalIF":3.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142339799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1007/s40258-024-00913-0
Mitchell L. Doucette, Dipak Hemraj, Emily Fisher, D. Luke Macfarlan
Introduction
Recent studies suggest that medical cannabis laws may contribute to a relative reduction in health insurance costs within the individual health insurance markets at the state level. We investigated the effects of adopting a medical cannabis law on the cost of employer-sponsored health insurance in the United States.
Methods
We analyzed state-level data from the Medical Expenditure Panel Survey–Insurance Component (MEPS-IC) Private Sector spanning from 2003 to 2022. The outcomes included log-transformed average total premium costs per employee for single, employee-plus-one, and family coverage plans. We utilized the Sun and Abraham (J Econometr 225(2):175–199, 2021) difference-in-difference (DiD) method, looking at the overall DiD and event-study DiD. Models were adjusted for various state-level demographics and dichotomous policy variables, including whether a state later adopted recreational cannabis, as well as time and unit fixed effects and population weights.
Results
For states that adopted a medical cannabis law, there was a significant decrease in the log average total premium per employee for single (−0.034, standard error [SE] 0.009 (−$238)) and employee-plus-one (−0.025, SE 0.009 (−$348)) coverage plans per year considering the first 10 years of policy change compared with states without such laws. Looking at the last 5 years of policy change, we saw increases in effect size and statistical significance. In-time placebo testing suggested model robustness. Under a hypothetical scenario where all 50 states adopted medical cannabis in 2022, we estimated that employers and employees could collectively save billions on healthcare coverage, potentially reducing healthcare expenditure's contribution to GDP by 0.65% in 2022.
Conclusion
Adoption of a medical cannabis law may contribute to decreases in healthcare costs. This phenomenon is likely a secondary effect and suggests positive externalities outside of medical cannabis patients.
{"title":"Measuring the Impact of Medical Cannabis Law Adoption on Employer-Sponsored Health Insurance Costs: A Difference-in-Difference Analysis, 2003–2022","authors":"Mitchell L. Doucette, Dipak Hemraj, Emily Fisher, D. Luke Macfarlan","doi":"10.1007/s40258-024-00913-0","DOIUrl":"10.1007/s40258-024-00913-0","url":null,"abstract":"<div><h3>Introduction</h3><p>Recent studies suggest that medical cannabis laws may contribute to a relative reduction in health insurance costs within the individual health insurance markets at the state level. We investigated the effects of adopting a medical cannabis law on the cost of employer-sponsored health insurance in the United States.</p><h3>Methods</h3><p>We analyzed state-level data from the Medical Expenditure Panel Survey–Insurance Component (MEPS-IC) Private Sector spanning from 2003 to 2022. The outcomes included log-transformed average total premium costs per employee for single, employee-plus-one, and family coverage plans. We utilized the Sun and Abraham (J Econometr 225(2):175–199, 2021) difference-in-difference (DiD) method, looking at the overall DiD and event-study DiD. Models were adjusted for various state-level demographics and dichotomous policy variables, including whether a state later adopted recreational cannabis, as well as time and unit fixed effects and population weights.</p><h3>Results</h3><p>For states that adopted a medical cannabis law, there was a significant decrease in the log average total premium per employee for single (−0.034, standard error [SE] 0.009 (−$238)) and employee-plus-one (−0.025, SE 0.009 (−$348)) coverage plans per year considering the first 10 years of policy change compared with states without such laws. Looking at the last 5 years of policy change, we saw increases in effect size and statistical significance. In-time placebo testing suggested model robustness. Under a hypothetical scenario where all 50 states adopted medical cannabis in 2022, we estimated that employers and employees could collectively save billions on healthcare coverage, potentially reducing healthcare expenditure's contribution to GDP by 0.65% in 2022.</p><h3>Conclusion</h3><p>Adoption of a medical cannabis law may contribute to decreases in healthcare costs. This phenomenon is likely a secondary effect and suggests positive externalities outside of medical cannabis patients.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 1","pages":"119 - 129"},"PeriodicalIF":3.1,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1007/s40258-024-00914-z
Ting Zhao, Michelle Tew, Talitha Feenstra, Pieter van Baal, Michael Willis, William J. Valentine, Philip M. Clarke, Barnaby Hunt, James Altunkaya, An Tran-Duy, Richard F. Pollock, Samuel J. P. Malkin, Andreas Nilsson, Phil McEwan, Volker Foos, Jose Leal, Elbert S. Huang, Neda Laiteerapong, Mark Lamotte, Harry Smolen, Jianchao Quan, Luís Martins, Mafalda Ramos, Andrew J. Palmer
Objective
This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus.
Methods
Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes.
Results
The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations.
Conclusions
For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations.
{"title":"The Impact of Unrelated Future Medical Costs on Economic Evaluation Outcomes for Different Models of Diabetes","authors":"Ting Zhao, Michelle Tew, Talitha Feenstra, Pieter van Baal, Michael Willis, William J. Valentine, Philip M. Clarke, Barnaby Hunt, James Altunkaya, An Tran-Duy, Richard F. Pollock, Samuel J. P. Malkin, Andreas Nilsson, Phil McEwan, Volker Foos, Jose Leal, Elbert S. Huang, Neda Laiteerapong, Mark Lamotte, Harry Smolen, Jianchao Quan, Luís Martins, Mafalda Ramos, Andrew J. Palmer","doi":"10.1007/s40258-024-00914-z","DOIUrl":"10.1007/s40258-024-00914-z","url":null,"abstract":"<div><h3>Objective</h3><p>This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus.</p><h3>Methods</h3><p>Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes.</p><h3>Results</h3><p>The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations.</p><h3>Conclusions</h3><p>For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"22 6","pages":"861 - 869"},"PeriodicalIF":3.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40258-024-00914-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1007/s40258-024-00908-x
Eva Rodríguez-Míguez, Antonio Sampayo
Objective
We assess whether the preferences regarding dependency-related health states as stated by informal caregivers are aligned with those expressed by the general population.
Methods
The preferences of a sample of 139 Spanish informal caregivers of dependent patients are compared with those obtained via a sample of 312 persons, also from the Spanish general population. We assess 24 dependency states extracted from the DEP-6D using the time trade-off method. Descriptive statistics and regression methods are used to explore differences between the two samples.
Results
Mean difference tests establish that, for all but one of the 24 states, there are no significant differences between the samples. The estimated mean values ranged from − 0.64 to 0.60 for the caregiver sample and from − 0.60 to 0.65 for the general population sample, with a correlation of 0.96. On average, the classification of states as better or worse than dead matched in both samples (except for one state). Regression models also show that sample type does not have a significant average impact. After we introduce interaction effects, only the most severe level of two dimensions, cognitive problems and housework, result in significant differences—with the caregiver sample reporting higher values for the former, and lower values for the latter.
Conclusion
Caregivers and the general population exhibit quite similar preferences concerning dependency-related health states. This suggests that the results of cost-utility analyses, and the resource allocation decisions based on them, would likewise not be significantly affected by the preferences used to generate the weighting algorithm.
{"title":"Comparison of Caregiver and General Population Preferences for Dependency-Related Health States","authors":"Eva Rodríguez-Míguez, Antonio Sampayo","doi":"10.1007/s40258-024-00908-x","DOIUrl":"10.1007/s40258-024-00908-x","url":null,"abstract":"<div><h3>Objective</h3><p>We assess whether the preferences regarding dependency-related health states as stated by informal caregivers are aligned with those expressed by the general population.</p><h3>Methods</h3><p>The preferences of a sample of 139 Spanish informal caregivers of dependent patients are compared with those obtained via a sample of 312 persons, also from the Spanish general population. We assess 24 dependency states extracted from the DEP-6D using the time trade-off method. Descriptive statistics and regression methods are used to explore differences between the two samples.</p><h3>Results</h3><p>Mean difference tests establish that, for all but one of the 24 states, there are no significant differences between the samples. The estimated mean values ranged from − 0.64 to 0.60 for the caregiver sample and from − 0.60 to 0.65 for the general population sample, with a correlation of 0.96. On average, the classification of states as better or worse than dead matched in both samples (except for one state). Regression models also show that sample type does not have a significant average impact. After we introduce interaction effects, only the most severe level of two dimensions, cognitive problems and housework, result in significant differences—with the caregiver sample reporting higher values for the former, and lower values for the latter.</p><h3>Conclusion</h3><p>Caregivers and the general population exhibit quite similar preferences concerning dependency-related health states. This suggests that the results of cost-utility analyses, and the resource allocation decisions based on them, would likewise not be significantly affected by the preferences used to generate the weighting algorithm.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 1","pages":"105 - 117"},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40258-024-00908-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patients may get more treatment options with off-label use of drugs while exposed to unknown risks of adverse events. Little is known about the public or demand-side perspective on off-label drug use, which is important to understand how to use off-label treatment and devise financial assistance. This study aimed to quantify public preference for off-label cancer treatment outcomes, process, and costs, and perceived importance of associated adverse events.
Methods
A discrete choice experiment and a best-worst scaling were conducted in Hong Kong in December 2022. Quota sampling was used to randomly select the study sample from a territory-wide panel of working-age adults. Preferences and willingness to pay (WTP) for treatment effectiveness, risk of adverse events, mode of drug administration, and availability of off-label treatment guidelines were estimated using a random parameter logit model and latent class model. The relative importance of different adverse events was elicited using Case 1 best-worst scaling.
Results
A total of 435 respondents provided valid responses. In the discrete choice experiment, the respondents indicated that extra overall survival as treatment effectiveness (WTP: HK$448,000/US$57,400 for 12-month vs 3-month extra survival) was the most important attribute for off-label drugs, followed by the risk of adverse events (WTP: HK$318,000/US$40,800 for 10% chance to have adverse event vs 55%), mode of drug administration (WTP: HK$42,000/US$5300 for oral intake vs injection), and availability of guidelines (WTP: HK$31,000/US$4000 for available versus not available). Four groups with distinct preferences were identified, including effectiveness oriented, off-label use refusal, oral intake oriented, and adverse event risk aversion. In the best-worse scaling, hypothyroidism, nausea/vomiting, and arthralgia/joint pain were the three most important adverse events based on the perceptions of respondents. Risk-averse respondents, who were identified from the discrete choice experiment, had different perceived importance of the adverse events compared with those with other preferences.
Conclusions
Knowing the preference and WTP for cancer treatment-related characteristics from a societal perspective facilitates doctors’ communications with patients on decision making and treatment goal-setting for off-label treatment, and enables devising financial assistance for related treatments. This study also provides important insight to inform evaluations of public acceptance and information dissemination in drug development as well as future economic evaluations.
{"title":"Public Preference for Off-Label Use of Drugs for Cancer Treatment and Relative Importance of Associated Adverse Events: A Discrete Choice Experiment and Best-Worst Scaling","authors":"Kailu Wang, Ho-Man Shum, Carrie Ho-Kwan Yam, Yushan Wu, Eliza Lai-Yi Wong, Eng-Kiong Yeoh","doi":"10.1007/s40258-024-00912-1","DOIUrl":"10.1007/s40258-024-00912-1","url":null,"abstract":"<div><h3>Background and Objective</h3><p>Patients may get more treatment options with off-label use of drugs while exposed to unknown risks of adverse events. Little is known about the public or demand-side perspective on off-label drug use, which is important to understand how to use off-label treatment and devise financial assistance. This study aimed to quantify public preference for off-label cancer treatment outcomes, process, and costs, and perceived importance of associated adverse events.</p><h3>Methods</h3><p>A discrete choice experiment and a best-worst scaling were conducted in Hong Kong in December 2022. Quota sampling was used to randomly select the study sample from a territory-wide panel of working-age adults. Preferences and willingness to pay (WTP) for treatment effectiveness, risk of adverse events, mode of drug administration, and availability of off-label treatment guidelines were estimated using a random parameter logit model and latent class model. The relative importance of different adverse events was elicited using Case 1 best-worst scaling.</p><h3>Results</h3><p>A total of 435 respondents provided valid responses. In the discrete choice experiment, the respondents indicated that extra overall survival as treatment effectiveness (WTP: HK$448,000/US$57,400 for 12-month vs 3-month extra survival) was the most important attribute for off-label drugs, followed by the risk of adverse events (WTP: HK$318,000/US$40,800 for 10% chance to have adverse event vs 55%), mode of drug administration (WTP: HK$42,000/US$5300 for oral intake vs injection), and availability of guidelines (WTP: HK$31,000/US$4000 for available versus not available). Four groups with distinct preferences were identified, including effectiveness oriented, off-label use refusal, oral intake oriented, and adverse event risk aversion. In the best-worse scaling, hypothyroidism, nausea/vomiting, and arthralgia/joint pain were the three most important adverse events based on the perceptions of respondents. Risk-averse respondents, who were identified from the discrete choice experiment, had different perceived importance of the adverse events compared with those with other preferences.</p><h3>Conclusions</h3><p>Knowing the preference and WTP for cancer treatment-related characteristics from a societal perspective facilitates doctors’ communications with patients on decision making and treatment goal-setting for off-label treatment, and enables devising financial assistance for related treatments. This study also provides important insight to inform evaluations of public acceptance and information dissemination in drug development as well as future economic evaluations.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"22 6","pages":"849 - 860"},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40258-024-00912-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142279690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1007/s40258-024-00910-3
A. Alex Levine, Daniel E. Enright, Katherine A. Clifford, Stacey Kowal, James D. Chambers
Objective
The aim of this study was to examine the association between characteristics of novel drugs and incremental health gains relative to standard of care, in terms of quality-adjusted life-years (QALYs).
Methods
This study’s unit of analysis is the drug–indication pair. For pairs approved by the US FDA from 1999 to 2018, we quantified incremental health gains using QALYs from the published literature and characterized each pair’s novelty in terms of a series of six binary (yes/no) characteristics of novel drugs given special consideration by Health Technology Assessment agencies: Novel mechanism of action, Indicated for a rare disease, Indicated for a pediatric population, Treats a serious condition, Offers meaningful improvement over available therapies, and Potential to address unmet clinical needs. We analyzed measures of bivariate association (Mann-Whitney U and Kolmogorov-Smirnov tests) and multivariable regression, accounting for the influence of multiple novelty characteristics simultaneously.
Results
Our sample of 146 drugs represents 21% of drugs approved the FDA in the time period (1999–2018). Median and mean QALY gains for ‘novel’ drug–indication pairs exceeded corresponding QALY gains for non-novel drug–indication pairs. For most comparisons, the bivariate relationships between QALY gains and novelty characteristics were significant at p < 0.05 except for novel mechanism of action (Kolmogorov-Smirnov test) and pediatric indication (both bivariate tests). Multivariable models revealed an independent association between novelty characteristics and QALY gain except for unmet clinical need and indicated for a rare disease.
Conclusions
Drugs with novelty characteristics conferred larger health gains than drugs without these characteristics in bivariate analysis, multivariable models, or both. Future research should examine other aspects of drug novelty, such as patient and health system costs and equitable access.
研究目的本研究的目的是以质量调整生命年（QALYs）为单位，研究新型药物的特征与相对于标准护理的增量健康收益之间的关联：本研究的分析单位是药物-适应症配对。对于美国 FDA 在 1999 年至 2018 年期间批准的药物配对，我们使用已发表文献中的 QALYs 量化了增量健康收益，并根据健康技术评估机构特别考虑的新型药物的六种二进制（是/否）特征来描述每对药物配对的新颖性：新的作用机制、适用于罕见疾病、适用于儿科人群、治疗严重疾病、与现有疗法相比有明显改善、有可能满足未满足的临床需求。我们分析了二元相关性（Mann-Whitney U 和 Kolmogorov-Smirnov 检验）和多变量回归，同时考虑了多种新特性的影响：我们的146种药物样本占1999-2018年期间FDA批准药物的21%。新型 "药物-适应症配对的QALY收益中位数和平均值超过了非新型药物-适应症配对的相应QALY收益。在大多数比较中，QALY 收益与新颖性特征之间的双变量关系在 p 结论下具有显著性：在双变量分析、多变量模型或两者中，具有新颖性特征的药物比不具有这些特征的药物能带来更大的健康收益。未来的研究应考察药物新颖性的其他方面，如患者和医疗系统成本以及公平获取。
{"title":"Are Drug Novelty Characteristics Associated With Greater Health Benefits?","authors":"A. Alex Levine, Daniel E. Enright, Katherine A. Clifford, Stacey Kowal, James D. Chambers","doi":"10.1007/s40258-024-00910-3","DOIUrl":"10.1007/s40258-024-00910-3","url":null,"abstract":"<div><h3>Objective</h3><p>The aim of this study was to examine the association between characteristics of novel drugs and incremental health gains relative to standard of care, in terms of quality-adjusted life-years (QALYs).</p><h3>Methods</h3><p>This study’s unit of analysis is the drug–indication pair. For pairs approved by the US FDA from 1999 to 2018, we quantified incremental health gains using QALYs from the published literature and characterized each pair’s novelty in terms of a series of six binary (yes/no) characteristics of novel drugs given special consideration by Health Technology Assessment agencies: <i>Novel mechanism of action</i>, <i>Indicated for a rare disease</i>, <i>Indicated for a pediatric population</i>, <i>Treats a serious condition</i>, <i>Offers meaningful improvement over available therapies</i>, and <i>Potential to address unmet clinical needs</i>. We analyzed measures of bivariate association (Mann-Whitney U and Kolmogorov-Smirnov tests) and multivariable regression, accounting for the influence of multiple novelty characteristics simultaneously.</p><h3>Results</h3><p>Our sample of 146 drugs represents 21% of drugs approved the FDA in the time period (1999–2018). Median and mean QALY gains for ‘novel’ drug–indication pairs exceeded corresponding QALY gains for non-novel drug–indication pairs. For most comparisons, the bivariate relationships between QALY gains and novelty characteristics were significant at <i>p </i>< 0.05 except for <i>novel mechanism of action</i> (Kolmogorov-Smirnov test) and <i>pediatric indication</i> (both bivariate tests). Multivariable models revealed an independent association between novelty characteristics and QALY gain except for <i>unmet clinical need</i> and <i>indicated for a rare disease</i>.</p><h3>Conclusions</h3><p>Drugs with novelty characteristics conferred larger health gains than drugs without these characteristics in bivariate analysis, multivariable models, or both. Future research should examine other aspects of drug novelty, such as patient and health system costs and equitable access.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"22 6","pages":"827 - 832"},"PeriodicalIF":3.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142118841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1007/s40258-024-00906-z
Neil Hawkins, Janet Bouttell, Dmitry Ponomarev
Background
Predictive biomarkers are intended to predict an individual’s expected response to specific treatments. These are an important component of precision medicine. We explore measures of biomarker performance that are based on the expected probability of response to individual treatment conditional on biomarker status. We show how these measures can be used to establish thresholds at which testing strategies will be clinically superior.
Methods
We used a decision model to compare expected probabilities of response of treat-all and test-and-treat strategies. Based on this, R-Shiny-based apps were developed which produce plots of the threshold positive and negative predictive values or sensitivities and specificities above which a ‘test-and-treat’ strategy will outperform a ‘treat-all’ strategy. We present a case study using data on the use of RAS status to predict response to panitumumab in metastatic colorectal cancer.
Results
Where a companion diagnostic is predictive of response to one of the treatments being compared, it is possible to estimate threshold sensitivities and specificities above which a testing strategy will outperform a treat-all strategy, based only on the odds ratio of response. Where negative and positive predictive values were used, the threshold depended on the prevalence of the biomarker-positive patients.
Discussion
These intuitive performance measures for predictive biomarkers, based on expected response to individual treatments, can be used to identify promising candidate companion diagnostic tests and indicate the potential magnitude of the net benefit of testing.
{"title":"Measures of Performance and Clinical Superiority Thresholds for ‘Test-and-treat’ Predictive Biomarkers","authors":"Neil Hawkins, Janet Bouttell, Dmitry Ponomarev","doi":"10.1007/s40258-024-00906-z","DOIUrl":"10.1007/s40258-024-00906-z","url":null,"abstract":"<div><h3>Background</h3><p>Predictive biomarkers are intended to predict an individual’s expected response to specific treatments. These are an important component of precision medicine. We explore measures of biomarker performance that are based on the expected probability of response to individual treatment conditional on biomarker status. We show how these measures can be used to establish thresholds at which testing strategies will be clinically superior.</p><h3>Methods</h3><p>We used a decision model to compare expected probabilities of response of treat-all and test-and-treat strategies. Based on this, R-Shiny-based apps were developed which produce plots of the threshold positive and negative predictive values or sensitivities and specificities above which a ‘test-and-treat’ strategy will outperform a ‘treat-all’ strategy. We present a case study using data on the use of RAS status to predict response to panitumumab in metastatic colorectal cancer.</p><h3>Results</h3><p>Where a companion diagnostic is predictive of response to one of the treatments being compared, it is possible to estimate threshold sensitivities and specificities above which a testing strategy will outperform a treat-all strategy, based only on the odds ratio of response. Where negative and positive predictive values were used, the threshold depended on the prevalence of the biomarker-positive patients.</p><h3>Discussion</h3><p>These intuitive performance measures for predictive biomarkers, based on expected response to individual treatments, can be used to identify promising candidate companion diagnostic tests and indicate the potential magnitude of the net benefit of testing.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 1","pages":"65 - 74"},"PeriodicalIF":3.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With its clear focus on financial protection, government-funded health insurance (GFHI) stands out among the strategies for universal health coverage (UHC) implemented by low-to-middle income countries globally. Since 2018, India has implemented a GFHI programme called the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which covers 500 million individuals. The current study aims to evaluate the performance of GFHI in meeting its key objectives of improving access, quality and financial protection for hospital-based care in two large central Indian states: Madhya Pradesh and Maharashtra.
Methods
The study measures access in terms of utilisation of inpatient care. Financial protection was measured in terms of catastrophic health expenditure which was defined as the incidence of out-of-pocket expenditure (OOPE) above thresholds of 10% and 25% of annual household expenditure. Patient-satisfaction with care was taken as an indicator of quality. A household survey was conducted in 2023, covering a multi-stage sample of 11,569 and 12,384 individuals in Madhya Pradesh and Maharashtra, respectively. Multi-variate analyses were conducted to find the effect of GFHI-enrolment on the desired outcomes. The instrumental variable method was applied to address potential endogeneity in insurance enrolment. Additionally, propensity score matching was done to ensure robustness.
Results
Around 71% and 63% of surveyed individuals were enrolled under GFHI in Madhya Pradesh and Maharashtra, respectively. The hospitalisation rate did not differ much between the GFHI-enrolled and non-enrolled population. The average OOPE on hospitalisation was similar for the GFHI-enrolled and non-enrolled patients. The OOPE and catastrophic health expenditure in private hospitals remained very high, irrespective of GFHI enrolment. The pattern was similar in both states. Multi-variate adjusted models showed that GFHI had no significant effect on utilisation, quality, OOPE and catastrophic health expenditure. The above results were confirmed by propensity score matching.
Conclusions
Coverage by GFHI enrolment was ineffective in improving access, quality or financial protection for inpatient hospital care despite 5 years of implementation of the programme. Long-standing supply-side gaps and poor regulation of private providers continue to hamper the effectiveness of GFHI in India.
{"title":"Impact of Government-Funded Health Insurance on Out-of-Pocket Expenditure and Quality of Hospital-Based Care in Indian States of Madhya Pradesh and Maharashtra","authors":"Samir Garg, Kirtti Kumar Bebarta, Narayan Tripathi, Vikash Ranjan Keshri","doi":"10.1007/s40258-024-00911-2","DOIUrl":"10.1007/s40258-024-00911-2","url":null,"abstract":"<div><h3>Background</h3><p>With its clear focus on financial protection, government-funded health insurance (GFHI) stands out among the strategies for universal health coverage (UHC) implemented by low-to-middle income countries globally. Since 2018, India has implemented a GFHI programme called the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which covers 500 million individuals. The current study aims to evaluate the performance of GFHI in meeting its key objectives of improving access, quality and financial protection for hospital-based care in two large central Indian states: Madhya Pradesh and Maharashtra.</p><h3>Methods</h3><p>The study measures access in terms of utilisation of inpatient care. Financial protection was measured in terms of catastrophic health expenditure which was defined as the incidence of out-of-pocket expenditure (OOPE) above thresholds of 10% and 25% of annual household expenditure. Patient-satisfaction with care was taken as an indicator of quality. A household survey was conducted in 2023, covering a multi-stage sample of 11,569 and 12,384 individuals in Madhya Pradesh and Maharashtra, respectively. Multi-variate analyses were conducted to find the effect of GFHI-enrolment on the desired outcomes. The instrumental variable method was applied to address potential endogeneity in insurance enrolment. Additionally, propensity score matching was done to ensure robustness.</p><h3>Results</h3><p>Around 71% and 63% of surveyed individuals were enrolled under GFHI in Madhya Pradesh and Maharashtra, respectively. The hospitalisation rate did not differ much between the GFHI-enrolled and non-enrolled population. The average OOPE on hospitalisation was similar for the GFHI-enrolled and non-enrolled patients. The OOPE and catastrophic health expenditure in private hospitals remained very high, irrespective of GFHI enrolment. The pattern was similar in both states. Multi-variate adjusted models showed that GFHI had no significant effect on utilisation, quality, OOPE and catastrophic health expenditure. The above results were confirmed by propensity score matching.</p><h3>Conclusions</h3><p>Coverage by GFHI enrolment was ineffective in improving access, quality or financial protection for inpatient hospital care despite 5 years of implementation of the programme. Long-standing supply-side gaps and poor regulation of private providers continue to hamper the effectiveness of GFHI in India.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"22 6","pages":"815 - 825"},"PeriodicalIF":3.1,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142054752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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