Abstract The world of patient-centered outcomes research (PCOR) seems to bridge the previously disjointed worlds of comparative effectiveness research (CER) and personalized medicine (PM). Indeed, theoretical reasoning on how information on medical quality should inform decision making, both at the individual and the policy level, reveals that personalized information on the value of medical products is critical for improving decision making at all levels. However, challenges to generating, evaluating and translating evidence that might lead to personalization need to be critically assessed. In this paper, I discuss two different concepts of personalized medicine – passive personalization (PPM) and active personalization (APM) that are important to distinguish in order to invest efficiently in PCOR and develop objective evidence on the value of personalization that will aid in its translation. APM constitutes the process of actively seeking identifiers, which can be genotypical, phenotypical or even environmental, that can be used to differentiate between the marginal benefits of treatment across patients. In contrast, PPM involves a passive approach to personalization where, in the absence of explicit research to discover identifiers, patients and physicians “learn by doing” mostly due to the repeated use of similar products on similar patients. Benchmarking the current state of PPM sets the bar to which the expected value of any new APM agenda should be evaluated. Exploring processes that enable PPM in practice can help discover new APM agendas, such as those based on developing predictive algorithms based on clinical, phenotypical and preference data, which may be more efficient that trying to develop expensive genetic tests. It can also identify scenarios or subgroups of patients where genomic research would be most valuable since alternative prediction algorithms were difficult to develop in those settings. Two clinical scenarios are discussed where PPM was explored through novel econometric methods. Related discussions around exploring PPM processes, multi-dimensionality of outcomes, and a balanced agenda for future research on personalization follow.
{"title":"Personalized Medicine in the Context of Comparative Effectiveness Research","authors":"A. Basu","doi":"10.1515/fhep-2013-0009","DOIUrl":"https://doi.org/10.1515/fhep-2013-0009","url":null,"abstract":"Abstract The world of patient-centered outcomes research (PCOR) seems to bridge the previously disjointed worlds of comparative effectiveness research (CER) and personalized medicine (PM). Indeed, theoretical reasoning on how information on medical quality should inform decision making, both at the individual and the policy level, reveals that personalized information on the value of medical products is critical for improving decision making at all levels. However, challenges to generating, evaluating and translating evidence that might lead to personalization need to be critically assessed. In this paper, I discuss two different concepts of personalized medicine – passive personalization (PPM) and active personalization (APM) that are important to distinguish in order to invest efficiently in PCOR and develop objective evidence on the value of personalization that will aid in its translation. APM constitutes the process of actively seeking identifiers, which can be genotypical, phenotypical or even environmental, that can be used to differentiate between the marginal benefits of treatment across patients. In contrast, PPM involves a passive approach to personalization where, in the absence of explicit research to discover identifiers, patients and physicians “learn by doing” mostly due to the repeated use of similar products on similar patients. Benchmarking the current state of PPM sets the bar to which the expected value of any new APM agenda should be evaluated. Exploring processes that enable PPM in practice can help discover new APM agendas, such as those based on developing predictive algorithms based on clinical, phenotypical and preference data, which may be more efficient that trying to develop expensive genetic tests. It can also identify scenarios or subgroups of patients where genomic research would be most valuable since alternative prediction algorithms were difficult to develop in those settings. Two clinical scenarios are discussed where PPM was explored through novel econometric methods. Related discussions around exploring PPM processes, multi-dimensionality of outcomes, and a balanced agenda for future research on personalization follow.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78940140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Personalized prevention uses family history and predictive genetic testing to identify people at high risk of serious diseases. The availability of predictive genetic tests is a newer and still-developing phenomenon. Many observers see tremendous potential for personalized prevention to improve public health. At the same time, the emergence of these new markets raises familiar health policy concerns about costs, cost-effectiveness, and health disparities. This paper first discusses an economic framework for the analysis of personalized prevention. On the demand side, consumers use personalized prevention as a form of information that allows them to make better choices about prevention, including medical care and health behaviors like diet and exercise. On the supply side, an interplay of complex market forces and regulations will determine the prices, advertising, and insurance coverage of predictive genetic tests. Beyond the question of whether health insurance will cover the costs of predictive genetic tests, there is a great deal of concern about whether consumers’ use of genetic tests might place them at risk of genetic discrimination or might lead to adverse selection. The paper also reports descriptive analysis of data from the 2000, 2005, and 2010 National Health Interview Surveys on the use of predictive genetic tests. The empirical analysis documents large socioeconomic status-related disparities in consumers having heard of genetic tests: for example, consumers with less schooling, Blacks, and Hispanics were substantially less likely to have heard of genetic tests. Evidence from other empirical studies provides little evidence that genetic testing leads to genetic discrimination in insurance markets. There is more evidence suggesting adverse selection, where genetic testing leads consumers to purchase long-term care insurance. The paper concludes with some preliminary thoughts about important directions for future research. The goal of the paper is to review relevant research to help develop an economic approach and social science research agenda into the determinants and consequences of genetic tests for prevention.
{"title":"The Economics of Personalization in Prevention and Public Health","authors":"D. Kenkel, Hua Wang","doi":"10.1515/fhep-2013-0011","DOIUrl":"https://doi.org/10.1515/fhep-2013-0011","url":null,"abstract":"Abstract Personalized prevention uses family history and predictive genetic testing to identify people at high risk of serious diseases. The availability of predictive genetic tests is a newer and still-developing phenomenon. Many observers see tremendous potential for personalized prevention to improve public health. At the same time, the emergence of these new markets raises familiar health policy concerns about costs, cost-effectiveness, and health disparities. This paper first discusses an economic framework for the analysis of personalized prevention. On the demand side, consumers use personalized prevention as a form of information that allows them to make better choices about prevention, including medical care and health behaviors like diet and exercise. On the supply side, an interplay of complex market forces and regulations will determine the prices, advertising, and insurance coverage of predictive genetic tests. Beyond the question of whether health insurance will cover the costs of predictive genetic tests, there is a great deal of concern about whether consumers’ use of genetic tests might place them at risk of genetic discrimination or might lead to adverse selection. The paper also reports descriptive analysis of data from the 2000, 2005, and 2010 National Health Interview Surveys on the use of predictive genetic tests. The empirical analysis documents large socioeconomic status-related disparities in consumers having heard of genetic tests: for example, consumers with less schooling, Blacks, and Hispanics were substantially less likely to have heard of genetic tests. Evidence from other empirical studies provides little evidence that genetic testing leads to genetic discrimination in insurance markets. There is more evidence suggesting adverse selection, where genetic testing leads consumers to purchase long-term care insurance. The paper concludes with some preliminary thoughts about important directions for future research. The goal of the paper is to review relevant research to help develop an economic approach and social science research agenda into the determinants and consequences of genetic tests for prevention.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80107589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Economics of Personalized Health Care and Prevention: Introduction","authors":"Gregory Bloss, J. Haaga","doi":"10.1515/fhep-2013-0018","DOIUrl":"https://doi.org/10.1515/fhep-2013-0018","url":null,"abstract":"","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78543040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The objective of this paper is to provide an overview of economic evaluation of personalized medicine, focusing particularly on the use of cost-effectiveness analysis and other methods of valuation. We draw on insights from the literature and our work at the University of California, San Francisco Center for Translational and Policy Research on Personalized Medicine (TRANSPERS). We begin with a discussion of why personalized medicine is of interest and challenges to adoption, whether personalized medicine is different enough to require different evaluation approaches, and what is known about the economics of personalized medicine. We then discuss insights from TRANSPERS research and six areas for future research: Develop and Apply Multiple Methods of Assessing Value Identify Key Factors in Determining the Value of Personalized Medicine Use Real World Perspectives in Economic Analyses Consider Patient Heterogeneity and Diverse Populations in Economic Analyses Prepare for Upcoming Challenges of Assessing Value of Emerging Technologies Incorporate Behavioral Economics into Value Assessments
{"title":"Economic Perspectives on Personalized Health Care and Prevention","authors":"K. Phillips, J. Sakowski, S. Liang, N. Ponce","doi":"10.1515/fhep-2013-0010","DOIUrl":"https://doi.org/10.1515/fhep-2013-0010","url":null,"abstract":"Abstract The objective of this paper is to provide an overview of economic evaluation of personalized medicine, focusing particularly on the use of cost-effectiveness analysis and other methods of valuation. We draw on insights from the literature and our work at the University of California, San Francisco Center for Translational and Policy Research on Personalized Medicine (TRANSPERS). We begin with a discussion of why personalized medicine is of interest and challenges to adoption, whether personalized medicine is different enough to require different evaluation approaches, and what is known about the economics of personalized medicine. We then discuss insights from TRANSPERS research and six areas for future research: Develop and Apply Multiple Methods of Assessing Value Identify Key Factors in Determining the Value of Personalized Medicine Use Real World Perspectives in Economic Analyses Consider Patient Heterogeneity and Diverse Populations in Economic Analyses Prepare for Upcoming Challenges of Assessing Value of Emerging Technologies Incorporate Behavioral Economics into Value Assessments","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86052142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Do differences in rates of use among managed care and Fee-for-Service Medicare beneficiaries reflect selection bias or successful care management by insurers? I demonstrate a new method to estimate the treatment effect of insurance status on health care utilization. Using clinical information and risk-adjustment techniques on data on acute admission that are unrelated to recent medical care, I create a proxy measure of unobserved health status. I find that positive selection accounts for between one-quarter and one-third of the risk-adjusted differences in rates of hospitalization for ambulatory care sensitive conditions and elective procedures among Medicare managed care and Fee-for-Service enrollees in 7 years of Healthcare Cost and Utilization Project State Inpatient Databases from Arizona, Florida, New Jersey and New York matched to Medicare enrollment data. Beyond selection effects, I find that managed care plans reduce rates of potentially preventable hospitalizations by 12.5 per 1,000 enrollees (compared to mean of 46 per 1,000) and reduce annual rates of elective admissions by 4 per 1,000 enrollees (mean 18.6 per 1,000).
{"title":"Better Quality of Care or Healthier Patients? Hospital Utilization by Medicare Advantage and Fee-for-Service Enrollees.","authors":"Lauren Hersch Nicholas","doi":"10.1515/fhep-2012-0037","DOIUrl":"10.1515/fhep-2012-0037","url":null,"abstract":"<p><p>Do differences in rates of use among managed care and Fee-for-Service Medicare beneficiaries reflect selection bias or successful care management by insurers? I demonstrate a new method to estimate the treatment effect of insurance status on health care utilization. Using clinical information and risk-adjustment techniques on data on acute admission that are unrelated to recent medical care, I create a proxy measure of unobserved health status. I find that positive selection accounts for between one-quarter and one-third of the risk-adjusted differences in rates of hospitalization for ambulatory care sensitive conditions and elective procedures among Medicare managed care and Fee-for-Service enrollees in 7 years of Healthcare Cost and Utilization Project State Inpatient Databases from Arizona, Florida, New Jersey and New York matched to Medicare enrollment data. Beyond selection effects, I find that managed care plans reduce rates of potentially preventable hospitalizations by 12.5 per 1,000 enrollees (compared to mean of 46 per 1,000) and reduce annual rates of elective admissions by 4 per 1,000 enrollees (mean 18.6 per 1,000).</p>","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/fhep-2012-0037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32120735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper studies the relationship between civil war and HIV/AIDS in Burundi at the micro level. The case of Burundi provides interesting grounds of analysis, as seroprevalence rates are heterogeneous across the country, the serological and conflict data for Burundi are of good quality and conclusions can inform HIV/AIDS policies in Burundi and other fragile states. Ordinary least squares and instrumental variable results indicate that there is no empirical relationship between seroprevalence at the general population level and three measures of local conflict intensity within provinces. This evidence could imply that areas that are relatively more conflict affected do not need to be prioritized over others in terms of HIV/AIDS policies. Further research should focus on individual rather than geographical exposure to conflict. There are likely certain groups and individuals at risk in the general population that need special attention after conflict. Furthermore, violence changes societies, in particular gender relations, thereby indirectly feeding and possibly fueling the dynamics of the epidemic.
{"title":"AIDS and Conflict: Micro Evidence from Burundi1)","authors":"Matthias Rieger","doi":"10.1515/fhep-2012-0035","DOIUrl":"https://doi.org/10.1515/fhep-2012-0035","url":null,"abstract":"Abstract This paper studies the relationship between civil war and HIV/AIDS in Burundi at the micro level. The case of Burundi provides interesting grounds of analysis, as seroprevalence rates are heterogeneous across the country, the serological and conflict data for Burundi are of good quality and conclusions can inform HIV/AIDS policies in Burundi and other fragile states. Ordinary least squares and instrumental variable results indicate that there is no empirical relationship between seroprevalence at the general population level and three measures of local conflict intensity within provinces. This evidence could imply that areas that are relatively more conflict affected do not need to be prioritized over others in terms of HIV/AIDS policies. Further research should focus on individual rather than geographical exposure to conflict. There are likely certain groups and individuals at risk in the general population that need special attention after conflict. Furthermore, violence changes societies, in particular gender relations, thereby indirectly feeding and possibly fueling the dynamics of the epidemic.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80964822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neeraj Sood, Zachary Wagner, Peter Huckfeldt, Amelia Haviland
We use health insurance claims data from 63 large employers to estimate the extent of price shopping for nine common outpatient services in consumer-directed health plans (CDHPs) compared to traditional health plans. The main measures of price-shopping include: (1) the total price paid on the claim, (2) the share of claims from low and high cost providers and (3) the savings from price shopping relative to choosing prices randomly. All analyses control for individual and zip code level demographics and plan characteristics. We also estimate differences in price shopping within CDHPs depending on expected health care costs and whether the service was bought before or after reaching the deductible. For 8 out of 9 services analyzed, prices paid by CDHP and traditional plan enrollees did not differ significantly; CDHP enrollees paid 2.3% less for office visits. Similarly, office visits was the only service where CDHP enrollment resulted in a significantly larger share of claims from low cost providers and greater savings from price shopping relative to traditional plans. There was also no evidence that, within CDHP plans, consumers with lower expected medical expenses exhibited more price-shopping or that consumers exhibited more price-shopping before reaching the deductible.
{"title":"Price-Shopping in Consumer-Directed Health Plans.","authors":"Neeraj Sood, Zachary Wagner, Peter Huckfeldt, Amelia Haviland","doi":"10.1515/thep-2012-0028","DOIUrl":"https://doi.org/10.1515/thep-2012-0028","url":null,"abstract":"<p><p>We use health insurance claims data from 63 large employers to estimate the extent of price shopping for nine common outpatient services in consumer-directed health plans (CDHPs) compared to traditional health plans. The main measures of price-shopping include: (1) the total price paid on the claim, (2) the share of claims from low and high cost providers and (3) the savings from price shopping relative to choosing prices randomly. All analyses control for individual and zip code level demographics and plan characteristics. We also estimate differences in price shopping within CDHPs depending on expected health care costs and whether the service was bought before or after reaching the deductible. For 8 out of 9 services analyzed, prices paid by CDHP and traditional plan enrollees did not differ significantly; CDHP enrollees paid 2.3% less for office visits. Similarly, office visits was the only service where CDHP enrollment resulted in a significantly larger share of claims from low cost providers and greater savings from price shopping relative to traditional plans. There was also no evidence that, within CDHP plans, consumers with lower expected medical expenses exhibited more price-shopping or that consumers exhibited more price-shopping before reaching the deductible.</p>","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4204109/pdf/nihms491189.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32770820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We develop an empirical method to assess the degree of financial exposure associated with medical care spending among non-elderly US families with employer-sponsored insurance. A key feature of this method is its simplicity – it only requires data on out-of-pocket (OOP) health care spending and total health care spending and does not require detailed knowledge of health insurance benefit design. We apply our method to assess whether families with a chronically ill member face more financial exposure given their level of total spending relative to families with no chronically ill members. We find that the insured chronically ill face more financial exposure than the insured non-chronically ill. Additional analyses suggest that the reason for this additional financial exposure is not that families with a chronically ill member are in different, less generous plans, on average. Rather, families with a chronically ill member have higher spending on certain types of medical services (e.g., pharmaceuticals) that face higher levels of coinsurance. Given recent work on value-based insurance design and coinsurance as an obstacle to medication adherence, our findings suggest that the current design of health plans could jeopardize both the health and the financial well-being of the chronically ill.
{"title":"Measuring the Financial Exposure from Medical Care Spending Among Families with Employer Sponsored Insurance","authors":"J. Abraham, A. Royalty, T. DeLeire","doi":"10.1515/fhep-2012-0012","DOIUrl":"https://doi.org/10.1515/fhep-2012-0012","url":null,"abstract":"Abstract We develop an empirical method to assess the degree of financial exposure associated with medical care spending among non-elderly US families with employer-sponsored insurance. A key feature of this method is its simplicity – it only requires data on out-of-pocket (OOP) health care spending and total health care spending and does not require detailed knowledge of health insurance benefit design. We apply our method to assess whether families with a chronically ill member face more financial exposure given their level of total spending relative to families with no chronically ill members. We find that the insured chronically ill face more financial exposure than the insured non-chronically ill. Additional analyses suggest that the reason for this additional financial exposure is not that families with a chronically ill member are in different, less generous plans, on average. Rather, families with a chronically ill member have higher spending on certain types of medical services (e.g., pharmaceuticals) that face higher levels of coinsurance. Given recent work on value-based insurance design and coinsurance as an obstacle to medication adherence, our findings suggest that the current design of health plans could jeopardize both the health and the financial well-being of the chronically ill.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88136753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We use health insurance claims data from 63 large employers to estimate the extent of price shopping for nine common outpatient services in consumer-directed health plans (CDHPs) compared to traditional health plans. The main measures of price shopping include (1) the total price paid on the claim, (2) the share of claims from low- and high-cost providers, and (3) the savings from price shopping relative to choosing prices randomly. All analyses control for individual and zip code level demographics and plan characteristics. We also estimate differences in price shopping within CDHPs depending on expected health care costs and whether the service was bought before or after reaching the deductible. For eight out of nine services analyzed, prices paid by CDHP and traditional plan enrollees did not differ significantly; CDHP enrollees paid 2.3% less for office visits. Similarly, office visits was the only service where CDHP enrollment resulted in a significantly larger share of claims from low-cost providers and greater savings from price shopping relative to traditional plans. There was also no evidence that, within CDHP plans, consumers with lower expected medical expenses exhibited more price shopping or that consumers exhibited more price shopping before reaching the deductible.
{"title":"Price Shopping in Consumer-Directed Health Plans","authors":"N. Sood, Z. Wagner, P. Huckfeldt, A. Haviland","doi":"10.1515/fhep-2012-0028","DOIUrl":"https://doi.org/10.1515/fhep-2012-0028","url":null,"abstract":"Abstract We use health insurance claims data from 63 large employers to estimate the extent of price shopping for nine common outpatient services in consumer-directed health plans (CDHPs) compared to traditional health plans. The main measures of price shopping include (1) the total price paid on the claim, (2) the share of claims from low- and high-cost providers, and (3) the savings from price shopping relative to choosing prices randomly. All analyses control for individual and zip code level demographics and plan characteristics. We also estimate differences in price shopping within CDHPs depending on expected health care costs and whether the service was bought before or after reaching the deductible. For eight out of nine services analyzed, prices paid by CDHP and traditional plan enrollees did not differ significantly; CDHP enrollees paid 2.3% less for office visits. Similarly, office visits was the only service where CDHP enrollment resulted in a significantly larger share of claims from low-cost providers and greater savings from price shopping relative to traditional plans. There was also no evidence that, within CDHP plans, consumers with lower expected medical expenses exhibited more price shopping or that consumers exhibited more price shopping before reaching the deductible.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75398606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We analyze the problem of incentivizing research and development (R&D) into developing world disease from an economic efficiency perspective. We view the problem as how to best promote R&D into goods with positive external effects in the sense that medicines that directly affect the health of the poor also indirectly affect the utility of the altruistic “rich.” We demonstrate why existing policy proposals – such as price concessions by manufacturers – adversely impact the poor by placing the burden of R&D only on innovators rather than all altruists in the rich world. We offer policy solutions that are based on economic efficiency and therefore rely on a broad sense of how the world values the treatment of developing world disease. We estimate that global altruism toward those with malaria is, at a minimum, valued between $835 million and $2.4 billion annually and for HIV/AIDS, between $9.1 billion and $26.6 billion annually. We argue that future policies toward neglected diseases need to better incorporate how efficient R&D meets the need of this global altruism.
{"title":"Should Global Health be Tailored Toward the Rich? Altruism and Efficient R&D for Neglected Diseases","authors":"A. Jena, Stéphane Mechoulan, T. Philipson","doi":"10.1515/fhep-2012-0036","DOIUrl":"https://doi.org/10.1515/fhep-2012-0036","url":null,"abstract":"Abstract We analyze the problem of incentivizing research and development (R&D) into developing world disease from an economic efficiency perspective. We view the problem as how to best promote R&D into goods with positive external effects in the sense that medicines that directly affect the health of the poor also indirectly affect the utility of the altruistic “rich.” We demonstrate why existing policy proposals – such as price concessions by manufacturers – adversely impact the poor by placing the burden of R&D only on innovators rather than all altruists in the rich world. We offer policy solutions that are based on economic efficiency and therefore rely on a broad sense of how the world values the treatment of developing world disease. We estimate that global altruism toward those with malaria is, at a minimum, valued between $835 million and $2.4 billion annually and for HIV/AIDS, between $9.1 billion and $26.6 billion annually. We argue that future policies toward neglected diseases need to better incorporate how efficient R&D meets the need of this global altruism.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83721575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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