Obesity accounts for medical costs and lost productivity totaling more than $100 billion per year. Two important economic factors have been forwarded to explain obesity trends. The first is that healthy, palatable foods are no longer affordable to lower-income consumers. Recent decades have seen a trend toward lower consumption of nutrient dense fruits and vegetables and increasing consumption of less expensive energy-dense foods such as added sugars, fats, and refined grains among lower socioeconomic status households. The second is that lower socioeconomic status is associated with less investment in future well-being through healthy behaviors such as good nutrition and regular exercise. Both the availability of inexpensive, convenient, high-calorie foods and the lack of a desire to eat a healthy diet may explain obesity trends. However, they imply different means of reversing these trends.Taxes and subsidies are economic policy instruments that can induce healthier diets. Advances in food production have reduced the calorie costs of sweeteners and fats well below the costs of fruits, vegetables and proteins. Aligning diets with USDA recommendations would require significant market intervention. In order to improve diet quality, policymakers need to focus on the aggregate supply of healthy and unhealthy foods. Investing in better nutrition information only shifts the supply of healthier foods toward higher-income, health conscious consumers. The most promising food consumption policy interventions focus on providing incentives to increase production of healthier foods, and modifying choice architecture to improve diet quality among myopic consumers who are less likely to select a healthy diet.
{"title":"Low-Cost Obesity Interventions: The Market for Foods","authors":"Michael S. Finke, Sandra J. Huston","doi":"10.2139/ssrn.2899886","DOIUrl":"https://doi.org/10.2139/ssrn.2899886","url":null,"abstract":"Obesity accounts for medical costs and lost productivity totaling more than $100 billion per year. Two important economic factors have been forwarded to explain obesity trends. The first is that healthy, palatable foods are no longer affordable to lower-income consumers. Recent decades have seen a trend toward lower consumption of nutrient dense fruits and vegetables and increasing consumption of less expensive energy-dense foods such as added sugars, fats, and refined grains among lower socioeconomic status households. The second is that lower socioeconomic status is associated with less investment in future well-being through healthy behaviors such as good nutrition and regular exercise. Both the availability of inexpensive, convenient, high-calorie foods and the lack of a desire to eat a healthy diet may explain obesity trends. However, they imply different means of reversing these trends.Taxes and subsidies are economic policy instruments that can induce healthier diets. Advances in food production have reduced the calorie costs of sweeteners and fats well below the costs of fruits, vegetables and proteins. Aligning diets with USDA recommendations would require significant market intervention. In order to improve diet quality, policymakers need to focus on the aggregate supply of healthy and unhealthy foods. Investing in better nutrition information only shifts the supply of healthier foods toward higher-income, health conscious consumers. The most promising food consumption policy interventions focus on providing incentives to increase production of healthier foods, and modifying choice architecture to improve diet quality among myopic consumers who are less likely to select a healthy diet.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121996665","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}
When one faces competing claims of varying strength on public resources for health, which claims count? This paper proposes the following answer. One should count, or aggregate, a person’s claim just in case one could sympathize with her desire to prioritize her own claim over the strongest competing claim. It argues that this principle yields appealing case judgments and has a plausible grounding in both sympathetic identification with each person, taken separately, and respect for the person for whom most is at stake. It also defends this principle against several heretofore unanswered objections, including those raised by Daniel Hausman in Valuing Health.
{"title":"Why One Should Count only Claims with which One Can Sympathize","authors":"Alex Voorhoeve","doi":"10.1093/PHE/PHW006","DOIUrl":"https://doi.org/10.1093/PHE/PHW006","url":null,"abstract":"When one faces competing claims of varying strength on public resources for health, which claims count? This paper proposes the following answer. One should count, or aggregate, a person’s claim just in case one could sympathize with her desire to prioritize her own claim over the strongest competing claim. It argues that this principle yields appealing case judgments and has a plausible grounding in both sympathetic identification with each person, taken separately, and respect for the person for whom most is at stake. It also defends this principle against several heretofore unanswered objections, including those raised by Daniel Hausman in Valuing Health.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315880","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}
Drug overdoses involving opioid analgesics have increased dramatically since 1999, representing one of the United States' top public health crises. Opioids have legitimate medical functions, but they are often diverted, suggesting a tradeoff between improving medical access and nonmedical abuse. We provide causal estimates of the relationship between the medical opioid supply and drug overdoses using Medicare Part D as a differential shock to the geographic distribution of opioids. Our estimates imply that a 10% increase in opioid medical supply leads to a 7.1% increase in opioid-related deaths among the Medicare-ineligible population, suggesting substantial diversion from medical markets.
{"title":"How Increasing Medical Access to Opioids Contributes to the Opioid Epidemic: Evidence from Medicare Part D","authors":"David Powell, R. Pacula, E. Taylor","doi":"10.2139/ssrn.2851163","DOIUrl":"https://doi.org/10.2139/ssrn.2851163","url":null,"abstract":"Drug overdoses involving opioid analgesics have increased dramatically since 1999, representing one of the United States' top public health crises. Opioids have legitimate medical functions, but they are often diverted, suggesting a tradeoff between improving medical access and nonmedical abuse. We provide causal estimates of the relationship between the medical opioid supply and drug overdoses using Medicare Part D as a differential shock to the geographic distribution of opioids. Our estimates imply that a 10% increase in opioid medical supply leads to a 7.1% increase in opioid-related deaths among the Medicare-ineligible population, suggesting substantial diversion from medical markets.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134645453","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}
The 2014 mid-term election that put Republicans in control of Congress reduced any chance of federal legislative action to limit greenhouse gases such as carbon dioxide from electric power plants. However, the executive branch already has authority from the U.S. Supreme Court to limit emissions under the Clean Air Act. In June 2014, the Obama Administration issued its proposed Clean Power Plan, which sets a specific limit on emissions for each state and then allows each state to decide how to meet its target. Comments are invited on this plan, and President Obama can modify the executive order before it is issued in June of 2015. The U.S. Clean Power Plan uses a formula to determine the target for each state, expressed as a maximum emission rate per unit of electricity, but it provides states with exibility regarding how to meet that target. It even allows states to convert that emission rate target to an absolute quantity of emissions and then to sell permits for that many tons of carbon dioxide. Any state that chooses to comply with the federal mandate by selling permits can collect revenue for the state, and this revenue can be used for additional spending, to cut other taxes, or to reduce the projected budget deficit. Indeed, many states since the Great Recession are still facing major deficit projections. This federal mandate provides an opportunity for states like Illinois to address some significant budget problems. In Illinois, for example, projections of the deficit under current law increase from $1 billion in FY2014 to $14 billion in FY2025. Our purpose here is to calculate the fraction of several states’ projected future deficits that can be offset by collecting their own permit revenue.
{"title":"U.S. Clean Power Plan Provides Opportunity for Significant Cuts in State Budget Deficits","authors":"D. Fullerton, Daniel H. Karney","doi":"10.2139/ssrn.3883435","DOIUrl":"https://doi.org/10.2139/ssrn.3883435","url":null,"abstract":"The 2014 mid-term election that put Republicans in control of Congress reduced any chance of federal legislative action to limit greenhouse gases such as carbon dioxide from electric power plants. However, the executive branch already has authority from the U.S. Supreme Court to limit emissions under the Clean Air Act. In June 2014, the Obama Administration issued its proposed Clean Power Plan, which sets a specific limit on emissions for each state and then allows each state to decide how to meet its target. Comments are invited on this plan, and President Obama can modify the executive order before it is issued in June of 2015. The U.S. Clean Power Plan uses a formula to determine the target for each state, expressed as a maximum emission rate per unit of electricity, but it provides states with exibility regarding how to meet that target. It even allows states to convert that emission rate target to an absolute quantity of emissions and then to sell permits for that many tons of carbon dioxide. Any state that chooses to comply with the federal mandate by selling permits can collect revenue for the state, and this revenue can be used for additional spending, to cut other taxes, or to reduce the projected budget deficit. Indeed, many states since the Great Recession are still facing major deficit projections. This federal mandate provides an opportunity for states like Illinois to address some significant budget problems. In Illinois, for example, projections of the deficit under current law increase from $1 billion in FY2014 to $14 billion in FY2025. Our purpose here is to calculate the fraction of several states’ projected future deficits that can be offset by collecting their own permit revenue.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131990901","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}
West Virginia reports a high obesity rate, and the prevalence of obesity is 8 percent higher than the national rate. Obesity is linked with several health diseases, certain psychological disorders, quality of life, premature deaths as well as healthcare costs. Prevention of obesity is a must and changing behavioral factors is one way of controlling obesity. This research study attempts to examine the potential use of physical exercise and fewer calorie intakes in controlling obesity, and to estimate costs of obesity in West Virginia using Behavioral risk Factor Surveillance System data of 2001 and 2009. Three logit equations were used in the analysis. Results reveal that potential of using physical exercise in controlling obesity in West Virginia has increased from 2001 to 2009, though the desire of engaging in physical exercise of obese people has decreased. However, the willingness of taking fewer calories of obese individuals has increased significantly from 2001 to 2009. The cost estimations indicate that direct medical cost of obesity and total costs associated with obesity have increased by $51 million and $704 million respectively from 2001 to 2009.
{"title":"Obesity in West Virginia: Control and Costs","authors":"J. Herath, Cheryl Brown","doi":"10.2139/ssrn.2441703","DOIUrl":"https://doi.org/10.2139/ssrn.2441703","url":null,"abstract":"West Virginia reports a high obesity rate, and the prevalence of obesity is 8 percent higher than the national rate. Obesity is linked with several health diseases, certain psychological disorders, quality of life, premature deaths as well as healthcare costs. Prevention of obesity is a must and changing behavioral factors is one way of controlling obesity. This research study attempts to examine the potential use of physical exercise and fewer calorie intakes in controlling obesity, and to estimate costs of obesity in West Virginia using Behavioral risk Factor Surveillance System data of 2001 and 2009. Three logit equations were used in the analysis. Results reveal that potential of using physical exercise in controlling obesity in West Virginia has increased from 2001 to 2009, though the desire of engaging in physical exercise of obese people has decreased. However, the willingness of taking fewer calories of obese individuals has increased significantly from 2001 to 2009. The cost estimations indicate that direct medical cost of obesity and total costs associated with obesity have increased by $51 million and $704 million respectively from 2001 to 2009.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125833990","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}
This brief summarizes the using performance incentives to improve medical care productivity and health outcomes in Rwanda. The author nested a large-scale field experiment into the national rollout of the introduction of performance pay for medical care providers in Rwanda to study the effect of incentives for health care providers. In order to identify the effect of incentives separately from higher compensation, the author held constant compensation across treatment and comparison groups, a portion of the treatment group's compensation was based on performance whereas the compensation of the comparison group was fixed. The incentives led to a 20 percent increase in productivity, and significant improvements in child health. The author also fined evidence of a strong complementarity between performance incentives and baseline provider skill.
{"title":"Using Performance Incentives to Improve Medical Care Productivity and Health Outcomes","authors":"P. Gertler, C. Vermeersch","doi":"10.3386/W19046","DOIUrl":"https://doi.org/10.3386/W19046","url":null,"abstract":"This brief summarizes the using performance incentives to improve medical care productivity and health outcomes in Rwanda. The author nested a large-scale field experiment into the national rollout of the introduction of performance pay for medical care providers in Rwanda to study the effect of incentives for health care providers. In order to identify the effect of incentives separately from higher compensation, the author held constant compensation across treatment and comparison groups, a portion of the treatment group's compensation was based on performance whereas the compensation of the comparison group was fixed. The incentives led to a 20 percent increase in productivity, and significant improvements in child health. The author also fined evidence of a strong complementarity between performance incentives and baseline provider skill.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116005697","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}
This note's aim is to investigate the sensitivity of Christakis and Fowler's claim [Christakis, N., Fowler, J., 2007. The spread of obesity in a large social network over 32 years. The New England Journal of Medicine 357, 370-379] that obesity has spread through social networks. It is well known in the economics literature that failure to include contextual effects can lead to spurious inference on "social network effects." We replicate the NEJM results using their specification and a complementary dataset. We find that point estimates of the "social network effect" are reduced and become statistically indistinguishable from zero once standard econometric techniques are implemented. We further note the presence of estimation bias resulting from use of an incorrectly specified dynamic model.
{"title":"Is Obesity Contagious? Social Networks vs. Environmental Factors in the Obesity Epidemic","authors":"Ethan Cohen-Cole, Jason M. Fletcher","doi":"10.2139/ssrn.1098321","DOIUrl":"https://doi.org/10.2139/ssrn.1098321","url":null,"abstract":"This note's aim is to investigate the sensitivity of Christakis and Fowler's claim [Christakis, N., Fowler, J., 2007. The spread of obesity in a large social network over 32 years. The New England Journal of Medicine 357, 370-379] that obesity has spread through social networks. It is well known in the economics literature that failure to include contextual effects can lead to spurious inference on \"social network effects.\" We replicate the NEJM results using their specification and a complementary dataset. We find that point estimates of the \"social network effect\" are reduced and become statistically indistinguishable from zero once standard econometric techniques are implemented. We further note the presence of estimation bias resulting from use of an incorrectly specified dynamic model.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123048760","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}
Public healthcare serves people. It is important to understand how effective these services are. This study aims to provide a methodology on how to evaluate the hospital’s effectiveness of a specific service. The studied service is patient discharge from a hospital ward. More specifically, we have focused on patient discharge time. We calculated patient discharge time as a difference in time between two observed timestamps: firstly, the time when the patient was told to go home, and secondly, the time when the patient had discharge documentation and medication handed in his/her possession. We collected data from direct observations about patient discharge at ward and also from the hospital information system. Findings of our preliminary analyses show that patient discharge time can be improved by organizational change and new electronic prescribing system.
{"title":"Evaluation of Patients’ Discharge Time on a Ward: Preliminary Findings","authors":"M. Trkman, Ziva Stepancic, R. Malkoč","doi":"10.2139/ssrn.3629151","DOIUrl":"https://doi.org/10.2139/ssrn.3629151","url":null,"abstract":"Public healthcare serves people. It is important to understand how effective these services are. This study aims to provide a methodology on how to evaluate the hospital’s effectiveness of a specific service. The studied service is patient discharge from a hospital ward. More specifically, we have focused on patient discharge time. We calculated patient discharge time as a difference in time between two observed timestamps: firstly, the time when the patient was told to go home, and secondly, the time when the patient had discharge documentation and medication handed in his/her possession. We collected data from direct observations about patient discharge at ward and also from the hospital information system. Findings of our preliminary analyses show that patient discharge time can be improved by organizational change and new electronic prescribing system.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"60 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126561213","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}
Background: Non communicable diseases (NCD) are a rising problem worldwide and is a cause of public health concern in India too. These diseases result in high health care demand and are expensive especially in low resource settings. We studied the NCD burden estimates for India and its major states and the associated cost. Methods: The disease burden was estimated using DALYs (Disability Adjusted life years), the Years of Life lost (YLL) and Years lived in disability (YLD). National Sample Survey was used to calculate OOPE and CHE. Results: NCDs account for 16939 DALYs per 100,000 in India. About 50-70% of patients took treatment in private facilities. There is huge variation in the utilization of health facilities, disease burden and cost of treatment across the states. Karnataka had highest DALY rate of 25790 DALYs per 100,000 but Chandigarh showed highest mean OOPE of INR 63952 and Arunachal Pradesh showed the highest CHE of 47.7 percent. Conclusions: The economic burden of NCDs is notably high both in terms of OOPE and CHE and indicate variation across states in both diseases burden and economic burden. The findings highlight the need for improvement of NCD management programmes in socioeconomically lower states of India. Funding Information: No funds received for this research. Declaration of Interests: None declared.
{"title":"Burden of Non-Communicable Diseases and Its Associated Economic Costs in India","authors":"G. Menon, Jeetendra Yadav, D. John","doi":"10.2139/ssrn.3899118","DOIUrl":"https://doi.org/10.2139/ssrn.3899118","url":null,"abstract":"Background: Non communicable diseases (NCD) are a rising problem worldwide and is a cause of public health concern in India too. These diseases result in high health care demand and are expensive especially in low resource settings. We studied the NCD burden estimates for India and its major states and the associated cost. Methods: The disease burden was estimated using DALYs (Disability Adjusted life years), the Years of Life lost (YLL) and Years lived in disability (YLD). National Sample Survey was used to calculate OOPE and CHE. Results: NCDs account for 16939 DALYs per 100,000 in India. About 50-70% of patients took treatment in private facilities. There is huge variation in the utilization of health facilities, disease burden and cost of treatment across the states. Karnataka had highest DALY rate of 25790 DALYs per 100,000 but Chandigarh showed highest mean OOPE of INR 63952 and Arunachal Pradesh showed the highest CHE of 47.7 percent. Conclusions: The economic burden of NCDs is notably high both in terms of OOPE and CHE and indicate variation across states in both diseases burden and economic burden. The findings highlight the need for improvement of NCD management programmes in socioeconomically lower states of India. Funding Information: No funds received for this research. Declaration of Interests: None declared.","PeriodicalId":137980,"journal":{"name":"Public Health eJournal","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114963599","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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