This paper focuses on risk information campaigns familiarizing consumers with hazardous product attributes and compares policies advocating voluntary and mandatory displays of warning messages. The food allergen labeling campaign provides an opportunity to focus on the availability and visibility of a warning and to evaluate the immediate effect of different disclosure rules. Using a multivariate two-step framework, I compare food allergies to a set of control diseases. I find that voluntary allergen labeling boosts the number of outpatient allergy visits, while mandatory labeling has the opposite effect. This result demonstrates that the disclosure of product risk characteristics might adversely affect consumers' health if the disclosure policy is not chosen carefully.
{"title":"Is This Salad Safe? Voluntary versus Mandatory Disclosure of Product Risk Characteristics","authors":"M. Aslam","doi":"10.2139/ssrn.2495134","DOIUrl":"https://doi.org/10.2139/ssrn.2495134","url":null,"abstract":"This paper focuses on risk information campaigns familiarizing consumers with hazardous product attributes and compares policies advocating voluntary and mandatory displays of warning messages. The food allergen labeling campaign provides an opportunity to focus on the availability and visibility of a warning and to evaluate the immediate effect of different disclosure rules. Using a multivariate two-step framework, I compare food allergies to a set of control diseases. I find that voluntary allergen labeling boosts the number of outpatient allergy visits, while mandatory labeling has the opposite effect. This result demonstrates that the disclosure of product risk characteristics might adversely affect consumers' health if the disclosure policy is not chosen carefully.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78028085","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}
Qiang Liu, Sachin Gupta, S. Venkataraman, Hongju Liu
The practice of detailing in the marketing of prescription drugs is undergoing significant changes. For instance, in September 2013, the Physician Payment Sunshine Act went into full effect. The accompanying transparency requirements have prompted physician practices and hospitals to severely restrict pharmaceutical sales representatives’ direct access to their physicians. Despite all the attention in the popular press, scant scholarly research has investigated how these restrictions on physician access impact physician prescription behavior and competitive detailing to physicians. To analyze the impact of these restrictions, we develop a structural model of how pharmaceutical firms compete dynamically to schedule detailing to physicians. Detailing activities are known to have significant carryover effects that are captured in a first-stage model of physicians’ demand for prescription drugs. We also specify detailing policy functions that describe each firm’s observed detailing actions. In a second stage, we estimate a model that describes costs of detailing, assuming that the observed detailing levels are consistent with a Markov perfect Nash equilibrium. The estimated structural model is used to examine the implications of restrictions on the amount of detailing via counterfactual simulations. We find that restriction policies would increase the market share of a nondrug-treatment-only option but impact firms asymmetrically; firms that are strong in detailing and/or rely more on detailing would be hurt more. Unexpectedly, a policy that imposes a ceiling on detailing frequency could significantly reduce detailing of all firms in the market, including those firms with average detailing levels below the ceiling, and effectively would soften competition between firms and enhance their profits. This paper was accepted by J. Miguel Villas-Boas, marketing .
在处方药的销售中，细化的做法正在发生重大变化。例如，2013年9月，《医生薪酬阳光法案》(Physician Payment Sunshine Act)全面生效。随之而来的透明度要求促使医生执业和医院严格限制药品销售代表直接接触他们的医生。尽管大众媒体都很关注，但很少有学术研究调查了这些对医生访问的限制如何影响医生的处方行为和对医生的竞争细节。为了分析这些限制的影响，我们开发了一个制药公司如何动态竞争的结构模型，以安排详细的医生。在医生对处方药需求的第一阶段模型中，已知详细活动具有显著的结转效应。我们还详细说明了描述每个公司观察到的详细行动的详细政策功能。在第二阶段，我们估计一个描述细节成本的模型，假设观察到的细节水平与马尔可夫完美纳什均衡一致。估计的结构模型用于通过反事实模拟来检查对细节量的限制的影响。我们发现，限制政策会增加非药物治疗方案的市场份额，但对企业的影响是不对称的;注重细节和/或更依赖细节的公司将受到更大的伤害。出乎意料的是，对细节频率设置上限的政策可以显著减少市场上所有公司的细节，包括那些平均细节水平低于上限的公司，并有效地软化公司之间的竞争并提高它们的利润。这篇论文被市场营销学的J. Miguel Villas-Boas接受。
{"title":"An Empirical Model of Drug Detailing: Dynamic Competition and Policy Implications","authors":"Qiang Liu, Sachin Gupta, S. Venkataraman, Hongju Liu","doi":"10.2139/ssrn.2320153","DOIUrl":"https://doi.org/10.2139/ssrn.2320153","url":null,"abstract":"The practice of detailing in the marketing of prescription drugs is undergoing significant changes. For instance, in September 2013, the Physician Payment Sunshine Act went into full effect. The accompanying transparency requirements have prompted physician practices and hospitals to severely restrict pharmaceutical sales representatives’ direct access to their physicians. Despite all the attention in the popular press, scant scholarly research has investigated how these restrictions on physician access impact physician prescription behavior and competitive detailing to physicians. To analyze the impact of these restrictions, we develop a structural model of how pharmaceutical firms compete dynamically to schedule detailing to physicians. Detailing activities are known to have significant carryover effects that are captured in a first-stage model of physicians’ demand for prescription drugs. We also specify detailing policy functions that describe each firm’s observed detailing actions. In a second stage, we estimate a model that describes costs of detailing, assuming that the observed detailing levels are consistent with a Markov perfect Nash equilibrium. The estimated structural model is used to examine the implications of restrictions on the amount of detailing via counterfactual simulations. We find that restriction policies would increase the market share of a nondrug-treatment-only option but impact firms asymmetrically; firms that are strong in detailing and/or rely more on detailing would be hurt more. Unexpectedly, a policy that imposes a ceiling on detailing frequency could significantly reduce detailing of all firms in the market, including those firms with average detailing levels below the ceiling, and effectively would soften competition between firms and enhance their profits. This paper was accepted by J. Miguel Villas-Boas, marketing .","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"132 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88133673","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}
We assess the quantitative importance of reclassification risk in the US health insurance market. Reclassification risk arises because the health conditions of individuals evolve over time, while a typical health insurance contract only lasts for one year. Thus, a change in the health status can lead to a significant change in the health insurance premium. We measure welfare gains from introducing explicit insurance against this risk in the form of guaranteed renewable health insurance contracts. We find that in the current institutional environment individuals are well-sheltered against reclassification risk and they only moderately gain from having access to these contracts. More specifically, we show that employer-sponsored health insurance and public means-tested transfers play an important role in providing implicit insurance against reclassification risk. If these institutions are removed, the average welfare gains from having access to guaranteed renewable contracts exceed 4% of the annual consumption.
{"title":"Welfare Costs of Reclassification Risk in the Health Insurance Market","authors":"S. Pashchenko, Ponpoje Porapakkarm","doi":"10.2139/ssrn.1946152","DOIUrl":"https://doi.org/10.2139/ssrn.1946152","url":null,"abstract":"We assess the quantitative importance of reclassification risk in the US health insurance market. Reclassification risk arises because the health conditions of individuals evolve over time, while a typical health insurance contract only lasts for one year. Thus, a change in the health status can lead to a significant change in the health insurance premium. We measure welfare gains from introducing explicit insurance against this risk in the form of guaranteed renewable health insurance contracts. We find that in the current institutional environment individuals are well-sheltered against reclassification risk and they only moderately gain from having access to these contracts. More specifically, we show that employer-sponsored health insurance and public means-tested transfers play an important role in providing implicit insurance against reclassification risk. If these institutions are removed, the average welfare gains from having access to guaranteed renewable contracts exceed 4% of the annual consumption.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"148 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88659432","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 objective of this study was to examine where nutritional gatekeepers obtain information about new foods, and whether information source is associated with Body Mass Index (BMI), as well as whether any association varied according to how often the participant cooked from scratch. A national panel survey of 501 females aged 20-35 assessed how participants obtained information on new recipes, and asked a series of questions about their cooking habits, their weight and height. Linear regressions were run to determine associations between information source, cooking from scratch, and BMI. Obtaining information from cooking shows was positively correlated with BMI (p<0.05), as was obtaining information from social media (p<0.05), whereas obtaining information from other print, online, or in-person sources was not significantly associated with BMI. A significant interaction between watching cooking shows and cooking from scratch indicated that cooking from scratch, as well as watching cooking shows was associated with higher BMI (p<0.05). Obtaining information about new foods from television cooking shows or social media – versus other sources – appears to have a unique relationship with BMI. Furthermore, watching cooking shows may have a differential effect on BMI for those who are merely TV “viewers,�? versus those who are “doers.�? Promoting healthy foods on cooking shows may be one way to positively influence the weight status of “doers�? as well as “viewers.�?
{"title":"Viewers vs. Doers: The Relationship between Watching Food Television and BMI","authors":"Lizzy Pope, L. Latimer, B. Wansink","doi":"10.2139/ssrn.2575823","DOIUrl":"https://doi.org/10.2139/ssrn.2575823","url":null,"abstract":"The objective of this study was to examine where nutritional gatekeepers obtain information about new foods, and whether information source is associated with Body Mass Index (BMI), as well as whether any association varied according to how often the participant cooked from scratch. A national panel survey of 501 females aged 20-35 assessed how participants obtained information on new recipes, and asked a series of questions about their cooking habits, their weight and height. Linear regressions were run to determine associations between information source, cooking from scratch, and BMI. Obtaining information from cooking shows was positively correlated with BMI (p<0.05), as was obtaining information from social media (p<0.05), whereas obtaining information from other print, online, or in-person sources was not significantly associated with BMI. A significant interaction between watching cooking shows and cooking from scratch indicated that cooking from scratch, as well as watching cooking shows was associated with higher BMI (p<0.05). Obtaining information about new foods from television cooking shows or social media – versus other sources – appears to have a unique relationship with BMI. Furthermore, watching cooking shows may have a differential effect on BMI for those who are merely TV “viewers,�? versus those who are “doers.�? Promoting healthy foods on cooking shows may be one way to positively influence the weight status of “doers�? as well as “viewers.�?","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81853686","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 planning of the operating rooms (ORs) is a difficult process due to the different stakeholders involved. The real complexity, however, results from various sources of variability entering the processes. These uncertain processes cannot be ignored since they greatly influence the trade-offs between the hospital costs and the patient waiting times. As a result, a need for policies guiding the OR manager in handling the trade-offs arises. Therefore, researchers have investigated different possibilities to incorporate non-elective patients in the schedule with the goal of maximizing both patient- and hospital-related measures. This paper reviews the literature on OR planning where both elective and non-elective patient categories are involved. It shows the various policies, the differences and similarities in the research settings and the resulting outcomes, whether they are beneficial or not. We find that the dedicated and the flexible policy are mostly pursued, but the setting and the assumptions of the reviewed papers vary widely. Decisions on both operational policies as well as on capacity are required to assure timely access and efficiency, which are the two main drivers for the problem at hand. Furthermore, the policy choice impacts the number of schedule disruptions and the OR utilization. However, results on the overtime and the patient waiting time are partly contradicting. The review shows that some policies have already received considerable attention, but the question of which policies are most appropriate is not yet fully answered. Neither has the full spectrum of policies been explored yet. Consequently, this topic provides several areas for future research, which are outlined throughout the paper.
{"title":"Trade-Offs in Operating Room Planning for Electives and Emergencies: A Review","authors":"C. Van Riet, E. Demeulemeester","doi":"10.2139/ssrn.2553849","DOIUrl":"https://doi.org/10.2139/ssrn.2553849","url":null,"abstract":"The planning of the operating rooms (ORs) is a difficult process due to the different stakeholders involved. The real complexity, however, results from various sources of variability entering the processes. These uncertain processes cannot be ignored since they greatly influence the trade-offs between the hospital costs and the patient waiting times. As a result, a need for policies guiding the OR manager in handling the trade-offs arises. Therefore, researchers have investigated different possibilities to incorporate non-elective patients in the schedule with the goal of maximizing both patient- and hospital-related measures. This paper reviews the literature on OR planning where both elective and non-elective patient categories are involved. It shows the various policies, the differences and similarities in the research settings and the resulting outcomes, whether they are beneficial or not. We find that the dedicated and the flexible policy are mostly pursued, but the setting and the assumptions of the reviewed papers vary widely. Decisions on both operational policies as well as on capacity are required to assure timely access and efficiency, which are the two main drivers for the problem at hand. Furthermore, the policy choice impacts the number of schedule disruptions and the OR utilization. However, results on the overtime and the patient waiting time are partly contradicting. The review shows that some policies have already received considerable attention, but the question of which policies are most appropriate is not yet fully answered. Neither has the full spectrum of policies been explored yet. Consequently, this topic provides several areas for future research, which are outlined throughout the paper.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83614877","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 study examines the effect of a dental-care reform for children in Israel on the use of dental care and intra-household allocation of dental treatments. Using seven-year administrative panel data on patients’ dental treatments at a large clinic in Jerusalem that serves one of the target populations of the reform (ultra-Orthodox Jews, a population typically characterized by low income and education), the treatment patterns of reform-eligible children and ineligible household members are analyzed, using a difference-in-differences empirical strategy. The reform is found to have increased access to dental care substantially, particularly in terms of preventive treatments, and therefore enhanced oral health. It also created a substantial spillover effect for households that have eligible children, as evidenced by a change in the treatment mix and the intensity of recourse to treatments by ineligible household members.
{"title":"The Effect of Subsidizing Dental Care Costs on Demand and Intra-Household Allocation of Dental Care","authors":"Elior Cohen","doi":"10.2139/ssrn.3513629","DOIUrl":"https://doi.org/10.2139/ssrn.3513629","url":null,"abstract":"The study examines the effect of a dental-care reform for children in Israel on the use of dental care and intra-household allocation of dental treatments. Using seven-year administrative panel data on patients’ dental treatments at a large clinic in Jerusalem that serves one of the target populations of the reform (ultra-Orthodox Jews, a population typically characterized by low income and education), the treatment patterns of reform-eligible children and ineligible household members are analyzed, using a difference-in-differences empirical strategy. The reform is found to have increased access to dental care substantially, particularly in terms of preventive treatments, and therefore enhanced oral health. It also created a substantial spillover effect for households that have eligible children, as evidenced by a change in the treatment mix and the intensity of recourse to treatments by ineligible household members.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77524699","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}
Since the introduction of the DRG system in 2004, the German hospital market experienced a stream of consolidations in terms of mergers and acquisitions, resulting in a decreasing number of hospital owners. In this study, I examine the ex-ante characteristics of hospitals prior to a merger or an acquisition occurring between 2005 and 2010 in Germany, predominantly focusing on the financial conditions of hospitals. The results reveal that hospitals with a higher probability of default and less liquid resources are more often the targets of acquisitions. On the other hand, hospitals with a lower equity-to-assets ratio exhibit a higher probability of merger. This pattern can be explained by different motives and rationales of hospital chains and potential investors.
{"title":"Mergers and Acquisitions in the German Hospital Market – Who are the Targets?","authors":"Adam Pilny","doi":"10.2139/ssrn.2565703","DOIUrl":"https://doi.org/10.2139/ssrn.2565703","url":null,"abstract":"Since the introduction of the DRG system in 2004, the German hospital market experienced a stream of consolidations in terms of mergers and acquisitions, resulting in a decreasing number of hospital owners. In this study, I examine the ex-ante characteristics of hospitals prior to a merger or an acquisition occurring between 2005 and 2010 in Germany, predominantly focusing on the financial conditions of hospitals. The results reveal that hospitals with a higher probability of default and less liquid resources are more often the targets of acquisitions. On the other hand, hospitals with a lower equity-to-assets ratio exhibit a higher probability of merger. This pattern can be explained by different motives and rationales of hospital chains and potential investors.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82105331","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}
Alex Rosenblat, Kate Wikelius, D. Boyd, S. Gangadharan, Corrine M. Yu
Employees and prospective employees produce more data than ever - in the workplace, on social media, and beyond. Employers and the third party companies that assist them increasingly apply analytical tools to these various data streams to measure factors that influence employee performance, attrition rates, and workplace profitability. While some of the data - such as past performance - are unquestionably relevant to such analysis, other data that produces strong correlations to performance are more surprising. For instance, Evolv, a recruiting software company, analyzed 3 million data points about 30,000 hourly employees and identified that those who installed newer browsers, like Chrome or Firefox, onto their computers stay at their jobs 15% longer than those who use default browsers that come pre-installed on their computers, like Safari for Macs. Job candidates may rightly worry that they will be excluded from or included in job opportunities based on data that seem arbitrary and are outside their field of vision. For example, a job candidate’s resume could be excluded from a talent pool because of her online browsing habits, but she is unlikely to find that out directly. The complexity of hiring algorithms which fold all kinds of data into scoring systems make it difficult to detect and therefore challenge hiring decisions, even when outputs appear to disadvantage particular groups within a protected class. When hiring algorithms weigh many factors to reach an unexplained decision, job applicants and outside observers are unable to detect and challenge factors that may have a disparate impact on protected groups.
{"title":"Data & Civil Rights: Employment Primer","authors":"Alex Rosenblat, Kate Wikelius, D. Boyd, S. Gangadharan, Corrine M. Yu","doi":"10.2139/ssrn.2541512","DOIUrl":"https://doi.org/10.2139/ssrn.2541512","url":null,"abstract":"Employees and prospective employees produce more data than ever - in the workplace, on social media, and beyond. Employers and the third party companies that assist them increasingly apply analytical tools to these various data streams to measure factors that influence employee performance, attrition rates, and workplace profitability. While some of the data - such as past performance - are unquestionably relevant to such analysis, other data that produces strong correlations to performance are more surprising. For instance, Evolv, a recruiting software company, analyzed 3 million data points about 30,000 hourly employees and identified that those who installed newer browsers, like Chrome or Firefox, onto their computers stay at their jobs 15% longer than those who use default browsers that come pre-installed on their computers, like Safari for Macs. Job candidates may rightly worry that they will be excluded from or included in job opportunities based on data that seem arbitrary and are outside their field of vision. For example, a job candidate’s resume could be excluded from a talent pool because of her online browsing habits, but she is unlikely to find that out directly. The complexity of hiring algorithms which fold all kinds of data into scoring systems make it difficult to detect and therefore challenge hiring decisions, even when outputs appear to disadvantage particular groups within a protected class. When hiring algorithms weigh many factors to reach an unexplained decision, job applicants and outside observers are unable to detect and challenge factors that may have a disparate impact on protected groups.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86950179","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}
P. Shi, J. Dai, Ding Ding, Soo Keng Ang, M. Chou, Xin Jin, Joe Sim
This document presents a comprehensive empirical study on the inpatient flow management in a Singaporean hospital. The empirical study uses high resolution patient flow data from 2008 to 2010. This document details the statistics of waiting times for patients admitted from the emergence department (ED) to inpatient wards, bed occupancy rate of the inpatient wards, and overflow proportions (proportions of patients that are admitted to a non-primary ward). The document also reports various statistics related to patient arrival, inpatient discharge, length of stay (LOS), and pre- and post-allocation delays incurred during the bed-assignment process.
{"title":"Patient Flow from Emergency Department to Inpatient Wards: Empirical Observations from a Singaporean Hospital","authors":"P. Shi, J. Dai, Ding Ding, Soo Keng Ang, M. Chou, Xin Jin, Joe Sim","doi":"10.2139/ssrn.2517050","DOIUrl":"https://doi.org/10.2139/ssrn.2517050","url":null,"abstract":"This document presents a comprehensive empirical study on the inpatient flow management in a Singaporean hospital. The empirical study uses high resolution patient flow data from 2008 to 2010. This document details the statistics of waiting times for patients admitted from the emergence department (ED) to inpatient wards, bed occupancy rate of the inpatient wards, and overflow proportions (proportions of patients that are admitted to a non-primary ward). The document also reports various statistics related to patient arrival, inpatient discharge, length of stay (LOS), and pre- and post-allocation delays incurred during the bed-assignment process.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74653369","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}
During the past twenty years, there has been a rapid growth in life expectancy and an increased attention on funding for old age. Attempts to forecast improving life expectancy have been boosted by the development of stochastic mortality modeling, for example the Cairns–Blake–Dowd (CBD) 2006 model. The most common optimization method for these models is maximum likelihood estimation (MLE) which relies on the assumption that the number of deaths follows a Poisson distribution. However, several recent studies have found that the true underlying distribution of death data is overdispersed in nature (see Cairns et al. 2009 and Dowd et al. 2010). Semiparametric models have been applied to many areas in economics but there are very few applications of such models in mortality modeling. In this paper we propose a local linear panel fitting methodology to the CBD model which would free the Poisson assumption on number of deaths. The parameters in the CBD model will be considered as smooth functions of time instead of being treated as a bivariate random walk with drift process in the current literature. Using the mortality data of several developed countries, we find that the proposed estimation methods provide comparable fitting results with the MLE method but without the need of additional assumptions on number of deaths. Further, the 5-year-ahead forecasting results show that our method significantly improves the accuracy of the forecast.
在过去的二十年里，人们的预期寿命迅速增长，人们越来越关注为老年人提供资金。由于随机死亡率模型的发展，例如凯恩斯-布莱克-多德(CBD) 2006模型，预测预期寿命改善的尝试得到了推动。这些模型最常用的优化方法是最大似然估计(MLE)，它依赖于死亡人数服从泊松分布的假设。然而，最近的几项研究发现，死亡数据的真正潜在分布在本质上是过度分散的(见Cairns et al. 2009和Dowd et al. 2010)。半参数模型已应用于经济学的许多领域，但在死亡率建模中的应用很少。在本文中，我们提出了一种局部线性面板拟合方法，该方法可以消除对死亡人数的泊松假设。目前的文献将CBD模型中的参数视为时间的光滑函数，而不是将其视为具有漂移过程的二元随机游走。使用几个发达国家的死亡率数据，我们发现所提出的估计方法提供了与MLE方法相当的拟合结果，但不需要对死亡人数进行额外假设。此外，5年预测结果表明，该方法显著提高了预测精度。
{"title":"A Semiparametric Panel Approach to Mortality Modeling","authors":"Han Li, Colin O’Hare, Xibin Zhang","doi":"10.2139/ssrn.2512571","DOIUrl":"https://doi.org/10.2139/ssrn.2512571","url":null,"abstract":"During the past twenty years, there has been a rapid growth in life expectancy and an increased attention on funding for old age. Attempts to forecast improving life expectancy have been boosted by the development of stochastic mortality modeling, for example the Cairns–Blake–Dowd (CBD) 2006 model. The most common optimization method for these models is maximum likelihood estimation (MLE) which relies on the assumption that the number of deaths follows a Poisson distribution. However, several recent studies have found that the true underlying distribution of death data is overdispersed in nature (see Cairns et al. 2009 and Dowd et al. 2010). Semiparametric models have been applied to many areas in economics but there are very few applications of such models in mortality modeling. In this paper we propose a local linear panel fitting methodology to the CBD model which would free the Poisson assumption on number of deaths. The parameters in the CBD model will be considered as smooth functions of time instead of being treated as a bivariate random walk with drift process in the current literature. Using the mortality data of several developed countries, we find that the proposed estimation methods provide comparable fitting results with the MLE method but without the need of additional assumptions on number of deaths. Further, the 5-year-ahead forecasting results show that our method significantly improves the accuracy of the forecast.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88169575","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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