Quality indicators are classified into system or clinical quality indicators. Typically, different levels of an organization steer each of the two types of indicators. Decentralized levels control clinical indicators (blood pressure, blood sugar etc.) while centralized levels control system indicators (waiting time, electronic health records etc.). In this paper we examine optimal pay-for-performance schemes for the two indicators by considering a model consisting of hierarchy of principal-agent interactions where pay-for-performance rewards are distributed to the centralized level (unit of accountability). We find that the optimal pay-for-performance price depends on factors such as the degree and distribution of altruistic preferences, quality costs, the marginal cost of public funds, and the interdependence between the quality variables. The optimal price should differ for system and clinical indicators both when an internal incentive system is in place and when this is not the case. The optimal price for clinical indicators is to reflect the centralized levels’ ability to steer the decentralized level - the type of internal contract that exists between the two levels of the organization. The optimal price for system indicators is independent of the type of internal contract since such indicators are under the control of the unit of accountability. Finally, it is shown that rewarding organizations on the basis of clinical quality indicators can be optimal also when such incentives are not transmitted to the decentralized level of the organization. This conclusion is the result of the indirect effects that non-incentivized variables (system indicators) might have on the incentivized ones (clinical indicators).Published: Online May 2019.
{"title":"Pay-for-performance schemes: Should optimal prices vary across system and clinical quality indicators?","authors":"S. Grepperud","doi":"10.5617/NJHE.5932","DOIUrl":"https://doi.org/10.5617/NJHE.5932","url":null,"abstract":"Quality indicators are classified into system or clinical quality indicators. Typically, different levels of an organization steer each of the two types of indicators. Decentralized levels control clinical indicators (blood pressure, blood sugar etc.) while centralized levels control system indicators (waiting time, electronic health records etc.). In this paper we examine optimal pay-for-performance schemes for the two indicators by considering a model consisting of hierarchy of principal-agent interactions where pay-for-performance rewards are distributed to the centralized level (unit of accountability). We find that the optimal pay-for-performance price depends on factors such as the degree and distribution of altruistic preferences, quality costs, the marginal cost of public funds, and the interdependence between the quality variables. The optimal price should differ for system and clinical indicators both when an internal incentive system is in place and when this is not the case. The optimal price for clinical indicators is to reflect the centralized levels’ ability to steer the decentralized level - the type of internal contract that exists between the two levels of the organization. The optimal price for system indicators is independent of the type of internal contract since such indicators are under the control of the unit of accountability. Finally, it is shown that rewarding organizations on the basis of clinical quality indicators can be optimal also when such incentives are not transmitted to the decentralized level of the organization. This conclusion is the result of the indirect effects that non-incentivized variables (system indicators) might have on the incentivized ones (clinical indicators).Published: Online May 2019. ","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80893876","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 section consists of an overview (names, universities, thesis titles and abstracts) of new PhD:s within the field of health economics in the Nordic countries.
本节包括北欧国家卫生经济学领域新博士的概述(姓名、大学、论文题目和摘要)。
{"title":"Recent PhDs","authors":"Margareta Dackehag","doi":"10.5617/njhe.6744","DOIUrl":"https://doi.org/10.5617/njhe.6744","url":null,"abstract":"This section consists of an overview (names, universities, thesis titles and abstracts) of new PhD:s within the field of health economics in the Nordic countries.","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76007214","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":"The Nordic health care systems: Most similar comparative research?","authors":"K. M. Pedersen","doi":"10.5617/NJHE.6707","DOIUrl":"https://doi.org/10.5617/NJHE.6707","url":null,"abstract":"<jats:p>TBA</jats:p>","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78448732","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}
Previous studies on patients with acute myocardial infarction have found that Finland has higher hospital costs per patient than Norway for the first hospital episode (HEP), while Norway has higher costs during the first year after the initial admission. In this paper, we analyze the variation in treatment costs between Finland and Norway in detail by introducing novel explanatory variables. We find that the distance from the patient’s home to the hospital increases hospital costs at a declining scale and one-year hospital costs are higher for low-income patients. The higher one-year hospital costs in Norway are accompanied by a comparatively lower mortality rate. While for HEP, the introduction of new explanatory variables does not explain the greater costs in Finland compared with Norway, for one-year costs, the additional variables explain the greater one-year costs in Norway compared to Finland.Published: Online January 2019. In print January 2019.
{"title":"Comparative treatment costs for patients with acute myocardial infarction between Finland and Norway","authors":"T. Iversen, U. Häkkinen","doi":"10.5617/NJHE.5543","DOIUrl":"https://doi.org/10.5617/NJHE.5543","url":null,"abstract":"Previous studies on patients with acute myocardial infarction have found that Finland has higher hospital costs per patient than Norway for the first hospital episode (HEP), while Norway has higher costs during the first year after the initial admission. In this paper, we analyze the variation in treatment costs between Finland and Norway in detail by introducing novel explanatory variables. We find that the distance from the patient’s home to the hospital increases hospital costs at a declining scale and one-year hospital costs are higher for low-income patients. The higher one-year hospital costs in Norway are accompanied by a comparatively lower mortality rate. While for HEP, the introduction of new explanatory variables does not explain the greater costs in Finland compared with Norway, for one-year costs, the additional variables explain the greater one-year costs in Norway compared to Finland.Published: Online January 2019. In print January 2019.","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83724198","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":"Editorial: Nordic health system performance comparison","authors":"U. Häkkinen, T. Iversen, Åsa Ljungvall","doi":"10.5617/NJHE.6738","DOIUrl":"https://doi.org/10.5617/NJHE.6738","url":null,"abstract":"Published: January 2019.","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"52-54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78297455","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}
Finland and Norway have health care systems that have a varying degree of vertical integration. In Finland the financial responsibility for all patient treatment is placed at the municipal level, while in Norway the responsibility for patients is divided between the municipalities (primary and long-term care) and state-owned hospitals. From 2012, the Norwegian system became more vertically integrated following the introduction of the Coordination Reform. The aim of the paper is to analyse the associations between different modes of integration and performance indicators. The data included operated hip fracture patients from the years 2009–2014 residing in the cities of Oslo and Helsinki. Data from routinely collected national registers, also including data from primary health and long-term-care services, were linked. Performance indicators were compared at baseline (before the Coordination Reform, i.e., 2009–2011), and trends were described and analysed by difference-in-difference methods. The baseline study indicated that hip fracture patients in Oslo, compared with those in Helsinki, had longer stays in acute hospitals. They used less institutional care outside of hospitals as well as more GP services and fewer other outpatient services. Mortality was lower, and the probability of being discharged to home within 90 days from the index day was higher. After the Coordination Reform, the length of stay in hospital was shorter and the length of the first institutional episode in Oslo was longer than before the Reform, demonstrating that the shorter hospital stays were more than compensated for by longer stays in long-term-care institutions. The number of patients institutionalised 90 days from the index day increased and the number of patients discharged to home within 90 days from the index day decreased in Oslo after the Reform while the opposite trends were observed in Helsinki. After the Reform, the performance differences between the two regions had decreased. Published: Online December 2018. In print January 2019.
{"title":"Performance comparison of hip fracture pathways in two capital cities: Associations with level and change of integration","authors":"U. Häkkinen, T. Hagen, T. Moger","doi":"10.5617/NJHE.4836","DOIUrl":"https://doi.org/10.5617/NJHE.4836","url":null,"abstract":"Finland and Norway have health care systems that have a varying degree of vertical integration. In Finland the financial responsibility for all patient treatment is placed at the municipal level, while in Norway the responsibility for patients is divided between the municipalities (primary and long-term care) and state-owned hospitals. From 2012, the Norwegian system became more vertically integrated following the introduction of the Coordination Reform. The aim of the paper is to analyse the associations between different modes of integration and performance indicators. The data included operated hip fracture patients from the years 2009–2014 residing in the cities of Oslo and Helsinki. Data from routinely collected national registers, also including data from primary health and long-term-care services, were linked. Performance indicators were compared at baseline (before the Coordination Reform, i.e., 2009–2011), and trends were described and analysed by difference-in-difference methods. The baseline study indicated that hip fracture patients in Oslo, compared with those in Helsinki, had longer stays in acute hospitals. They used less institutional care outside of hospitals as well as more GP services and fewer other outpatient services. Mortality was lower, and the probability of being discharged to home within 90 days from the index day was higher. After the Coordination Reform, the length of stay in hospital was shorter and the length of the first institutional episode in Oslo was longer than before the Reform, demonstrating that the shorter hospital stays were more than compensated for by longer stays in long-term-care institutions. The number of patients institutionalised 90 days from the index day increased and the number of patients discharged to home within 90 days from the index day decreased in Oslo after the Reform while the opposite trends were observed in Helsinki. After the Reform, the performance differences between the two regions had decreased. Published: Online December 2018. In print January 2019. ","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82298328","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}
Mortality following hospital treatment in Finland and Norway is similar for major diseases, with acute coronary syndrome (ACS) as an important exception. For ACS, the mortality is significantly higher in Finland than in Norway. We study whether a decentralized structure with reduced emergency preparedness and small-scale production in Finland vs. a centralized structure with large percutaneous coronary intervention (PCI) departments performing acute services 24/7 in Norway explains the country differences in mortality. For patients discharged with acute myocardial infarction (International Classification of Diseases - ICD-10 I21 and I22) and unstable angina pectoris (ICD-10 I 20.0), data from the hospital discharge registers for 1 Jan. 2009–30 Nov. 2014 was linked with socio-demographic and regional variables, variables describing distances to hospitals, and with data from causes of death registers in Norway and Finland. Variables relating to hospital system and organization of care were included as independent variables in logistic regression analyses. Marginal mortality differences between the countries for different categories of the variables are presented separately for ST-segment elevation myocardial infarction (STEMI) and for other ACS patients. In Finland, 36% of STEMI patients and 25% of other ACS patients were admitted to hospitals having an emergency PCI service. The corresponding numbers for Norway were 77% and 66%. However, the percentage of patients receiving PCI within one day was similar (STEMI: Norway 54% vs. Finland 56%, p < 0.001), as was the distribution of PCIs performed during weekends (28% vs. 26%, p = 0.02). The short term mortality was a little lower in Norway for STEMI patients (30-day mortality: 10% vs. 12%, p < 0.001; 365-day mortality: 18% vs. 18%, p = 0.48), while markedly lower for other ACS (30-day mortality: 6% vs. 10%, p < 0.001; 365-day mortality: 14% vs. 20%, p < 0.001). After adjusting for individual and regional variables, the mortality was found to be 2–4% lower in Norway within most categories of the hospital system and organization of care variables in all analyses. As such, we were not able to explain the mortality differences by the hospital system and organization of care variables. Rather, the explanation seems to have other sources. Published: Online December 2018. In print January 2019.
芬兰和挪威的主要疾病住院治疗后死亡率相似，但急性冠状动脉综合征(ACS)是一个重要的例外。对于ACS，芬兰的死亡率明显高于挪威。我们研究芬兰的分散结构减少了应急准备和小规模生产，而挪威的集中结构有大量的经皮冠状动脉介入治疗(PCI)部门提供24/7的急性服务，这是否解释了各国死亡率的差异。对于因急性心肌梗死(国际疾病分类- icd - 10i21和I22)和不稳定型心绞痛(icd - 10i20.0)出院的患者，2009年1月1日至2014年11月30日医院出院登记的数据与社会人口统计学和区域变量、描述到医院距离的变量以及挪威和芬兰死亡原因登记的数据相关联。与医院系统和护理组织相关的变量作为自变量纳入logistic回归分析。不同类型变量在不同国家间的边际死亡率差异分别为st段抬高型心肌梗死(STEMI)和其他ACS患者。在芬兰，36%的STEMI患者和25%的其他ACS患者被送往有急诊PCI服务的医院。挪威的相应数字分别为77%和66%。然而，在一天内接受PCI的患者比例相似(STEMI:挪威54%对芬兰56%，p < 0.001)，周末进行PCI的患者分布也相似(28%对26%，p = 0.02)。挪威STEMI患者的短期死亡率略低(30天死亡率:10% vs. 12%， p < 0.001;365天死亡率:18%对18%，p = 0.48)，而其他ACS的死亡率明显更低(30天死亡率:6%对10%，p < 0.001;365天死亡率:14% vs. 20%， p < 0.001)。在对个体和区域变量进行调整后，在所有分析中，在医院系统和护理组织变量的大多数类别中，挪威的死亡率降低了2-4%。因此，我们无法解释医院系统和护理变量组织的死亡率差异。相反，这种解释似乎有其他来源。出版日期:2018年12月。2019年1月出版。
{"title":"Higher mortality among ACS patients in Finland than in Norway: Do differences in acute services and scale effects in hospital treatment explain the variation?","authors":"T. Moger, U. Häkkinen, T. Hagen","doi":"10.5617/NJHE.4834","DOIUrl":"https://doi.org/10.5617/NJHE.4834","url":null,"abstract":"Mortality following hospital treatment in Finland and Norway is similar for major diseases, with acute coronary syndrome (ACS) as an important exception. For ACS, the mortality is significantly higher in Finland than in Norway. We study whether a decentralized structure with reduced emergency preparedness and small-scale production in Finland vs. a centralized structure with large percutaneous coronary intervention (PCI) departments performing acute services 24/7 in Norway explains the country differences in mortality. For patients discharged with acute myocardial infarction (International Classification of Diseases - ICD-10 I21 and I22) and unstable angina pectoris (ICD-10 I 20.0), data from the hospital discharge registers for 1 Jan. 2009–30 Nov. 2014 was linked with socio-demographic and regional variables, variables describing distances to hospitals, and with data from causes of death registers in Norway and Finland. Variables relating to hospital system and organization of care were included as independent variables in logistic regression analyses. Marginal mortality differences between the countries for different categories of the variables are presented separately for ST-segment elevation myocardial infarction (STEMI) and for other ACS patients. In Finland, 36% of STEMI patients and 25% of other ACS patients were admitted to hospitals having an emergency PCI service. The corresponding numbers for Norway were 77% and 66%. However, the percentage of patients receiving PCI within one day was similar (STEMI: Norway 54% vs. Finland 56%, p < 0.001), as was the distribution of PCIs performed during weekends (28% vs. 26%, p = 0.02). The short term mortality was a little lower in Norway for STEMI patients (30-day mortality: 10% vs. 12%, p < 0.001; 365-day mortality: 18% vs. 18%, p = 0.48), while markedly lower for other ACS (30-day mortality: 6% vs. 10%, p < 0.001; 365-day mortality: 14% vs. 20%, p < 0.001). After adjusting for individual and regional variables, the mortality was found to be 2–4% lower in Norway within most categories of the hospital system and organization of care variables in all analyses. As such, we were not able to explain the mortality differences by the hospital system and organization of care variables. Rather, the explanation seems to have other sources. Published: Online December 2018. In print January 2019.","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74199292","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: Different sources of patient heterogeneity or personal characteristics may contribute to differential cost-effectiveness profiles of national screening programs for colorectal cancer (CRC). To motivate the use of subgroup analyses when individual level data are unavailable, we provide a stylized example of the potential economic value of capturing patient heterogeneity in CRC screening. We developed a Markov model to capture the impacts of patient heterogeneity on the cost-effectiveness of CRC screening involving once-only sigmoidoscopy compared to no screening. We simulated cohorts of Norwegian men, women, and six comorbidity subgroups that differentially influenced the relative treatment effect, the risks of developing CRC, dying from CRC, dying from background mortality or screening-related adverse events and baseline quality of life. We calculated the discounted (4%) incremental cost-effectiveness ratio (ICER), defined as the cost per quality-adjusted life year (QALY) gained, and the net monetary benefit (NMB) gained by stratification, from a societal perspective. Screening in men was cost-effective at any threshold value, while screening in women only provides good value for money from threshold values of €50,000 per QALY gained and above. Comorbidities unrelated to CRC development yielded generally less attractive cost-effectiveness ratios (i.e., increased the ICER), while related comorbidities improved the cost-effectiveness profiles of screening for CRC. A stratified policy that accounts for different screening outcomes between men and women could potentially improve the value of screening by €5.8 million annually. Accounting for patient heterogeneity in CRC screening will likely improve the value of screening strategies, as a single screening approach for the entire population can result in inefficient use of resources.Published: Online December 2018.
{"title":"Acknowledging patient heterogeneity in colorectal cancer screening: An example from Norway","authors":"Mathyn Vervaart, E. Burger, E. Aas","doi":"10.5617/NJHE.4881","DOIUrl":"https://doi.org/10.5617/NJHE.4881","url":null,"abstract":"Abstract: Different sources of patient heterogeneity or personal characteristics may contribute to differential cost-effectiveness profiles of national screening programs for colorectal cancer (CRC). To motivate the use of subgroup analyses when individual level data are unavailable, we provide a stylized example of the potential economic value of capturing patient heterogeneity in CRC screening. We developed a Markov model to capture the impacts of patient heterogeneity on the cost-effectiveness of CRC screening involving once-only sigmoidoscopy compared to no screening. We simulated cohorts of Norwegian men, women, and six comorbidity subgroups that differentially influenced the relative treatment effect, the risks of developing CRC, dying from CRC, dying from background mortality or screening-related adverse events and baseline quality of life. We calculated the discounted (4%) incremental cost-effectiveness ratio (ICER), defined as the cost per quality-adjusted life year (QALY) gained, and the net monetary benefit (NMB) gained by stratification, from a societal perspective. Screening in men was cost-effective at any threshold value, while screening in women only provides good value for money from threshold values of €50,000 per QALY gained and above. Comorbidities unrelated to CRC development yielded generally less attractive cost-effectiveness ratios (i.e., increased the ICER), while related comorbidities improved the cost-effectiveness profiles of screening for CRC. A stratified policy that accounts for different screening outcomes between men and women could potentially improve the value of screening by €5.8 million annually. Accounting for patient heterogeneity in CRC screening will likely improve the value of screening strategies, as a single screening approach for the entire population can result in inefficient use of resources.Published: Online December 2018.","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82581338","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}
M. Trombley, J. Bray, Jesse M. Hinde, O. Buxton, Ryan C. Johnson
A substantial literature has established that obesity is negatively associated with wages, particularly among females. However, prior research has found limited evidence for the factors hypothesized to underlie the obesity wage penalty. We add to the literature using data from IT workers at a U.S. Fortune 500 firm that provides us with direct measures of employee income and BMI, and health measures that are unavailable in national-level datasets. Our estimates indicate that the wage-obesity penalty among females only occurs among obese mothers, and is not attributable to differences in health or human capital that may be caused by having children. Published: Online November 2018.
{"title":"Investigating the Negative Relationship between Wages and Obesity: New Evidence from the Work, Family, and Health Network","authors":"M. Trombley, J. Bray, Jesse M. Hinde, O. Buxton, Ryan C. Johnson","doi":"10.5617/NJHE.4720","DOIUrl":"https://doi.org/10.5617/NJHE.4720","url":null,"abstract":"A substantial literature has established that obesity is negatively associated with wages, particularly among females. However, prior research has found limited evidence for the factors hypothesized to underlie the obesity wage penalty. We add to the literature using data from IT workers at a U.S. Fortune 500 firm that provides us with direct measures of employee income and BMI, and health measures that are unavailable in national-level datasets. Our estimates indicate that the wage-obesity penalty among females only occurs among obese mothers, and is not attributable to differences in health or human capital that may be caused by having children. Published: Online November 2018. ","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84405816","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}
S. Kittelsen, K. S. Anthun, U. Häkkinen, M. Kruse, C. Rehnberg
Empirical analysis of hospitals in production economics often find little or no evidence of scale economies and quite small optimal sizes. Medical literature on the other hand provides evidence of better results for hospitals with a large volume of similar procedures. Based on a sample of Nordic hospitals and patients, we have examined whether the inclusion of quality variables in the production models changes estimates of scale elasticity. A sample of 58 million patient records from 2008 and 2009 in 149 hospitals in Denmark, Finland, Norway and Sweden were collected. Patient data DRG-points were aggregated into 3 outputs (medical inpatients, surgical inpatients and outpatients) and linked to operating costs for 292 observations. The patient data were used to calculate quality indicators on emergency readmissions and mortality within 30 days, adjusted for age, gender, comorbidities, hospital transfers and DRG using DRG-specific logistic regressions.The hypothesis that the elasticity of scale increases when quality variables are included was tested against the null hypothesis of no change in the scale elasticity. The observations were used to estimate a cost function using Stochastic Frontier Analysis (SFA). Country dummies as well as dummies for University hospitals, capital city hospitals and the average travelling time for the patients were included as environmental variables. The estimated scale elasticities did not change with the inclusion of quality indicators in any of the tested models. This may be because medical volume effects are confined to few patient groups or possibly even offset by effects on other groups, where quality is reduced by volume. In one model, the scale elasticity was significantly larger than 1.0, a result that contradicts previous studies which have found decreasing returns. Published: Online October 2018. In print Janury 2019.
{"title":"Scale and quality in Nordic hospitals","authors":"S. Kittelsen, K. S. Anthun, U. Häkkinen, M. Kruse, C. Rehnberg","doi":"10.5617/NJHE.4801","DOIUrl":"https://doi.org/10.5617/NJHE.4801","url":null,"abstract":"Empirical analysis of hospitals in production economics often find little or no evidence of scale economies and quite small optimal sizes. Medical literature on the other hand provides evidence of better results for hospitals with a large volume of similar procedures. Based on a sample of Nordic hospitals and patients, we have examined whether the inclusion of quality variables in the production models changes estimates of scale elasticity. A sample of 58 million patient records from 2008 and 2009 in 149 hospitals in Denmark, Finland, Norway and Sweden were collected. Patient data DRG-points were aggregated into 3 outputs (medical inpatients, surgical inpatients and outpatients) and linked to operating costs for 292 observations. The patient data were used to calculate quality indicators on emergency readmissions and mortality within 30 days, adjusted for age, gender, comorbidities, hospital transfers and DRG using DRG-specific logistic regressions.The hypothesis that the elasticity of scale increases when quality variables are included was tested against the null hypothesis of no change in the scale elasticity. The observations were used to estimate a cost function using Stochastic Frontier Analysis (SFA). Country dummies as well as dummies for University hospitals, capital city hospitals and the average travelling time for the patients were included as environmental variables. The estimated scale elasticities did not change with the inclusion of quality indicators in any of the tested models. This may be because medical volume effects are confined to few patient groups or possibly even offset by effects on other groups, where quality is reduced by volume. In one model, the scale elasticity was significantly larger than 1.0, a result that contradicts previous studies which have found decreasing returns. Published: Online October 2018. In print Janury 2019.","PeriodicalId":30931,"journal":{"name":"Nordic Journal of Health Economics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83104601","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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