Oliver Tiemann

Changes in hospital efficiency after privatization.

We investigated the effects of privatization on hospital efficiency in Germany. To do so, we obtained bootstrapped data envelopment analysis (DEA) efficiency scores in the first stage of our analysis and subsequently employed a difference-in-difference matching approach within a panel regression framework. Our findings show that conversions from public to private for-profit status were associated with an increase in efficiency of between 2.9 and 4.9%. We defined four alternative post-privatization periods and found that the increase in efficiency after a conversion to private for-profit status appeared to be permanent. We also observed an increase in efficiency for the first three years after hospitals were converted to private non-profit status, but our estimations suggest that this effect was rather transitory. Our findings also show that the efficiency gains after a conversion to private for-profit status were achieved through substantial decreases in staffing ratios in all analyzed staff categories with the exception of physicians and administrative staff. It was also striking that the efficiency gains of hospitals converted to for-profit status were significantly lower in the diagnosis-related groups (DRG) era than in the pre-DRG era. Altogether, our results suggest that converting hospitals to private for-profit status may be an effective way to ensure the scarce resources in the hospital sector are used more efficiently.

Effects of Ownership on Hospital Efficiency in Germany

The objective of our study was to evaluate the efficiency of public, private for-profit, and private non-profit hospitals in Germany. First, bootstrapped data envelopment analysis (DEA) was used to evaluate the efficiency of a panel (n = 1,046) of public, private for-profit, and private non-profit hospitals between 2002 and 2006. This was followed by a second-step truncated linear regression model with bootstrapped DEA efficiency scores as dependent variable. The results show that public hospitals performed significantly better than their private for-profit and non-profit counterparts. In addition, we found a significant positive association between hospital size and efficiency, and that competitive pressure had a significant negative impact on hospital efficiency.

Hospital ownership and efficiency: A review of studies with particular focus on Germany

The German hospital market has been subject over the past two decades to a variety of healthcare reforms. Particularly the introduction of diagnosis-related groups (DRGs) in 2004 aimed to increase efficiency of hospitals. The objective of the paper is to review recent studies comparing the efficiency of German public, private non-profit and private for-profit hospitals. The results of the studies are quite mixed. However, in line with the evidence found in studies from other countries, especially the US, the evidence from Germany suggests that private ownership (i.e., private non-profit and private for-profit) is not necessarily associated with higher efficiency compared to public ownership. This may be a surprising result to many policy makers as private for-profit hospitals are often perceived the most efficient ownership type by the public.

Variations in hospitalisation costs for acute myocardial infarction – a comparison across Europe

The aim of this study was to determine whether between-country variations in hospital costs are larger than within-country variations and, furthermore, to explore reasons for this variability. For this purpose, we chose the primary treatment of patients with acute myocardial infarction (AMI) as an episode of care. We obtained hospitalisation costs and reimbursement rates from 45 hospitals in nine different EU member states (i.e. Denmark, England, France, Germany, Hungary, Italy, Netherlands, Poland, and Spain) for the year 2005. To further analyse the variations in hospital costs, we employed a hierarchical random effects model based on treatment and hospital characteristics and using purchasing power parities (PPPs) as a proxy for country-specific price levels. The between-country standard error was estimated at 2473 euros, whereas the within-country standard error was estimated at 1242 euros. Our regression analysis showed that percutaneous coronary intervention was associated with significantly increased hospitals costs compared to other treatment strategies. We were able to distinguish between three groups of countries with different cost levels based on the number of hospitals that were able to provide these services (i.e. percutaneous transluminal coronary angioplasty (PTCA) with intracoronary stenting). Excluding Hungary, Poland, and Spain, where none of the participating hospitals were able to provide these procedures, the between-country standard error decreased to 1632 euros, whereas the within-country standard error increased to 1416 euros. Finally, we observed exogenous price-level effects between countries and within countries for hospitals located in urban areas.

Costs and quality of hospitals in different health care systems: a multi-level approach with propensity score matching†

Cross-country comparisons of costs and quality between hospitals are often made at the macro level. The goal of this study was to explore methods to compare micro-level data from hospitals in different health care systems. To do so, we developed a multi-level framework in combination with a propensity score matching technique using similarly structured data for patients receiving treatment for acute myocardial infarction in German and US Veterans Health Administration hospitals. Our case study shows important differences in results between multi-level regressions based on matched and unmatched samples. We conclude that propensity score matching techniques are an appropriate way to deal with the usual baseline imbalances across the samples from different countries. Multi-level models are recommendable to consider the clustered structure of the data when patient-level data from different hospitals and health care systems are compared. The results provide an important justification for exploring new ways in performing health system comparisons.

Cost of Illness of Cystic Fibrosis in Germany

AbstractBackground Cystic fibrosis (CF) is the most common life-shortening genetic disorder among Whites worldwide. Because many of these patients experience chronic endobronchial colonization and have to take antibiotics and be treated as inpatients, societal costs of CF may be high. As the disease severity varies considerably among patients, costs may differ between patients. Objectives Our objectives were to calculate the average total costs of CF per patient and per year from a societal perspective; to include all direct medical and non-medical costs as well as indirect costs; to identify the main cost drivers; to investigate whether patients with CF can be grouped into homogenous cost groups; and to determine the influence of specific factors on different cost categories. Methods Resource utilization data were collected for 87 patients admitted to an inpatient unit at a CF treatment centre during the first 6 months of 2004 and 125 patients who visited the centre’s CF outpatient unit during the entire year. Fifty-four patients were admitted to the hospital and also visited the outpatient unit. Since all patients were exclusively treated at the centre, data could be aggregated. Costs that varied greatly between patients were measured per patient. The remaining costs were summarized as overhead costs and allocated on the basis of days of treatment or contacts per patient. Costs of the outpatient and inpatient units and costs for drugs patients received at the outpatient pharmacy were summarized as direct medical costs. Direct non-medical costs (i.e. travel expenses), as well as indirect costs (i. e. absence from work, productivity losses), were also included in the analysis. Main cost drivers were detected by the analysis of different cost categories. Patients were classified according to a diagnosis-related severity model, and median comparison tests (Wilcoxon-Mann-Whitney tests) were performed to investigate differences between the severity groups. Generalized least squares (GLS) regressions were used to identify variables influencing different cost categories. A sensitivity analysis using Monte Carlo simulation was performed. Results The mean total cost per patient per year was €41468 (year 2004 values). Direct medical costs accounted for more than 90% of total costs and averaged €38 869 (€3876 to €88 096), whereas direct non-medical costs were minimal. Indirect costs amounted to €2491 (6% of total costs). Costs for drugs patients received at the outpatient pharmacy were the main cost driver. Costs rose with the degree of severity. Patients with moderate and severe disease had significantly higher direct costs than the relatively milder group. Regression analysis revealed that direct costs were mainly affected by the diagnosis-related severity level and the expiratory volume; the coefficient indicating the relationship between costs for mild CF patients and other patients rose with the degree of severity. A similar result was obtained for drug costs per patient as the dependent variable. Monte Carlo simulation suggests that there is a 90% probability that annual costs will be lower than €37 300. Conclusions The share of indirect costs as a percentage of total costs for CF was rather low in this study. However, the relevance of indirect costs is likely to increase in the future as the life expectancy of CF patients increases, which is likely to lead to a rising work disability rate and thus increase indirect costs. Moreover we found that infection with Pseudomonas aeruginosa increases costs substantially. Thus, a decrease of the prevalence of P. aeruginosa would lead to substantial savings for society.

Cross-country comparisons of costs: the use of episode-specific transitive purchasing power parities with standardised cost categories

International comparisons of healthcare costs are growing in importance for a number of different applications. The use of common approaches to converting costs such as GDP purchasing power parities (PPPs) often does not reflect price differences in healthcare in an appropriate manner. This means that new approaches need to be explored. The objective of this paper is to demonstrate the feasibility of using episode-specific PPPs (ESPPPs) to facilitate cross-country comparisons of healthcare costs and to compare this approach with other common approaches to conversion. Costs for five care episodes from hospitals in eight European countries were obtained from the EU HealthBASKET project. ESPPPs were created by using Fisher-type PPPs in combination with the Eltetö-Köves-Szulc method at the episode level. Differences in ESPPPs among the five care episodes were discussed and compared with other common conversion approaches. We found that ESPPPs-reflected prices and resource use more accurately than conventional conversion approaches such as GDP PPPs and medical care PPPs. This was particularly evident for labour-intensive care episodes in which other conversion approaches revealed problems in the way that labour input had not been considered appropriately. The results demonstrate that ESPPPs are preferable to other common conversion approaches when international healthcare cost comparisons are performed.

Employment effects of hospital privatization in Germany

The main argument for the ongoing privatization process is that privatization will lead to an increase in efficiency, which has been confirmed by a large number of studies. An important argument against privatization is that privatization may lead to employment reductions. In the hospital sector, potential employment reductions might also lead to a decrease in the quality of care. This is the first study to investigate the employment effects of different types of hospital privatization (i.e., for-profit vs non-profit privatization) on different categories of staff. A combination of propensity score matching and difference-in-difference methods was used to identify the causal effect. We found large employment reductions after for-profit privatization, while there were no permanent reductions after non-profit privatization. Moreover, even for-profit privatization does not affect all types of staff. While there are large reductions in non-clinical staff, we could not detect any reduction in the number of physicians. The consequences of the detected employment effects of privatization have to be addressed in greater detail in future research.