Evaluation of the Quality of Life and the Quality of Sleep of postmenopausal osteoporotic women, without evidence of an osteoporotic fracture, who attended an outpatient DXA scan service.

OBJECTIVES: The present study aimed to investigate whether impairment of health-related quality of life (HRQOL) and possibly, the quality of sleep (Sleep Quality - SQ), of osteoporotic women, may occur, even before the onset of an osteoporotic fracture. METHODS: The study included 109 women, divided (DXA) into two groups (age-matched): the Control Group (n=68; normal and osteopenic) and the Patient Group (n=41; osteoporotic). Review of medical history of the participants, was followed by evaluation of HRQOL and SQ with the EQ-5D-3L and the PSQI questionnaires, respectively. RESULTS: There was no significant difference between the two groups (Control vs. Patient) in terms of average HRQOL and SQ, as measured by the EQ-5D-3L Questionnaire (0.73 vs. 0.70, p>0.05) and the PSQI Index value (5.56 vs. 6.29, p>0.05), respectively. A high percentage of patients was estimated as having a poor SQ (52.9% of the Control Group and 46.3% of the Patient Group, p>0.05). Increasing age, with or without the presence of osteoporosis, seemed to lead to worst QoL (OR<1.00, p<0.05). CONCLUSIONS: Our study documented homogeneity in HRQOL and SQ, between the two study groups. The strongest predictor for the HRQOL was age (for each year of age increase, the probability of excellent HRQOL significantly decreased).

A window-DEA based efficiency evaluation of the public hospital sector in Greece during the 5-year economic crisis.

The main objective of this study was to apply the non-parametric method of Data Envelopment Analysis (DEA) to measure the efficiency of Greek NHS hospitals between 2009-2013. Hospitals were divided into four separate groups with common characteristics which allowed comparisons to be carried out in the context of increased homogeneity. The window-DEA method was chosen since it leads to increased discrimination on the results especially when applied to small samples and it enables year-by-year comparisons of the results. Three inputs -hospital beds, physicians and other health professionals- and three outputs-hospitalized cases, surgeries and outpatient visits- were chosen as production variables in an input-oriented 2-year window DEA model for the assessment of technical and scale efficiency as well as for the identification of returns to scale. The Malmquist productivity index together with its components (i.e. pure technical efficiency change, scale efficiency change and technological scale) were also calculated in order to analyze the sources of productivity change between the first and last year of the study period. In the context of window analysis, the study identified the individual efficiency trends together with "all-windows" best and worst performers and revealed that a high level of technical and scale efficiency was maintained over the entire 5-year period. Similarly, the relevant findings of Malmquist productivity index analysis showed that both scale and pure technical efficiency were improved in 2013 whilst technological change was found to be in favor of the two groups with the largest hospitals.

Decomposition of potential efficiency gains from hospital mergers in Greece.

This paper evaluates the technical efficiency of 71 Greek public hospitals and examines potential efficiency gains from 13 candidate mergers among them. Efficiency assessments are performed using bootstrapped Data Envelopment Analysis (DEA) whilst merger analysis is conducted by applying the Bogetoft and Wang methodology which allows the overall potential merger gains to be decomposed into three main components of inefficiency, namely technical (or learning), scope (or harmony) and scale (or size) effects. Thus, the analysis provides important insights not only on the magnitude of the potential total efficiency gains but also on their sources. The overall analysis is conducted in the context of a complete methodological framework where methods for outlier detection, returns to scale identification, and bias corrections for DEA estimations are also applied. Mergers are analyzed under the assumptions of constant, variable and non-decreasing returns to scale in an input oriented DEA model with three inputs and three outputs. The main finding of the study indicates that almost all mergers show substantial potential room for efficiency improvement, which is mainly attributed to the pre-merger technical inefficiencies of the individual hospitals and therefore it might be possible to be achieved without the need of implementing full-scale mergers. The same -though, at a lower extent- applies to the harmony effect whilst the size effect shows marginal or even negative gains.

Employing post-DEA cross-evaluation and cluster analysis in a sample of Greek NHS hospitals.

To increase Data Envelopment Analysis (DEA) discrimination of efficient Decision Making Units (DMUs), by complementing "self-evaluated" efficiencies with "peer-evaluated" cross-efficiencies and, based on these results, to classify the DMUs using cluster analysis. Healthcare, which is deprived of such studies, was chosen as the study area. The sample consisted of 27 small- to medium-sized (70-500 beds) NHS general hospitals distributed throughout Greece, in areas where they are the sole NHS representatives. DEA was performed on 2005 data collected from the Ministry of Health and the General Secretariat of the National Statistical Service. Three inputs -hospital beds, physicians and other health professionals- and three outputs -case-mix adjusted hospitalized cases, surgeries and outpatient visits- were included in input-oriented, constant-returns-to-scale (CRS) and variable-returns-to-scale (VRS) models. In a second stage (post-DEA), aggressive and benevolent cross-efficiency formulations and clustering were employed, to validate (or not) the initial DEA scores. The "maverick index" was used to sort the peer-appraised hospitals. All analyses were performed using custom-made software. Ten benchmark hospitals were identified by DEA, but using the aggressive and benevolent formulations showed that two and four of them respectively were at the lower end of the maverick index list. On the other hand, only one 100% efficient (self-appraised) hospital was at the higher end of the list, using either formulation. Cluster analysis produced a hierarchical "tree" structure which dichotomized the hospitals in accordance to the cross-evaluation results, and provided insight on the two-dimensional path to improving efficiency. This is, to our awareness, the first study in the healthcare domain to employ both of these post-DEA techniques (cross efficiency and clustering) at the hospital (i.e. micro) level. The potential benefit for decision-makers is the capability to examine high and low "all-round" performers and maverick hospitals more closely, and identify and address problems typically overlooked by first-stage DEA.

The impact of non-discretionary factors on DEA and SFA technical efficiency differences.

The purpose of this study was to examine if factors of the external operating environment can explain differences in technical efficiency derived from Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). In a sample of 124 dialysis facilities, technical efficiency was compared according to ownership, region, years in operation and size. With second-stage Tobit regression, DEA and SFA efficiency was regressed against these environmental factors to determine their potential for predicting technical efficiency, as well as the efficiency differences between the two frontier methods. DEA expectedly generated lower mean efficiency scores than SFA (68.2% vs. 79.4%, P < 0.001), due to the "random effects" term computed by the latter, in addition to "true" inefficiency. This finding was consistent for the subgroups formed on the basis of the environmental factors. Half the variation in the DEA-SFA efficiency differences was explained by environmental factors. This suggests that in addition to market instabilities, luck, and other related phenomena, decision-makers in their effort to determine optimal resource allocation, should point their attention to the potentially useful insight provided by environmental factors.