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      Measuring and explaining mortality in Dutch hospitals; The Hospital Standardized Mortality Rate between 2003 and 2005

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          Abstract

          Background

          Indicators of hospital quality, such as hospital standardized mortality ratios (HSMR), have been used increasingly to assess and improve hospital quality. Our aim has been to describe and explain variation in new HSMRs for the Netherlands.

          Methods

          HSMRs were estimated using data from the complete population of discharged patients during 2003 to 2005. We used binary logistic regression to indirectly standardize for differences in case-mix. Out of a total of 101 hospitals 89 hospitals remained in our explanatory analysis. In this analysis we explored the association between HSMRs and determinants that can and cannot be influenced by hospitals. For this analysis we used a two-level hierarchical linear regression model to explain variation in yearly HSMRs.

          Results

          The average HSMR decreased yearly with more than eight percent. The highest HSMR was about twice as high as the lowest HSMR in all years. More than 2/3 of the variation stemmed from between-hospital variation. Year (-), local number of general practitioners (-) and hospital type were significantly associated with the HSMR in all tested models.

          Conclusion

          HSMR scores vary substantially between hospitals, while rankings appear stable over time. We find no evidence that the HSMR cannot be used as an indicator to monitor and compare hospital quality. Because the standardization method is indirect, the comparisons are most relevant from a societal perspective but less so from an individual perspective. We find evidence of comparatively higher HSMRs in academic hospitals. This may result from (good quality) high-risk procedures, low quality of care or inadequate case-mix correction.

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          Most cited references28

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          Is volume related to outcome in health care? A systematic review and methodologic critique of the literature.

          To systematically review the methodologic rigor of the research on volume and outcomes and to summarize the magnitude and significance of the association between them. The authors searched MEDLINE from January 1980 to December 2000 for English-language, population-based studies examining the independent relationship between hospital or physician volume and clinical outcomes. Bibliographies were reviewed to identify other articles of interest, and experts were contacted about missing or unpublished studies. Of 272 studies reviewed, 135 met inclusion criteria and covered 27 procedures and clinical conditions. Two investigators independently reviewed each article, using a standard form to abstract information on key study characteristics and results. The methodologic rigor of the primary studies varied. Few studies used clinical data for risk adjustment or examined effects of hospital and physician volume simultaneously. Overall, 71% of all studies of hospital volume and 69% of studies of physician volume reported statistically significant associations between higher volume and better outcomes. The strongest associations were found for AIDS treatment and for surgery on pancreatic cancer, esophageal cancer, abdominal aortic aneurysms, and pediatric cardiac problems (a median of 3.3 to 13 excess deaths per 100 cases were attributed to low volume). Although statistically significant, the volume-outcome relationship for coronary artery bypass surgery, coronary angioplasty, carotid endarterectomy, other cancer surgery, and orthopedic procedures was of much smaller magnitude. Hospital volume-outcome studies that performed risk adjustment by using clinical data were less likely to report significant associations than were studies that adjusted for risk by using administrative data. High volume is associated with better outcomes across a wide range of procedures and conditions, but the magnitude of the association varies greatly. The clinical and policy significance of these findings is complicated by the methodologic shortcomings of many studies. Differences in case mix and processes of care between high- and low-volume providers may explain part of the observed relationship between volume and outcome.
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            The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments.

            To examine the relationship between hospital and emergency department (ED) occupancy, as indicators of hospital overcrowding, and mortality after emergency admission. Retrospective analysis of 62 495 probabilistically linked emergency hospital admissions and death records. Three tertiary metropolitan hospitals between July 2000 and June 2003. All patients 18 years or older whose first ED attendance resulted in hospital admission during the study period. Deaths on days 2, 7 and 30 were evaluated against an Overcrowding Hazard Scale based on hospital and ED occupancy, after adjusting for age, diagnosis, referral source, urgency and mode of transport to hospital. There was a linear relationship between the Overcrowding Hazard Scale and deaths on Day 7 (r=0.98; 95% CI, 0.79-1.00). An Overcrowding Hazard Scale>2 was associated with an increased Day 2, Day 7 and Day 30 hazard ratio for death of 1.3 (95% CI, 1.1-1.6), 1.3 (95% CI, 1.2-1.5) and 1.2 (95% CI, 1.1-1.3), respectively. Deaths at 30 days associated with an Overcrowding Hazard Scale>2 compared with one of <3 were undifferentiated with respect to age, diagnosis, urgency, transport mode, referral source or hospital length of stay, but had longer ED durations of stay (risk ratio per hour of ED stay, 1.1; 95% CI, 1.1-1.1; P<0.001) and longer physician waiting times (risk ratio per hour of ED wait, 1.2; 95% CI, 1.1-1.3; P=0.01). Hospital and ED overcrowding is associated with increased mortality. The Overcrowding Hazard Scale may be used to assess the hazard associated with hospital and ED overcrowding. Reducing overcrowding may improve outcomes for patients requiring emergency hospital admission.
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              Explaining differences in English hospital death rates using routinely collected data.

              To ascertain hospital inpatient mortality in England and to determine which factors best explain variation in standardised hospital death ratios. Weighted linear regression analysis of routinely collected data over four years, with hospital standardised mortality ratios as the dependent variable. England. Eight million discharges from NHS hospitals when the primary diagnosis was one of the diagnoses accounting for 80% of inpatient deaths. Hospital standardised mortality ratios and predictors of variations in these ratios. The four year crude death rates varied across hospitals from 3.4% to 13.6% (average for England 8.5%), and standardised hospital mortality ratios ranged from 53 to 137 (average for England 100). The percentage of cases that were emergency admissions (60% of total hospital admissions) was the best predictor of this variation in mortality, with the ratio of hospital doctors to beds and general practitioners to head of population the next best predictors. When analyses were restricted to emergency admissions (which covered 93% of all patient deaths analysed) number of doctors per bed was the best predictor. Analysis of hospital episode statistics reveals wide variation in standardised hospital mortality ratios in England. The percentage of total admissions classified as emergencies is the most powerful predictor of variation in mortality. The ratios of doctors to head of population served, both in hospital and in general practice, seem to be critical determinants of standardised hospital death rates; the higher these ratios, the lower the death rates in both cases.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2008
                3 April 2008
                : 8
                : 73
                Affiliations
                [1 ]National Institute for Public Health and the Environment, Bilthoven, The Netherlands
                [2 ]Erasmus University Medical Center, Rotterdam, The Netherlands
                [3 ]Prismant, Utrecht, The Netherlands
                [4 ]De Praktijk Index, Utrecht, The Netherlands
                [5 ]Dr Foster Unit, Imperial College, London, UK
                [6 ]Tilburg University, Tilburg, The Netherlands
                Article
                1472-6963-8-73
                10.1186/1472-6963-8-73
                2362116
                18384695
                925c62a9-4ca6-4a52-b2b8-b42732f3f90b
                Copyright © 2008 Heijink et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 November 2007
                : 3 April 2008
                Categories
                Research Article

                Health & Social care
                Health & Social care

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