103 results found
Alexandrescu R, Bottle A, Hua Jen M, et al., 2015, The US hospital standardised mortality ratio: Retrospective database study of Massachusetts hospitals., JRSM Open, Vol: 6, ISSN: 2054-2704
OBJECTIVES: To present a case-mix adjustment model that can be used to calculate Massachusetts hospital standardised mortality ratios and can be further adapted for other state-wide data-sets. DESIGN: We used binary logistic regression models to predict the probability of death and to calculate the hospital standardised mortality ratios. Independent variables were patient sociodemographic characteristics (such as age, gender) and healthcare details (such as admission source). Statistical performance was evaluated using c statistics, Brier score and the Hosmer-Lemeshow test. SETTING: Massachusetts hospitals providing care to patients over financial years 2005/6 to 2007/8. PATIENTS: 1,073,122 patients admitted to Massachusetts hospitals corresponding to 36 hospital standardised mortality ratio diagnosis groups that account for 80% of in-hospital deaths nationally. MAIN OUTCOME MEASURES: Adjusted in-hospital mortality rates and hospital standardised mortality ratios. RESULTS: The significant factors determining in-hospital mortality included age, admission type, primary diagnosis, the Charlson index and do-not-resuscitate status. The Massachusetts hospital standardised mortality ratios for acute (non-specialist) hospitals ranged from 60.3 (95% confidence limits 52.7-68.6) to 130.3 (116.1-145.8). The reference standard hospital standardised mortality ratio is 100 with the values below and above 100 suggesting either random or special cause variation. The model was characterised by excellent discrimination (c statistic 0.87), high accuracy (Brier statistics 0.03) and close agreement between predicted and observed mortality rates. CONCLUSIONS: We have developed a case-mix model to give insight into mortality rates for patients served by hospitals in Massachusetts. Our analysis indicates that this technique would be applicable and relevant to Massachusetts hospital care as well as to other US hospitals.
Alexandrescu R, Bottle A, Jarman B, et al., 2014, Classifying Hospitals as Mortality Outliers: Logistic Versus Hierarchical Logistic Models, JOURNAL OF MEDICAL SYSTEMS, Vol: 38, ISSN: 0148-5598
Jarman B, 2014, Quality and safety in healthcare revisited: a challenge to anaesthetists., Anaesthesia, Vol: 69, Pages: 531-536
Alexandrescu R, Bottle A, Jarman B, et al., 2013, Current ICD10 codes are insufficient to clearly distinguish acute myocardial infarction type: a descriptive study, BMC HEALTH SERVICES RESEARCH, Vol: 13, ISSN: 1472-6963
Carter P, Jarman B, 2013, Who knew what, and when, at Mid Staffs?, BMJ-BRITISH MEDICAL JOURNAL, Vol: 346, ISSN: 1756-1833
Jarman B, 2013, Quality of care and patient safety in the UK: the way forward after Mid Staffordshire, LANCET, Vol: 382, Pages: 573-575, ISSN: 0140-6736
Jarman B, 2013, Monitoring patient safety in general practice: the increasing role of GPs., Br J Gen Pract, Vol: 63, Pages: 398-399
Pinder RJ, Greaves FE, Aylin PP, et al., 2013, Staff perceptions of quality of care: an observational study of the NHS Staff Survey in hospitals in England, BMJ QUALITY & SAFETY, Vol: 22, Pages: 563-570, ISSN: 2044-5415
Alexandrescu R, Bottle A, Jarman B, et al., 2012, Impact of transfer for angioplasty and distance on AMI in-hospital mortality., Acute Card Care, Vol: 14, Pages: 5-12
BACKGROUND: The aim of the study was to evaluate the impact of transfer status and distance on in-hospital mortality for acute myocardial infarction (AMI) patients undergoing angioplasty on the same or next day of hospital admission. METHODS: Retrospective analysis of English hospital administrative data using logistic regression modelling. RESULTS: After risk adjustment for the patient baseline characteristics, transferred patients had a higher in-hospital mortality rate than those admitted directly to hospital for angioplasty performed on the same or next day: OR=1.25 (95% confidence interval: 1.02-1.52), P=0.029. There was no statistically significant increased risk of in-hospital mortality with increasing distance between home and angioplasty centre (OR=0.98 (0.84-1.16), P=0.842 for 6-15 km and 1.03 (0.87-1.22), P=0.768 for >15 km when compared with <6 km) or with increasing inter-hospital transfer distance for angioplasty (OR=0.84 (0.55-1.29), P=0.435 for 16-34 km and 0.88 (0.58-1.35), for >34 km when compared with <16 km). CONCLUSIONS: Transfer status is associated with in-hospital mortality rate for AMI patients undergoing angioplasty on the same or next day of hospital admission. No relation between in-hospital mortality and the distance from home to angioplasty centre or inter-hospital transfer distance for angioplasty was found in these patients.
Jarman B, 2012, When managers rule., BMJ, Vol: 345
Alexandrescu R, Jen M-H, Bottle A, et al., 2011, Logistic Versus Hierarchical Modeling: An Analysis of a Statewide Inpatient Sample, JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS, Vol: 213, Pages: 392-401, ISSN: 1072-7515
Bottle A, Jarman B, Aylin P, 2011, Strengths and weaknesses of hospital standardised mortality ratios, BMJ-BRITISH MEDICAL JOURNAL, Vol: 342, ISSN: 1756-1833
Bottle A, Jarman B, Aylin P, 2011, Hospital Standardized Mortality Ratios: Sensitivity Analyses on the Impact of Coding, HEALTH SERVICES RESEARCH, Vol: 46, Pages: 1741-1761, ISSN: 0017-9124
Bottle A, Jarman B, Aylin P, 2011, Strengths and weaknesses of hospital standardised mortality ratios, BMJ, Vol: 342, Pages: 749-753
Hospital standardised mortality ratios are fairly easy to produce and, as the example of Mid Staffordshire shows, can help identify hospitals with poor performance. However, they are not without problems.
Jarman B, 2011, Quality and safety in healthcare and the role of anaesthetists., Anaesthesia, Vol: 66, Pages: 757-761
Jarman B, Aylin P, Bottle A, 2010, Hospital mortality ratios A plea for reason, BRITISH MEDICAL JOURNAL, Vol: 340, ISSN: 0959-535X
Jarman B, Pieter D, van der Veen AA, et al., 2010, The hospital standardised mortality ratio: a powerful tool for Dutch hospitals to assess their quality of care?, QUALITY & SAFETY IN HEALTH CARE, Vol: 19, Pages: 9-13, ISSN: 1475-3898
Robb E, Jarman B, Suntharalingam G, et al., 2010, Quality Improvement Report Using care bundles to reduce in-hospital mortality: quantitative survey, BRITISH MEDICAL JOURNAL, Vol: 340, ISSN: 0959-535X
Aylin P, Bottle A, Jarman B, 2009, Standardised mortality ratios Monitoring mortality, BRITISH MEDICAL JOURNAL, Vol: 338, ISSN: 0959-8146
Jolley DJ, Jarman B, Elliott P, 2009, Socio-economic confounding, Geographical and Environmental Epidemiology: Methods for Small Area Studies, ISBN: 9780191723636
© Oxford University Press, 1992. All rights reserved. This chapter reviews methods used to quantify ecological differences in the socioeconomic status of areas, with reference to the literature on inequalities in health in Britain and elsewhere. It examines local variation in socio-economic characteristics near an industrial source, and presents an approach to the adjustment of socio-economic confounding at the small-area level.
Heijink R, Koolman X, Pieter D, et al., 2008, Measuring and explaining mortality in Dutch hospitals; The hospital standardized mortality rate between 2003 and 2005, BMC HEALTH SERVICES RESEARCH, Vol: 8, ISSN: 1472-6963
Jarman B, 2008, In defence of the hospital standardized mortality ratio., Healthc Pap, Vol: 8, Pages: 37-42, ISSN: 1488-917X
This commentary addresses many of the points made by Penfold and colleagues in the lead article of this issue of Healthcare Papers, including the relationships between hospital standardized mortality ratios (HSMRs) and adverse event reporting, hospital policy and discharge rates. It also discusses what the HSMR is intended to measure, the various analyses and cumulative sum statistic data that my colleagues and I provide to hospitals, interpretation of the results and the inclusion or exclusion of patients receiving comfort or palliative care. It should be noted that my colleagues and I still have the attitude that if anyone can make improvements in our methodologies, we are happy to adopt these improvements as long as they are statistically sound. We feel strongly that if a hospital has a high HSMR, then further investigation is merited to exclude or identify quality-of-care issues; this approach can result in a useful insight into mortality at the institution, which can be associated with a decrease in mortality.
Aylin P, Bottle A, Elliott P, 2007, Surgical mortality - Hospital episode statistics v central cardiac audit database, BRITISH MEDICAL JOURNAL, Vol: 335, Pages: 839-839, ISSN: 0959-8146
Aylin P, Bottle A, Elliott P, et al., 2007, Hospital episode statistics v central cardiac audit database, BMJ, Vol: 335, ISSN: 0959-8138
Jarman B, 2006, Privatising primary care., Br J Gen Pract, Vol: 56, ISSN: 0960-1643
Wright J, Dugdale B, Hammond I, et al., 2006, Learning from death: a hospital mortality reduction programme, JOURNAL OF THE ROYAL SOCIETY OF MEDICINE, Vol: 99, Pages: 303-308, ISSN: 0141-0768
Aylin P, Jarman B, Elliott P, 2005, Paediatric cardiac surgical mortality after Bristol - Reply, BRITISH MEDICAL JOURNAL, Vol: 330, Pages: 44-44, ISSN: 0959-535X
Aylin P, Williams S, Jarman B, et al., 2005, Dr Foster's case notes - Trends in day surgery rates, BRITISH MEDICAL JOURNAL, Vol: 331, Pages: 803-+, ISSN: 0959-8146
Hurwitz B, Jarman B, Cook A, et al., 2005, Scientific evaluation of community-based Parkinson's disease nurse specialists on patient outcomes and health care costs, JOURNAL OF EVALUATION IN CLINICAL PRACTICE, Vol: 11, Pages: 97-110, ISSN: 1356-1294
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