The treatment-mix, treatment time, and dental status of 268 male industrial workers entitled to employer-provided dental care were studied. The data were collected from treatment records of the covered workers over the 5-year period 1989-93. Treatment time was based on clinical treatment time recorded per patient visit, and the treatment procedure codes were reclassified into a treatment-mix according to American Dental Association categories, with a modification combining endodontics and restorative treatment. The mean number of check-ups followed by prescribed treatment (treatment courses) during the 5 years was 3.7 among those who had entered the in-house dental care program prior to the monitored period (old attenders). Their treatment time was stable, 57-63 min per year, while the first-year mean treatment time (170 min) of those who had entered the program during the study period (new attenders) was significantly higher (P
The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator.
Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013.
Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved.
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When deciding the right forms of treatment for various medical conditions it has been usual to consider medical knowledge, norms and experience. Increasingly, economic factors and principles are being introduced by the management, in the form of health economics and pharmaco-economic analyses, enforced as budgetary cuts and demands for rationalisation and measures to increase efficiency. Economic evaluations require construction of models for analyses. We have used DRG-information, National Health reimbursements and pharmacological retail prices to make a cost-efficiency analysis of treatments of menorrhagia. The analysis showed better cost-efficiency for certain pharmacological treatments than for surgery.
Accurate prediction of survival for patients with end-stage renal disease (ESRD) and multiple comorbid conditions is difficult. In nondialysis patients, the Charlson Comorbidity Index has been used to adjust for comorbidity. The purpose of this study is to assess the validity of the Charlson index in incident dialysis patients and modify the index for use specifically in this patient population.
Subjects included all incident hemodialysis and peritoneal dialysis patients starting dialysis therapy between July 1, 1999, and November 30, 2000. These 237 patients formed a cohort from which new integer weights for Charlson comorbidities were derived using Cox proportional hazards modeling. Performance of the original Charlson index and the new ESRD comorbidity index were compared using Kaplan-Meier survival curves, change in likelihood ratio, and the c statistic.
After multivariate analysis and conversion of hazard ratios to index weights, only 6 of the original 18 Charlson variables were assigned the same weight and 6 variables were assigned a weight higher than in the original Charlson index. Using Kaplan-Meier survival curves, we found that both the original Charlson index and the new ESRD comorbidity index were associated with and able to describe a wide range of survival. However, the new study-specific index had better validated performance, indicated by a greater change in the likelihood ratio test and higher c statistic.
This study indicates that the original Charlson index is a valid tool to assess comorbidity and predict survival in patients with ESRD. However, our modified ESRD comorbidity index had slightly better performance characteristics in this population.
Case mix methods such as diagnosis related groups have become a basis of payment for inpatient hospitalizations in many countries. Specifying cost weight values for case mix system payment has important consequences; recent evidence suggests case mix cost weight inaccuracies influence the supply of some hospital-based services. To begin to address the question of case mix cost weight accuracy, this paper is motivated by the objective of improving the accuracy of cost weight values due to inaccurate or incomplete comorbidity data. The methods are suitable to case mix methods that incorporate disease severity or comorbidity adjustments. The methods are based on the availability of detailed clinical and cost information linked at the patient level and leverage recent results from clinical data audits. A Bayesian framework is used to synthesize clinical data audit information regarding misclassification probabilities into cost weight value calculations. The models are implemented through Markov chain Monte Carlo methods. An example used to demonstrate the methods finds that inaccurate comorbidity data affects cost weight values by biasing cost weight values (and payments) downward. The implications for hospital payments are discussed and the generalizability of the approach is explored.
OBJECTIVE: We analysed the variation in the outcome of infrainguinal bypass surgery between departments in a register for clinical audit to see if variation in case-mix influenced the results. MATERIALS AND METHODS: The study was a retrospective analysis of 764 infrainguinal bypass operations performed from 1988 to 1990 at six Swedish surgical departments. Results were assessed at 30 days and at 1 year postoperatively. RESULTS: There was a significant variation (p
To examine the characteristics of patients transferred from a rural hospital emergency department, to compare them with patients admitted on an emergency basis, and to use this information to help plan physician education.
Descriptive study using records for the period January 1, 1991, to June 30, 1992.
The emergency department at Bonnyville Health Centre, an acute care rural hospital located 240 km northeast of Edmonton, serving a catchment population of approximately 10,000.
One thousand fifty-five patients seen in the emergency department who were either transferred to another centre or admitted to the Bonnyville Health Centre on an emergency basis.
For the transferred group, main diagnosis, category of transfer, and reason for transfer. For the admitted group, main diagnosis, length of stay, type of discharge.
Of the 1055 patients ill enough to be either admitted or transferred, 114 (10.8%) were transferred. Those transferred were predominantly men, the elderly, and people with orthopedic injuries or neurologic diseases. Those admitted presented primarily with internal, respiratory, gynecologic, or pediatric disorders. Reason for transfer was mainly lack of specialized services or equipment at the rural hospital.
Patients transferred out of the emergency department differed from those admitted in diagnoses and sex. Most transfers were considered "mandatory." Results of this analysis supported incorporating a formal rotation in orthopedics and adding 4 weeks to the existing emergency medicine rotation in our family medicine residency program.
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Research has demonstrated increased mortality rates in adolescent psychiatric in-patients.
To investigate this excess mortality by calculating standardised mortality ratios (SMRs) relative to cause of death, diagnosis, cohort and age.
A nationwide Norwegian sample of 1095 former adolescent psychiatric in-patients were followed up 15-33 years after first hospitalisation by record linkage to the National Death Cause Registry.
The SMR was significantly increased for almost all causes of death investigated. In males, all psychiatric diagnoses had significantly increased SMRs, whereas in females, organic mental disorder, anxiety disorder and affective disorder had non-significantly increased SMRs. The SMR was significantly elevated for all age-spans and cohorts investigated.
A broad prevention strategy is needed to combat the increased mortality rates found in adolescent psychiatric in-patients.
A recent Canadian Institute for Health Information report on all-cause readmission identified that cancer patients had higher-than-average readmission rates. This study provides further insight on the experience of cancer patients, exploring the risk factors associated with readmission at patient, hospital and community levels. An analysis showed that patient characteristics, including the reason for initial hospitalization, sex, co-morbidity levels, admission through the emergency department and the number of previous acute care admissions, were associated with readmission for cancer patients. In addition, we found that the readmission rate for these patients varied by hospital size and whether the patients lived in rural or urban locations.