Analysis of regional variation in hip and knee joint replacement rates in England using Hospital Episodes Statistics.
ABSTRACT Total hip and knee joint replacements are effective interventions for people with severe arthritis, and demand for these operations appears to be increasing as our population ages. This study explores regional variations in health care and inequalities in the provision of these expensive interventions, which are high on the UK Government's health agenda.
The Hospital Episode Statistics (HES) for England were analysed. The HES database holds information on patients who are admitted to National Health Service (NHS) hospitals in England.
Age-standardized procedure rates were calculated using 5-year age groups with the English mid-year population of 2000 as the reference. Univariate associations between age-standardized operation rates and regional characteristics were assessed using Pearson's correlation coefficient.
Age and sex-standardized surgery rates vary by 25-30%. For both hip and knee replacement, rates are highest in the South West and Midlands and lowest in the North West, South East and London regions. In the case of knee replacement, there are also marked differences in the sex ratios between regions. The variable that explained most variation in hip replacement rates was the proportion of older people in the region. In the case of knee replacement, the number of NHS centres offering surgery in the region was the main explanatory variable, with regions with fewer centres having the highest provision rates.
These data can help to inform planning of services. They suggest that there may be inequities as well as inequalities in the provision of primary joint replacement surgery in England.
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ABSTRACT: Aim This paper is the report of a study exploring the efficacy of a health maintenance clinic intervention for patients with severe osteoarthritis of the hip and knee whilst awaiting primary joint replacement surgery. Background Historically in the UK patients with severe osteoarthritis waited many months for their hip and knee replacement surgery. The waiting time was often in the absence of support and advice relating to symptom management. A health maintenance intervention was developed to provide support to patients whilst awaiting their joint replacement and to optimise their health status ahead of the procedure. Methods A randomised control trial (RCT) compared usual care (pre-operative assessment only) to a new intervention (health maintenance clinic plus pre-operative assessment). A sample of 250 people with osteoarthritis waiting for joint replacement were recruited via an orthopaedic out-patient department between 2005 and 2006. To assess the effectiveness of the intervention outcome, measures were recorded at two points in time (on referral to the waiting list and at 2 weeks prior to surgery). Results No significant difference between the total score on the disease specific outcome measure was found (p = 0.69). However, participants in the experimental group were significantly more satisfied with their care (p = 0.001) and had fewer incidences of postponement of surgery (p = 0.002). Conclusion A tailored intervention for symptom management in the pre-operative period increases patient satisfaction which may have a positive impact on concordance and postoperative recovery. Also, reducing the number of surgical postponements has a positive economic value for health care providers.International Journal of Orthopaedic and Trauma Nursing 11/2013; 17(4):171–179. DOI:10.1016/j.ijotn.2013.07.004
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ABSTRACT: The incidence of THA (total hip arthroplasty) will rise with an aging population and improvements in surgery, a feasible alternative in health care can effectively increase medical quality. The reason of a hip joint replaced is to relieve severe arthritis pain that is limiting your activities. Hip joint replacement is usually done in people age 60 and older. Younger people who have a hip replaced may put extra stress on the artificial hip. This paper uses a serious data screening function by experts to reduce data dimension after data collection from the National Health Insurance database. The proposed model also adopts an imbalanced sampling method to solve class imbalance problem, and utilizes rough set theory to find out core attributes (selected 7 features). Based on the core attributes, the extracted rules can be comprehensive for the rules of medical quality. In verification, THA dataset is taken as case study; the performance of the proposed model is verified and compared with other data-mining methods under various criteria. Furthermore, the performance of the proposed model is identified as winning the listing methods, as well as using hybrid-sampling can increase the far true-positive rate (minority class). The results show that the proposed model is efficient; the performance is superior to the listing methods under the listing criteria. And the generated decision rules and core attributes could find more managerial implication. Moreover, the result can provide stakeholders with useful THA information to help make decision.Quality and Quantity 01/2013; 47(3). DOI:10.1007/s11135-011-9624-9 · 0.76 Impact Factor
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ABSTRACT: Although regional variations in Medicare spending are known, it is not clear whether regional variations exist in hospital charges for total joint arthroplasty. Data from Centers for Medicare and Medicaid Services (CMS) on Diagnosis Related Groups 469 and 470 (Major Joint with and without Major Complicating or Comorbid Condition) from 2011 were analyzed for variation by region. Drastic variations in charges between institutions were apparent with significant differences between regions for hospital charges and payments. The median hospital charge nationwide was $71,601 and $46,219 for Diagnosis Related Groups 469 and 470, respectively, with corresponding median payments of $21,231 and $13,743. Weak to no correlation was found between hospital charges and payments despite adjustments for wage index, cost of living, low-income care and teaching institution status.The Journal of Arthroplasty 05/2014; 29(9). DOI:10.1016/j.arth.2014.03.052 · 2.37 Impact Factor