What is the perceived impact of Alexander technique lessons on health status, costs and pain management in the real life setting of an English hospital? The results of a mixed methods evaluation of an Alexander technique service for those with chronic bac

© 2015 McClean et al. Background: Randomised controlled trial evidence indicates that Alexander Technique is clinically and cost effective for chronic back pain. The aim of this mixed methods evaluation was to explore the role and perceived impact of Alexander Technique lessons in the naturalistic setting of an acute hospital Pain Management Clinic in England. Methods: To capture changes in health status and resource use amongst service users, 43 service users were administered three widely used questionnaires (Brief Pain Inventory, MYMOP and Client Service Resource Inventory) at three time points: baseline, six weeks and three months after baseline. We also carried out 27 telephone interviews with service users and seven face-to-face interviews with pain clinic staff and Alexander Technique teachers. Quantitative data were analysed using descriptive statistics and qualitative data were analysed thematically. Results: Those taking Alexander Technique lessons reported small improvements in health outcomes, and condition-related costs fell. However, due to the non-randomised, uncontrolled nature of the study design, changes cannot be attributed to the Alexander Technique lessons. Service users stated that their relationship to pain and pain management had changed, especially those who were more committed to practising the techniques regularly. These changes may explain the reported reduction in pain-related service use and the corresponding lower associated costs. Conclusions: Alexander Technique lessons may be used as another approach to pain management. The findings suggests that Alexander Technique lessons can help improve self-efficacy for those who are sufficiently motivated, which in turn may have an impact on service utilisation levels

Keep it Simple? Predicting Primary Health Care Costs with Measures of Morbidity and Multimorbidity

In this paper we investigate the relationship between patients’ primary care costs (consultations, tests, drugs) and their age, gender, deprivation and alternative measures of their morbidity and multimorbidity. Such information is required in order to set capitation fees or budgets for general practices to cover their expenditure on providing primary care services. It is also useful to examine whether practices’ expenditure decisions vary equitably with patient characteristics. Electronic practice record keeping systems mean that there is very rich information on patient diagnoses. But the diagnostic information (with over 9000 possible diagnoses) is too detailed to be practicable for setting capitation fees or practice budgets. Some method of summarizing such information into more manageable measures of morbidity is required. We therefore compared the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs using data on 86,100 individuals in 174 English practices. The measures were derived from four morbidity descriptive systems (17 chronic diseases in the Quality and Outcomes Framework (QOF), 17 chronic diseases in the Charlson scheme, 114 Expanded Diagnosis Clusters (EDCs), and 68 Adjusted Clinical Groups (ACGs)). We found that, in general, for a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power and that measures with more categories did better than those with fewer. The EDC measures performed best, followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Allowing for individual patient morbidity greatly reduced the association of age and cost. There was a pro-deprived bias in expenditure: after allowing for morbidity, patients in areas in the highest deprivation decile had costs which were 22% higher than those in the lowest deprivation decile. The predictive ability of the best performing morbidity and multimorbidity measures was very good for this type of individual level cross section data, with R2 ranging from 0.31 to 0.46. The statistical method of estimating the relationship between patient characteristics and costs was less important than the type of morbidity measure. Rankings of the morbidity and multimorbidity measures were broadly similar for generalised linear models with log link and Poisson errors and for OLS estimation. It would be currently feasible to combine the results from our study with the data on the number of patients with each QOF disease, which is available on all practices in England, to calculate budgets for general practices to cover their primary care costs.multimorbidity; primary care; utilisation; costs; deprivation; budgets

The Reporting of Treatment Nonadherence and Its Associated Impact on Economic Evaluations Conducted Alongside Randomized Trials:A Systematic Review

AbstractObjectivesTo review trial-based economic evaluations, identifying 1) the proportion reporting adherence, 2) methods for assigning intervention costs according to adherence, 3) which participants were included in the economic analysis, and 4) statistical methods to estimate cost-effectiveness in those who adhered. We provide recommendations on handling nonadherence in economic evaluations.MethodsThe National Health Service Economic Evaluation Database was searched for recently published trials. We extracted information on the methods used to assign shared costs in the presence of nonadherence and methods to account for nonadherence in the economic analysis.ResultsNinety-six eligible trials were identified. For one-off interventions, 86% reported the number of participants initiating treatment. For recurring interventions, 56% and 73%, respectively, reported the number initiating and completing treatment, whereas 66% reported treatment intensity. Most studies (23 of 31 [74%] trials and 42 of 53 [79%] trials of one-off and recurring interventions, respectively) reported strict intention-to-treat or complete case analyses. A minority (3 of 31 [10%] and 7 of 53 [13%], respectively), however, performed a per-protocol analysis. No studies used statistical methods to adjust for nonadherence directly in the economic evaluation. Only 13 studies described patient-level allocation of intervention costs; there was variation in how fixed costs were assigned according to adherence.ConclusionsMost of the trials reported a measure of adherence, but reporting was not comprehensive. A nontrivial proportion of studies report a primary per-protocol analysis that potentially produces biased results. Alongside primary intention-to-treat analysis, statistical methods for obtaining an unbiased estimate of cost-effectiveness in adherers should be considered

Keep it simple? Predicting primary health care costs with clinical morbidity measures

AbstractModels of the determinants of individuals’ primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models

Keep it Simple? Predicting Primary Health Care Costs with Measures of Morbidity and Multimorbidity

In this paper we investigate the relationship between patients’ primary care costs (consultations, tests, drugs) and their age, gender, deprivation and alternative measures of their morbidity and multimorbidity. Such information is required in order to set capitation fees or budgets for general practices to cover their expenditure on providing primary care services. It is also useful to examine whether practices’ expenditure decisions vary equitably with patient characteristics

The impact of attrition on the representativeness of cohort studies of older people

Background: There are well-established risk factors, such as lower education, for attrition of study participants. Consequently, the representativeness of the cohort in a longitudinal study may deteriorate over time. Death is a common form of attrition in cohort studies of older people. The aim of this paper is to examine the effects of death and other forms of attrition on risk factor prevalence in the study cohort and the target population over time

Short- and long-term cost and utilization of health care resources in Parkinson's disease in the UK

Background: There is currently no robust long‐term data on costs of treating patients with Parkinson's disease. The objective of this study was to report levels of health care utilization and associated costs in the 10 years after diagnosis among PD patients in the United Kingdom. Methods: We undertook a retrospective population‐based cohort study using linked data from the UK Clinical Practice Research Datalink and Hospital Episode Statistics databases. Total health care costs of PD patients were compared with those of a control group of patients without PD selected using 1:1 propensity score matching based on age, sex, and comorbidity. Results: Between 1994 and 2013, 7271 PD patients who met study inclusion criteria were identified in linked Clinical Practice Research Datalink‐Hospital Episode Statistics; 7060 were matched with controls. The mean annual health care cost difference (at 2013 costs) between PD patients and controls was £2471 (US3716) per patient in the first year postdiagnosis (P < 0.001), increasing to £4004 (US6021) per patient (P &lt; 0.001) 10 years following diagnosis because of higher levels of use across all categories of health care utilization. Costs in patients with markers of advanced PD (ie, presence of levodopa‐equivalent daily dose &gt; 1100 mg, dyskinesias, falls, dementia, psychosis, hospital admission primarily due to PD, or nursing home placement) were on average higher by £1069 (US$1608) per patient than those with PD without these markers. Conclusions: This study provides comprehensive estimates of health care costs in PD patients based on routinely collected data. Health care costs attributable to PD increase in the year following diagnosis and are higher for patients with indicators of advanced disease