High-Level Mobility Assessment Tool Normative Values for Children.

BACKGROUND: Physical therapists need to be able to evaluate high-level gross motor skills of children to determine their capacity to engage in activities such as running, jumping, hopping, and stair climbing. The High-Level Mobility Assessment Tool (HiMAT) has excellent interrater and retest reliability and is less susceptible to a ceiling effect than existing mobility scales in children who are 6 to 17 years old and have traumatic brain injury. OBJECTIVE: The purposes of this study were to develop normative HiMAT score ranges for Australian children and to investigate the relationship between children's HiMAT scores and their age, height, weight, and body mass index (BMI). DESIGN: This study used a cross-sectional design. METHODS: Children included in this study were 5 to 12 years old, had no condition affecting their mobility, could follow 2-stage instructions, and had written informed consent from their parent or guardian. A total 1091 children were assessed at their local school, where their height, weight, and HiMAT score were recorded. The relationships between children's age, height, weight, and BMI were summarized using Spearman rank correlations. Truncated regression models were used to determine the most appropriate predictor variable for developing sex-specific normative ranges. RESULTS: There was a positive correlation between children's HiMAT scores and their age, height, weight, and BMI. Age explained the most variability in HiMAT scores for both boys and girls. LIMITATIONS: The reliability, validity, and responsiveness of the HiMAT have not been tested across a broad range of children with mobility limitations. Normative data reported in this study are for Australian children only. CONCLUSIONS: HiMAT scores for children in this study increased with age, height, weight, and BMI. Age was the most appropriate variable for developing a normative dataset of HiMAT scores for children of primary school age.

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

OBJECTIVES: To 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. METHODS: The 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. RESULTS: Ninety-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. CONCLUSIONS: Most 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.

Models 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.

Implications of comorbidity for primary care costs in the UK: a retrospective observational study.

BACKGROUND: Comorbidity is increasingly common in primary care. The cost implications for patient care and budgetary management are unclear. AIM: To investigate whether caring for patients with specific disease combinations increases or decreases primary care costs compared with treating separate patients with one condition each. DESIGN: Retrospective observational study using data on 86 100 patients in the General Practice Research Database. METHOD: Annual primary care cost was estimated for each patient including consultations, medication, and investigations. Patients with comorbidity were defined as those with a current diagnosis of more than one chronic condition in the Quality and Outcomes Framework. Multiple regression modelling was used to identify, for three age groups, disease combinations that increase (cost-increasing) or decrease (cost-limiting) cost compared with treating each condition separately. RESULTS: Twenty per cent of patients had at least two chronic conditions. All conditions were found to be both cost-increasing and cost-limiting when co-occurring with other conditions except dementia, which is only cost-limiting. Depression is the most important cost-increasing condition when co-occurring with a range of conditions. Hypertension is cost-limiting, particularly when co-occurring with other cardiovascular conditions. CONCLUSION: Three categories of comorbidity emerge, those that are: cost-increasing, mainly due to a combination of depression with physical comorbidity; cost-limiting because treatment for the conditions overlap; and cost-limiting for no apparent reason but possibly because of inadequate care. These results can contribute to efficient and effective management of chronic conditions in primary care.