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3. Assessing Asthma control in UK primary care: Use of routinely collected prospective observational consultation data to determine appropriateness of a variety of control assessment models

Author

Norman Paul D, Jackson Cathy, Williams Brian, Hoskins Gaylor, and Donnan Peter T

Subjects
Medicine (General), R5-920
Abstract

Abstract Background Assessing asthma control using standardised questionnaires is recommended as good clinical practice but there is little evidence validating their use within primary care. There is however, strong empirical evidence to indicate that age, weight, gender, smoking, symptom pattern, medication use, health service resource use, geographical location, deprivation, and organisational issues, are factors strongly associated with asthma control. A good control measure is therefore one whose variation is most explained by these factors. Method Eight binary (Yes = poor control, No = good control) models of asthma control were constructed from a large UK primary care dataset: the Royal College of Physicians 3-Questions (RCP-3Qs); the Jones Morbidity Index; three composite measures; three single component models. Accounting for practice clustering of patients, we investigated the effects of each model for assessing control. The binary models were assessed for goodness-of-fit statistics using Pseudo R-squared and Akaikes Information Criteria (AIC), and for performance using Area Under the Receiver Operator Characteristic (AUROC). In addition, an expanded RCP-3Q control scale (0-9) was derived and assessed with linear modelling. The analysis identified which model was best explained by the independent variables and thus could be considered a good model of control assessment. Results 1,205 practices provided information on 64,929 patients aged 13+ years. The RCP-3Q model provided the best fit statistically, with a Pseudo R-squared of 18%, and an AUROC of 0.79. By contrast, the composite model based on the GINA definition of controlled asthma had a higher AIC, an AUROC of 0.72, and only 10% variability explained. In addition, although the Peak Expiratory Flow Rate (PEFR) model had the lowest AIC, it had an AUROC of 71% and only 6% of variability explained. However, compared with the RCP-3Qs binary model, the linear RCP-3Q Total Score Model (Scale 0-9), was found to be a more robust 'tool' for assessing asthma control with a lower AIC (28,6163) and an R-squared of 33%. Conclusion In the absence of a gold standard for assessing asthma control in primary care, the results indicate that the RCP-3Qs is an effective control assessment tool but, for maximum effect, the expanded scoring model should be used.

Published
2011
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6. Causal inference methods and simulation approaches in observational health research within a geographical framework

Author

Berrie, Lauren, Gilthorpe, Mark S., Norman, Paul D., and Baxter, Paul D.

Subjects
610
Abstract

Statistical methods are often used habitually, perhaps without sufficient reflection on their robustness in a range of novel circumstances. Increasingly, there is a desire to unravel the complexities of humans interacting with their environments, to improve our understanding and explanation of what influences population health in the wider context of our living environment. A framework is provided for using simulation and causal inference methods to evaluate analytical approaches in health geography, to introduce the reader to some of the considerations around complexity of context and data generation that may need to be reflected upon carefully when applying such methods in their own work. These methods have the potential to aid researchers in their explanation of what factors are important for population health and well-being in the context of our geographical environment while avoiding potential pitfalls in their work and allowing for greater critical evaluation of the methods employed by themselves and others. This thesis considers the utility of simulation to investigate applied problems related to mathematical coupling and specific considerations that need to be made in relation to research on the relationship between limiting long-term illness and deprivation and the challenges encountered while investigating the relationship between population mixing and childhood leukaemia -with all such considerations examined through the lens of cause and effect. The datasets chosen are representative of many others in health geography and span the full range of outcome prevalence rates likely encountered. Methods in causal inference and simulation are demonstrated to be powerful tools in understanding potential bias in research analyses. With careful planning, forethought and reflection on the data generating processes of the context of interest, causal inference and simulation methodologies are accessible to all researchers to improve their understanding of the methods they employ to address the research questions they pose.

Published
2019

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