The economic burden and determinants of healthcare costs of back pain in the UK : an empirical investigation using electronic health records

Back pain is a common health problem globally and it imposes great costs associated with its treatment. In the UK, the healthcare costs associated with back pain were last estimated 20 years ago in a cost-of-illness (COI) study. Given the aging population worldwide, these costs are likely to rise putting further pressure on healthcare services. The use of robust, and more advanced econometric methods to exploit national electronic health records (EHRs) may present up-to-date and more precise estimates for the healthcare costs of back pain. This thesis utilises such data source and methods to estimate the consultations, and prescriptions costs of back pain and to investigate factors associated with these costs. The systematic review of the literature assessed the methodologies used in COI studies of back pain. The vast majority of studies used a direct method of summing up back pain related costs which underestimated the true cost of back pain compared to an incremental cost approach. The latter approach which is conducted using a matched control or regression-based methods is more accurate, and comprehensive. This thesis further explored potential data sources that could be utilised for estimating healthcare resource use and costs of back pain in the UK. The Health Improvement Network (THIN), one of the two most widely used primary care databases in clinical research, was identified providing complete records of consultations and prescriptions data. Matched control studies, using propensity score matching, were conducted to estimate annual healthcare costs (2011-2015), and to assess how estimates vary by socio-economic factors, and over time. The incremental costs obtained in comparison with a similar reference population enabled the risk of confounding effects due to differences in baseline characteristics to be minimised. The thesis further evaluated several alternative econometric methods used in modelling healthcare cost data. A cross-sectional study was then conducted to assess factors associated with healthcare costs of back pain. Regression analysis methods applied included OLS, log transformed OLS, generalised linear models (GLMs), extended estimating equations (EEE) model, and a quantile regression approach. How well the alternative estimators performed in terms of bias, accuracy and goodness-of-fit was compared by examining regression diagnostics, and predictive performance of the models. The findings demonstrate the need for researchers to examine their assumptions about the most appropriate model for analysing healthcare cost data.