The use of sequence analysis to study primary care pathways: an exploratory study of people at high risk of lung cancer in England

Research background, gaps, and aim: Lung cancer (LC) is a research priority in the UK, due to its high incidence and mortality, and poor survival. Current population-based studies are focused on investigating ‘route to diagnosis’, factors associated with late diagnosis and poor survival, and the implications of different intervals (e.g. primary care interval, diagnostic interval, treatment interval) in the cancer care pathway. However, the longitudinal sequence of interdependent patient-GP events over time (patient’s help-seeking behaviours and general practitioner (GP) management) preceding cancer diagnosis is a research area less investigated. Therefore, this PhD study proposes a new perspective of studying primary care sequences for early diagnosis research, using a novel statistical method – sequence analysis (SA), to identify meaningful typologies in a less investigated population – patients at high risk but not yet diagnosed with LC. Methodology: A systematic scoping review was conducted to understand how SA has been applied to study disease trajectories and care pathways in health services research, to learn the lessons from published studies and inform the application of SA in the main study. - Study design, setting, and participants: 899 community patients at high risk of developing LC (based on patient's smoking history) but not yet diagnosed with LC from eight general practices in the South coast of England consented to participate in this study. Their primary care records from June 2010 to October 2012 (29 months) were reviewed. Information was extracted from GP notes in free text and transcribed manually. - Research process: Two study phases, methodological exploration and empirical analysis, were involved to address three research objectives: how to construct primary care sequences from discrete health events, how to use different features of SA to obtain meaningful cluster patterns (the outcome of SA), and how patients’ sociodemographic and