Publications by authors named "T M Woodcock"
Background: Better continuity in primary and secondary care is linked to improved health outcomes, but it is unclear whether the sociodemographic determinants of continuity are the same in both settings and whether continuity measures in each setting are associated.
Aim: To examine the determinants of relational continuity in general practice (GP) and fragmented outpatient specialty care in people with clusters of Multiple Long-Term Conditions (LTCs) and the association between continuity in each setting.
Design And Setting: A cohort of patients ≥18 years registered to general practices in England throughout 2019, and with linked hospital outpatient records.
Background: Polypharmacy, prescription of multiple medications to a patient, is a major challenge for health systems. There have been no peer-reviewed studies of polypharmacy prevalence and medication cost at a population level in England.
Aims: To determine prevalence and medication cost of polypharmacy, by patient characteristics.
Objectives: To determine demographic and clinical characteristics associated with uptake of COVID-19 vaccines among pregnant women, and quantify the relationship between vaccine uptake and admission to hospital for COVID-19.
Background: Pregnant women are at increased risk of severe adverse outcomes from COVID-19. Since April 2021, COVID-19 vaccines were recommended for pregnant women in the UK.
Background: Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time.
Methods: We use electronic health records from over ten million people with multimorbidity registered to primary care in England.
Objective: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes.
Materials And Methods: This cohort study used primary care EHRs from 6 286 233 people with Multiple Long-Term Conditions in England.