Objective: This study uses three linked datasets to provide an estimate of incidence of motor neuron disease (MND) in England from 1998 to 2019. Comparison is made to previous British studies. It examines age at diagnosis and ethnicity of those affected. Methods: The literature was searched for studies of MND incidence in Great Britain from 1995 to date. The QResearch and linked Hospital Episode Statistics and Death register databases were searched from 1998 to 2019 for cases of MND, and incidence calculated from 16.8 million adults and 112 million adult years of data. Results: We found 6437 adults with a diagnosis of MND giving an incidence of MND of 5.69/100,000 person years (95% CI 5.51–5.88); 6.57 (6.41–6.99) in men and 4.72 (4.49–4.97) in women when age-standardized to the 2011 UK population. The median age of diagnosis was 72 years. Peak incidence occurred in the 80–84 year age group in men and 75–79 in women. Age-standardized incidence was as high in Bangladeshi, Black Caribbean, Indian, other Asian and Pakistani people as in White people. Black African and Chinese people had a lower incidence. Conclusion: The use of three linked national datasets captured 33% more people than a primary care dataset alone. Patients were older than in previous studies and rates were high in all ethnic groups studied except Black African and Chinese people. We present the highest incidence of MND reported globally in the past 50 years. Methodological differences may in part explain differences with previous reports. The use of national datasets may have captured additional MND patients with serious comorbidities who have not seen a neurologist before death. A limitation of this approach is that unlike population registers, which minimize false positive diagnosis by neurologist review of each patient, we cannot review diagnosis for individuals as data are anonymized.

IntroductionHormone replacement therapy (HRT) can help women experiencing menopausal symptoms, but usage has declined due to uncertainty around risks of cancer and some cardiovascular diseases (CVD). Moreover, improved cancer survival rates mean that more women who survive cancer go on to experience menopausal symptoms. Understanding these relationships is important so that women and their clinicians can make informed decisions around the risks and benefits of HRT. This study’s primary aim is to determine the association between HRT use after cancer diagnosis and the risk of cancer-specific mortality. The secondary aims are to investigate the risks of HRT on subsequent cancer, all-cause mortality and CVD.Methods and analysisWe will conduct a population-based longitudinal cohort study of 18–79 year-old women diagnosed with cancer between 1998 and 2020, using the QResearch database. The main exposure is HRT use, categorised based on compound, dose and route of administration, and modelled as a time-varying covariate. Analysis of HRT use precancer and postcancer diagnosis will be conducted separately. The primary outcome is cancer-specific mortality, which will be stratified by cancer site. Secondary outcomes include subsequent cancer diagnosis, CVD (including venous thrombo-embolism) and all-cause mortality. Adjustment will be made for key confounders such as age, body mass index, ethnicity, deprivation index, comorbidities, and cancer grade, stage and treatment. Statistical analysis will include descriptive statistics and Cox proportional hazards models to calculate HRs and 95% CIs.Ethics and disseminationEthical approval for this project was obtained from the QResearch Scientific Committee (Ref: OX24, project title ‘Use of hormone replacement therapy and survival from cancer’). This project has been, and will continue to be, supported by patient and public involvement panels. We intend to the submit the findings for peer-reviewed publication in an academic journal and disseminate them to the public through Cancer Research UK.

Background and research aim: Lung cancer is a research priority in the UK. Early diagnosis of lung cancer can improve patients' survival outcomes. The DART-QResearch project is part of a larger academic-industrial collaborative initiative, using big data and artificial intelligence to improve patient outcomes with thoracic diseases. There are two general research aims in the DART-QResearch project: (1) to understand the natural history of lung cancer, (2) to develop, validate, and evaluate risk prediction models to select patients at high risk for lung cancer screening. Methods: This population-based cohort study uses the QResearch database (version 45) and includes patients aged between 25 and 84 years old and without a diagnosis of lung cancer at cohort entry (study period: 1 January 2005 to 31 December 2020). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. The following statistical techniques will be used for different research objectives, including descriptive statistics, multi-level modelling, multiple imputation for missing data, fractional polynomials to explore non-linear relationships between continuous variables and the outcome, and Cox regression for the prediction model. We will update our QCancer (lung, 10-year risk) algorithm, and compare it with the other two mainstream models (LLP and PLCOM2012) for lung cancer screening using the same dataset. We will evaluate the discrimination, calibration, and clinical usefulness of the prediction models, and recommend the best one for lung cancer screening for the English primary care population. Discussion: The DART-QResearch project focuses on both symptomatic presentation and asymptomatic patients in the lung cancer care pathway. A better understanding of the patterns, trajectories, and phenotypes of symptomatic presentation may help GPs consider lung cancer earlier. Screening asymptomatic patients at high risk is another route to achieve earlier diagnosis of lung cancer. The strengths of this study include using large-scale representative population-based clinical data, robust methodology, and a transparent research process. This project has great potential to contribute to the national cancer strategic plan and yields substantial public and societal benefits through earlier diagnosis of lung cancer.

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ObjectiveTo confirm the symptoms and signs for motor neuron disease (MND) in the Red Flag tool; to quantify the extent to which the key symptoms and signs are associated with MND; and to identify additional factors which may be helpful within the primary care setting in recognition of possible MND and triggering timely referral to neurology specialists.DesignA nested case–control study.Setting1292 UK general practices contributing to the QResearch primary care database, linked to hospital and mortality data.ParticipantsBaseline cohort included 16.8 million individuals aged 18 years and over without a diagnosis of MND at study entry and with more than 3 years of digitalised information available. The nested case–control data set comprised of 6437 cases of MND diagnosed between January 1998 and December 2019, matched by year of birth, gender, general practice and calendar year to 62 003 controls.Main outcome measuresClinically recognised symptoms and signs of MND prior to diagnosis and symptoms and factors which are relevant in primary care setting.ResultsThis study identified 17 signs and symptoms that were independently associated with MND diagnosis in a multivariable analysis. Of these, seven were new to the Red Flag tool: ataxia, dysphasia, weight loss, wheeze, hoarseness of voice, urinary incontinence and constipation. Among those from the Red Flag tool, dysarthria had the strongest association with subsequent MND (adjusted OR (aOR): 43.2 (95% CI 36.0 to 52.0)) followed by muscle fasciculations (aOR: 40.2 (95% CI 25.6 to 63.1)) and muscle wasting (aOR: 31.0 (95% CI 19.5 to 49.4)). Additionally, the associations between MND diagnosis and family history, dropped foot, focal weakness and sialorrhoea remained robust after controlling for confounders. Patients who reported symptoms indicative of damage to the lower brainstem and its connections were diagnosed sooner than those who presented with respiratory or cognitive signs.ConclusionThis is the first study that has identified, confirmed and quantified the association of key symptoms and signs with MND diagnosis. In addition to known factors, the study has identified the following new factors to be independently associated with MND prior to diagnosis: ataxia, dysphasia, wheeze and hoarseness of voice. These findings may be used to improve risk stratification and earlier detection of MND in primary care.