ABSTRACT
Background In the UK it is estimated that 10-15% of lung cancer cases occur in never-smokers. This study demonstrates the changes of the demographic characteristics, including the smoking status, of all the patients referred to the thoracic malignancy unit at Guy's Cancer Centre, South East London, between 2010 and 2021. Methods We included patients with a documented ICD10 diagnosis of bronchus and lung malignancy who were referred to Guy's thoracic malignancy unit from 2010 until 2021. A total of 6861 patients with a diagnosis of lung cancer were identified. We collected baseline demographic and clinical characteristics, including smoking status and socio-economic status for all the patients. Descriptive statistics were utilised to highlight the dynamic changes over the years of the referred patients. Results The number of referrals per year remained overall stable from 2010 until 2019, with a decrease in the number of referrals in 2020 and 2021, most likely due to the COVID-19 pandemic. We observed a gradual increase in the percentage of never smokers among the lung cancer patients: 5%, 8%, 10% and 13% of the referred patients were never smokers in the years 2010, 2015, 2018 and 2021 respectively. Median age remained stable across the years (range 68-71 years). Male percentage was 56%, 55%, 53% and 53% in 2010, 2015, 2018 and 2021 respectively. From the patients that we had a documented ethnic background the proportion of White/Black/Asian/Other or Mixed ethnicity remained stable across the years with a median 87%, 7%, 3%, and 3% respectively. The most common histological diagnosis was adenocarcinoma, followed by squamous cell carcinoma and small cell lung carcinoma. Conclusions The proportion of never-smoking to smoking related lung cancer has gradually increased between 2010 and 2021. There was little variability in age, sex and ethnic background. Never-smoking lung cancer is a distinct biological entity, therefore, further research should focus on the understanding of the aetiology and the risk factors leading to the development of lung cancer, in the absence of a history of tobacco exposure. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc.
ABSTRACT
The frequency and magnitude of natural disasters, especially floods, has been increasing in recent years and climate is expected to exacerbate these events. The increasing availability and number of satellites with continually improving spatial and temporal resolutions can provide efficient data sources to help with real-time disaster relief efforts from hazard assessment to rescue operations. Analyzing these volumes of data requires highly efficient algorithms that can produce fast and accurate results across the complex landscape. Artificial Intelligence (AI) provides an opportunity for repeatable, timely and reliable data processing. Once mapped, getting the spatial information to the users/decision-makers rapidly and in a format that is easily understandable and accessible has been a challenge. New methods of data sharing are emerging and gaining popularity. The widespread stability and availability of the internet has led to a surge in direct data access via Application Programming Interface (API) and interactive dashboards. Since the declaration of a global pandemic, there has been dozens of COVID-19 dashboards developed for tracking new cases. The overwhelming response to these dashboards has led to growth in the development of dashboards focused on assisting the emergency management community and first responders, as these dashboards can provide a common operating picture for multi-users operating from different locations. © 2021 IEEE