Multicentre service evaluation of presentation of newly diagnosed cancers and type 1 diabetes in children in the UK during the COVID-19 pandemic

Background: The COVID-19 pandemic led to changes in patterns of presentation to emergency departments. Child health professionals were concerned that this could contribute to the delayed diagnosis of life-threatening conditions, including childhood cancer (CC) and type 1 diabetes (T1DM). Our multicentre, UK-based service evaluation assessed diagnostic intervals and disease severity for these conditions.Methods: We collected presentation route, timing and disease severity for children with newly diagnosed CC in three principal treatment centres and T1DM in four centres between 1 January and 31 July 2020 and the corresponding period in 2019. Total diagnostic interval (TDI), patient interval (PI), system interval (SI) and disease severity across different time periods were compared.Results: For CCs and T1DM, the route to diagnosis and severity of illness at presentation were unchanged across all time periods. Diagnostic intervals for CCs during lockdown were comparable to that in 2019 (TDI 4.6, PI 1.1 and SI 2.1 weeks), except for an increased PI in January–March 2020 (median 2.7 weeks). Diagnostic intervals for T1DM during lockdown were similar to that in 2019 (TDI 16 vs 15 and PI 14 vs 14 days), except for an increased PI in January–March 2020 (median 21 days).Conclusions: There is no evidence of diagnostic delay or increased illness severity for CC or T1DM, during the first phase of the pandemic across the participating centres. This provides reassuring data for children and families with these life-changing conditions

Strategies for managing missing and incomplete information with applications to keystroke biometric data and a business analytical application

Missing Data Techniques (MDTs) are examined and placed into context by applying them to a keystroke biometric application and database, and then applying our findings to the broader topic of analytical applications. Multiple MDTs are examined in light of the nature and the amount of missing data and applied to the keystroke system through the vehicle of fallback models, a technique inspired by research in speech recognition. We conclude that heuristic-based MDTs are more effective than statistic-based techniques, that multiple imputations are more effective than single imputations, and that effectiveness overall deteriorates as the amount of missing data increases. Through the application of MDTs instantiated through fallback models, we were able to improve the performance of the keystroke system slightly, as determined by chi square analysis. Lastly, we apply our findings to the growing area of analytical applications and suggest a new, more thorough model in which the user is made aware of the nature and amount of missing data in a given data set, and is able to better and more intelligently manage that missing data through the selection of the most appropriate MDT. We develop and illustrate this new model through the creation of a feasibility simulation