Automating incidence and prevalence analysis in open cohorts (2024)

Type of publication:
Journal article

Author(s):
Cockburn N.; Hammond B.; Gani I.; Cusworth S.; Acharya A.; Gokhale K.; Thayakaran R.; Crowe F.; Minhas S.; *Smith W.P.; Taylor B.; Nirantharakumar K.; Chandan J.S.;

Citation:
BMC medical research methodology. 24(1) (pp 144), 2024. Date of Publication: 04 Jul 2024.

Abstract:
MOTIVATION: Data is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses. IMPLEMENTATION: We provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later. GENERAL FEATURES: The Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence. AVAILABILITY: The command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication .

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Automating incidence and prevalence analysis in open cohorts (2024)

Type of publication:
Journal article

Author(s):
Cockburn, Neil; Hammond, Ben; Gani, Illin; Cusworth, Samuel; Acharya, Aditya; Gokhale, Krishna; Thayakaran, Rasiah; Crowe, Francesca; Minhas, Sonica; *Smith, William Parry; Taylor, Beck; Nirantharakumar, Krishnarajah; Chandan, Joht Singh.

Citation:
BMC Medical Research Methodology. 24(1):144, 2024 Jul 04.

Abstract:
MOTIVATION: Data is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses. IMPLEMENTATION: We provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later. GENERAL FEATURES: The Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence. AVAILABILITY: The command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication

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The development and acceptability of an educational and training intervention for recruiters to neonatal trials: the TRAIN project (2023)

Type of publication:Journal article

Author(s):Smith, V; Delaney, H; Hunter, A; Torgerson, D; Treweek, S; Gamble, C; Mills, N; Stanbury, K; Dempsey, E; Daly, M; O'Shea, J; Weatherup, K; *Deshpande, S; Ryan, M A; Lowe, J; Black, G; Devane, D.

Citation:BMC Medical Research Methodology. 23(1):265, 2023 Nov 11.

Abstract:BACKGROUND: Suboptimal or slow recruitment affects 30-50% of trials. Education and training of trial recruiters has been identified as one strategy for potentially boosting recruitment to randomised controlled trials (hereafter referred to as trials). The Training tRial recruiters, An educational INtervention (TRAIN) project was established to develop and assess the acceptability of an education and training intervention for recruiters to neonatal trials. In this paper, we report the development and acceptability of TRAIN. METHODS: TRAIN involved three sequential phases, with each phase contributing information to the subsequent phase(s). These phases were 1) evidence synthesis (systematic review of the effectiveness of training interventions and a content analysis of the format, content, and delivery of identified interventions), 2) intervention development using a Partnership (co-design/co-creation) approach, and 3) intervention acceptability assessments with recruiters to neonatal trials. RESULTS: TRAIN, accompanied by a comprehensive intervention manual, has been designed for online or in-person delivery. TRAIN can be offered to recruiters before trial recruitment begins or as refresher sessions during a trial. The intervention consists of five core learning outcomes which are addressed across three core training units. These units are the trial protocol (Unit 1, 50 min, trial-specific), understanding randomisation (Unit 2, 5 min, trial-generic) and approaching and engaging with parents (Unit 3, 70 min, trial-generic). Eleven recruiters to neonatal trials registered to attend the acceptability assessment training workshops, although only four took part. All four positively valued the training Units and resources for increasing recruiter preparedness, knowledge, and confidence. More flexibility in how the training is facilitated, however, was noted (e.g., training divided across two workshops of shorter duration). Units 2 and 3 were considered beneficial to incorporate into Good Clinical Practice Training or as part of induction training for new staff joining neonatal units. CONCLUSION: TRAIN offers a comprehensive co-produced training and education intervention for recruiters to neonatal trials. TRAIN was deemed acceptable, with minor modification, to neonatal trial recruiters. The small number of recruiters taking part in the acceptability assessment is a limitation. Scale-up of TRAIN with formal piloting and testing foreffectiveness in a large cluster randomised trial is required.

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Rationalisation of capturing data for a participant’s research journey (2023)

Type of publication:
Service improvement case study

Author(s):
*Rachel Rikunenko

Citation:
SaTH Improvement Hub, 2023

Abstract:
In order to reduce duplication of data during a participant’s research journey a review of the use of Excel Spreadsheets versus the EDGE Local Performance Management System (LPMS) was completed. The EDGE LPMS can provide 100% of the same functionality as Excel Spreadsheets but in different formats. It can also provide a clear audit trial; reducing GDPR breaches and aiding reporting of a participant’s research journey. However, there are some concerns about using EDGE alone. Data was duplicated for 70% of research projects.

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The rise in trauma & orthopaedic trainee-led research and audit collaborative projects in the United Kingdom since the start of the COVID-19 pandemic (2023)

Type of publication:
Journal article

Author(s):
*Khaleeq T.; *Kabariti R.; *Ahmed U.

Citation:
Pakistan Journal of Medical Sciences. 39(3) (pp 769-774), 2023. Date of Publication: May – June 2023.

Abstract:
Background and Objective: A significant increase has been observed globally in multi-centre trainee-led trauma & orthopaedic (T&O) research collaborative projects with more emphasis have been on tackling important research questions since the start of the COCID-19 pandemic. The objective of our analysis was to determine the number of trainee-led research collaborative projects in T&O in the United Kingdom that were started during the COVID-19 pandemic. Method(s): A retrospective analysis was conducted to determine how many trainee-led national collaborative projects in T&O were conducted since the start of the COVID-19 pandemic lockdown (March 2020 to June 2021) and the number of projects identified were compared to the previous year (2019). Any regional collaborative projects, projects that were started before the onset of COVID and projects of other surgical specialities were not included in the study. Result(s): There were no projects identified in 2019 while in the Covid pandemic lockdown we identified 10 trainee-led collaborative trauma & orthopaedic projects with six of them being published with level of evidence from three to four. Conclusion(s): Covid was unprecedented and has placed considerable trials across healthcare. Our study highlights an increase in multi-centre trainee-led collaborative projects within the UK and it underlines the feasibility of such projects especially with the advent of social media and Redcap which facilitate recruitment of new studies and data.

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