Research

The OBS UK Dashboard: an interactive tool for representative trial site selection to facilitate equality and diversity in maternity research (2024)

Posted on by

Type of publication:

Journal article

Author(s):

*Elsmore, Amy; Rai, Tanvi; Pallmann, Philip; Townson, Julia; Kotecha, Sarah; Black, Mairead; Sanders, Julia; Collis, Rachel; Collins, Peter; Karunakaran, Bala; Wu, Pensee; Bell, Sarah; *Parry-Smith, William

Citation:

Trials. 25(1):629, 2024 Sep 27.

Abstract:

BACKGROUND: Obstetric Bleeding Study UK (OBS UK) (award ID: 152057) is a National Institute for Health and Care Research (NIHR)-funded stepped wedge cluster randomised controlled trial of a complex intervention for postpartum haemorrhage. This was developed in Wales and evaluated in a feasibility study, with improvements in maternal outcomes observed. Generalisability of the findings is limited by lack of control data and limited ethnic diversity in the Welsh obstetric patient population compared to the United Kingdom (UK): 94% of the Welsh population identifies as White, versus 82% in the UK. Non-White ethnicity and socioeconomic deprivation are linked to increased risk of adverse maternal outcomes. traditionally, decisions regarding site selection are based on desire to complete trials on target in 'tried and tested' research active institutions. To ensure widespread applicability of the results and investigate the impact of ethnicity and social deprivation on trial outcomes, maternity units were recruited that represent the ethnic diversity and social deprivation profiles of the UK. METHOD: Using routinely collected, publicly available data, an interactive dashboard was developed that demonstrates the demographics of the population served by each maternity unit in the UK, to inform site recruitment. Data on births per year, ethnic and socioeconomic group of the population for each maternity unit, across the UK, were integrated into the dashboard. RESULTS: The dashboard demonstrates that OBS UK trial sites reflect the ethnic and socioeconomic diversity of the UK (study vs UK population ethnicity: White 79.2% vs 81.7%, Asian 10.5% vs 9.3%, Black 4.7% vs 4.0%, Mixed 2.5% vs 2.9%, Other 3.0% vs 2.1%) with variation in site demography, size and location. Missing data varied across sites and nations and is presented. CONCLUSION: The NIHR equality, diversity and inclusion strategy states studies must widen

Link to full-text [open access - no password required]

Automating incidence and prevalence analysis in open cohorts (2024)

Posted on by

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 .

Link to full-text [open access - no password required]

Automating incidence and prevalence analysis in open cohorts (2024)

Posted on by

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

Link to full-text [open access - no password required]

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

Posted on by

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.

Link to full-text [open access - no password required]

Altmetrics:

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

Posted on by

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.

Link to PDF poster [no password required]