Conectar
by Claire L. Chan, Saskia Eddy, Jennie Hejdenberg, Ben Morgan, Heather M. Morgan, Gillian Lancaster, Clare Robinson, Sandra M. Eldridge
BackgroundThe National Institute for Health and Care Research accepts applications for pilot and feasibility studies to their Research for Patient Benefit (RfPB) programme. There has been limited work describing the design practices of these applications and funding status. Knowing some of the qualities which may contribute towards a pilot or feasibility study application successfully gaining funding could help researchers improve the quality of their applications. Therefore, this study describes the protocol for a review looking at the characteristics of funded and non-funded external pilot trial applications. In particular, the primary objective is to describe the planned sample size and sample size justifications.
MethodsThe study will be conducted on 100 applications from Competition 31–37 with a randomised feasibility design, identified and given access to us by RfPB where the lead applicant has consented. We will screen these applications to identify the external pilot trials, first looking through the titles and then the full text. Following this, we will extract data on information such as medical area, study design, objective(s), sample size, sample size justification, and funding outcome stage one and two. Validation will be performed on 20% of the data extracted; discrepancies will be resolved by discussion or a third reviewer will decide if there is no consensus. We will use descriptive statistics to summarise quantitative data, and will analyse qualitative data using thematic analysis. Findings will be summarised through discussion with the project contributors to produce a reader-friendly guidance document.
DiscussionThis work will provide a more complete picture of RfPB external randomised pilot and feasibility trials. The findings will assist researchers when planning their pilot trials, and could help improve the quality of submitted applications.
Protocol RegistrationOpen Science Framework protocol registration DOI: https://doi.org/10.17605/OSF.IO/PYKVG.
To determine if communication disorders (1) increase the risk for common mental and physical health conditions and (2) if risk varies by age of onset (≤25 years (developmental) or >25 years (acquired)) by using the large-scale All of Us Research Program participant-reported survey data to electronic health records (EHR) data. We hypothesised that adults with a communication disorder would have a higher risk of mental and physical health conditions.
A retrospective cross-sectional study.
Secondary analysis of EHR and online surveys conducted in the USA.
We assessed 410 360 US adults enrolled in the All of Us Research Program from August 2023 to May 2024 for study eligibility. We used medical diagnosis of a communication disorder from EHR data to group participants into communication disorder (CD) and typical communication (TC) groups, and age of first diagnosis to assign to age of onset (≤25 years (developmental) or >25 years (acquired)) groups. 234 519 participants (median (IQR) age 57.00 (41.00, 68.00); 3700 (1.6%) qualified for the CD group) were included in the analyses.
Primary outcome measures were diagnosis of 11 common mental and physical health conditions from EHR data.
Multiple logistic regression models with propensity score weighting revealed that participants with CD had higher odds for attention deficit hyperactivity disorder, anxiety, asthma, cancer, chronic kidney disease, cardiovascular disease, depression, diabetes and hypertension. Estimates for chronic kidney disease (acquired: adjusted OR (AOR), 1.89 (1.62, 2.20); developmental: AOR, 1.26 (0.42, 3.82)), diabetes (acquired: AOR, 1.64 (1.49, 1.81); developmental: AOR, 1.51 (0.95, 2.41)), hypertension (acquired: AOR, 2.02 (1.85, 2.19); developmental: AOR, 1.16 (0.80, 1.68)) and substance use (acquired: AOR, 1.76 (1.47, 2.12); developmental: AOR, 1.08 (0.65, 1.82)) varied by age of onset. Confounding factors are controlled in the analysis, such as age, income, employment, enrolment, sex at birth, gender identity and US census division.
Our study demonstrates that adults with CD experience health disparities compared with adults with TC, and that these disparities vary by age of onset of CD.
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