Impact of the COVID-19 pandemic on the mental health and well-being of Veterans’ spouses: a cross sectional analysis

Background COVID-19 has negatively impacted the mental health and well-being of both Canadians and the world as a whole, with Veterans, in particular, showing increased rates of depression, anxiety, and PTSD. Spouses and common-law partners often serve as primary caregivers and sources of support for Veterans, which may have a deleterious effect on mental health and increase risk of burnout. Pandemic related stressors may increase burden and further exacerbate distress; yet the effect of the pandemic on the mental health and well-being of Veterans’ spouses is currently unknown. This study explores the self-reported mental health and well-being of a group of spouses of Canadian Armed Forces Veterans and their adoption of new ways to access healthcare remotely (telehealth), using baseline data from an ongoing longitudinal survey. Methods Between July 2020 and February 2021, 365 spouses of Veterans completed an online survey regarding their general mental health, lifestyle changes, and experiences relating to the COVID-19 pandemic. Also completed were questions relating to their use of and satisfaction with health-care treatment services during the pandemic. Results Reported rates of probable major depressive disorder (MDD), generalized anxiety disorder (GAD), alcohol use disorder (AUD), and PTSD were higher than the general public, with 50–61% believing their symptoms either directly related to or were made worse by the pandemic. Those reporting being exposed to COVID-19 were found to have significantly higher absolute scores on mental health measures than those reporting no exposure. Over 56% reported using telehealth during the pandemic, with over 70% stating they would continue its use post-pandemic. Conclusions This is the first Canadian study to examine the impact of the COVID-19 pandemic specifically on the mental health and well-being of Veterans’ spouses. Subjectively, the pandemic negatively affected the mental health of this group, however, the pre-pandemic rate for mental health issues in this population is unknown. These results have important implications pertaining to future avenues of research and clinical/programme development postpandemic, particularly relating to the potential need for increased support for spouses of Veterans, both as individuals and in their role as supports for Veterans

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data

PURPOSE Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. EXPERIMENTAL DESIGN Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. RESULTS Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. CONCLUSIONS Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples

Bowel cancer registry data made whole: filling in the blanks through imputation in Northern Ireland

In healthcare, cost-effectiveness analysis (CEA) compares alternative strategies based on consequences and costs to allocate healthcare resources to benefit public health. CEA modelling assembles components of costs, quality of life utilities and survival analysis. Survival analysis can project the lifetime of a simulated individual based on available data, therefore survival data is vital within CEA.Supplementary data requested from the Northern Ireland Cancer Registry (NICR) obtained outputs published in the Pathway to a Cancer Diagnosis report [1] in NI, to inform colorectal cancer (CRC) natural history contained within a larger CEA model. The proportion of individuals diagnosed with CRC was presented based on the route, stage, sex and age, with the proportions of individuals alive after 3, 6 and 12 months. Missingness existed within the data to protect the patient’s identity. If &lt; 10 individuals were diagnosed with CRC based on a specified route, age group, stage and sex, the data were omitted. Also, if &lt; 3 individuals died 3/6/12 months after diagnosis, the data were omitted. Most missing data problems are solved by Rubin’s multiple imputation methods [2]. However, this approach can be biased towards missing not-at-random data compared to missing at/completely at-random data; thus, other approaches are required.Three approaches were developed to impute the missing values. The first approach randomly generated values based on why the data was initially omitted. The second and third approaches used the NICR’s publicly available 1 and 5-year net survival rates (NSRs) for CRC, categorised by age, sex and stage, however, did not incorporate the same routes found in [1]. The second approach considered the lowest NSRs based on route, stage and age. The third approach randomly generated values within the range of possible NSRs, using both the normal and uniform distributions. The 5-year NSRs from NICR were used to estimate the proportions of individuals after 5 years, to better inform and extend survival within the CEA model. After comparing all imputation approaches with the true NICR 1-year NSRs, the most appropriate choice was the third approach, using the normal distribution. Using this approach, we can illustrate the lifetime of an individual within the CEA model and produce more plausible results.Reference:1.Bannon F, Harbinson A, Mayock M, McKenna H. Pathways to a Cancer Diagnosis: Monitoring variation in the patient journey across Northern Ireland 2012 to 2016.2.Rubin DB. Multiple imputations in sample surveys - a phenomenological Bayesian approach to nonresponse. American Statistical Association. 1978;1:20–34.<br/