Search

Background Adolescents and young adults (AYAs) diagnosed with cancer are at an increased risk of experiencing social isolation and loneliness secondary to their cancer and its treatment. The physical distancing measures implemented during the COVID-19 pandemic may have further increased loneliness among this group. This study examined the prevalence of loneliness and factors associated with loneliness among AYAs with cancer during this pandemic. Methods We conducted a self-administered, online, cross-sectional survey of Canadian AYAs diagnosed with cancer between 15 and 39 between January and February 2021. Loneliness was measured using the 3-item UCLA Loneliness Scale. Factors associated with higher levels of loneliness were identified using multiple logistic regression. Results The analysis included 805 AYAs. The prevalence of loneliness was 52.2% [N = 419, 95% CI (confidence interval) 48.7 to 55.6%]. Individuals who were 18–25 years old [AOR (adjusted odds ratio)1.60, CI 1.03–2.47, p = 0.035], currently undergoing cancer therapy (AOR 1.46, 95% CI 1.03–2.07, p = 0.035), who self-disclosed the presence of a pre-pandemic mental health condition (AOR 2.09, 95% CI = 1.22–3.58, p = 0.007), or were not in a relationship (AOR 2.22, 95% CI 1.57–3.14, p

We aimed to describe the negative and positive impacts of changes in cancer care delivery due to COVID-19 pandemic for adolescents and young adults (AYAs) in Canada, as well as the correlates of negative impact and their perspectives on optimization of cancer care. We conducted an online, self-administered survey of AYAs with cancer living in Canada between January and February 2021. Multiple logistic regression was used to identify factors associated with a negative impact on cancer care. Of the 805 participants, 173 (21.5%) experienced a negative impact on their cancer care including delays in diagnostic tests (11.9%), cancer treatment (11.4%), and appointments (11.1%). A prior diagnosis of mental or chronic physical health condition, an annual income of &lt, 20,000 CAD, ongoing cancer treatment, and province of residence were independently associated with a negative cancer care impact (p-value &lt, 0.05). The majority (n = 767, 95.2%) stated a positive impact of the changes to cancer care delivery, including the implementation of virtual healthcare visits (n = 601, 74.6%). Pandemic-related changes in cancer care delivery have unfavorably and favorably influenced AYAs with cancer. Interventions to support AYAs who are more vulnerable to the adverse effects of the pandemic, and the thoughtful integration of virtual care into cancer care delivery models is essential.

Background Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination. Methods We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons. Results Based on 2 case definitions, we estimate between 0.42−3.2% and 0.33−1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08−0.61% and 0.07−0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32−2.4 million in 2011-2012 and 1.8−8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4−34 million in 2011-2012 and 23−102 million in 2012-2013. Conclusions We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.

Next generation sequencing technologies have made it possible to investigate the role of rare variants (RVs) in disease etiology. Because RVs associated with disease susceptibility tend to be enriched in families with affected individuals, study designs based on affected sib pairs (ASP) can be more powerful than case–control studies. We construct tests of RV‐set association in ASPs for single genomic regions as well as for multiple regions. Single‐region tests can efficiently detect a gene region harboring susceptibility variants, while multiple‐region extensions are meant to capture signals dispersed across a biological pathway, potentially as a result of locus heterogeneity. Within ascertained ASPs, the test statistics contrast the frequencies of duplicate rare alleles (usually appearing on a shared haplotype) against frequencies of a single rare allele copy (appearing on a nonshared haplotype); we call these allelic parity tests. Incorporation of minor allele frequency estimates from reference populations can markedly improve test efficiency. Under various genetic penetrance models, application of the tests in simulated ASP data sets demonstrates good type I error properties as well as power gains over approaches that regress ASP rare allele counts on sharing state, especially in small samples. We discuss robustness of the allelic parity methods to the presence of genetic linkage, misspecification of reference population allele frequencies, sequencing error and de novo mutations, and population stratification. As proof of principle, we apply single‐ and multiple‐region tests in a motivating study data set consisting of whole exome sequencing of sisters ascertained with early onset breast cancer.

This study investigated prevalence of psychological distress, factors associated with distress, and experiences of Adolescents and Young Adults (AYAs) with cancer during the COVID-19 pandemic. It also compared distress in this group to previously surveyed Canadian AYAs with cancer in 2018 by the Young Adults with Cancer in their Prime (YACPRIME) study.A cross-sectional, online, self-administered survey of AYAs diagnosed with cancer between 15 and 39 years of age was conducted. Psychological distress was measured by the Kessler Psychological Distress Scale (K10). Associations between variables and high psychological distress (K10 ≥ 25), and comparison of prevalence of psychological distress with the YACPRIME study were done using multivariable logistic regression. Summative qualitative content analysis analyzed participant experiences during this pandemic.We included 805 participants. High psychological distress was present in over two-thirds of the group (68.0%; 95% CI, 64.7%-71.2%). Employment impact during pandemic (AOR (adjusted odds ratio), 2.16; 95% CI, 1.41-3.31) and hematologic malignancy (AOR, 1.76; 95% CI 1.08-2.97) were associated with higher psychological distress, while older age [AOR, 0.95; 95% CI, 0.92-0.99] and personal income$40,000 (AOR, 0.38; 95% CI, 0.24-0.58) were associated with lower distress. Adjusted odds of experiencing psychological distress among AYAs with cancer during pandemic compared to pre-pandemic years was 1.85 (95% CI: 1.36-2.53). Overarching themes of pandemic experiences included: inferior quality of life, impairment of cancer care, COVID-19 related concerns and extreme social isolation.AYAs diagnosed with cancer are experiencing high psychological distress during this pandemic. Distress screening and evidence-based interventions to alleviate distress are essential.

Properties of statistical alarms have been well studied for simple disease surveillance models, such as normally distributed incidence rates with a sudden or gradual shift in mean at the start of an outbreak. It is known, however, that outbreak dynamics in human populations depend significantly on the heterogeneity of the underlying contact network. The rate of change in incidence for a disease such as influenza peaks early on during the outbreak, when the most highly connected individuals get infected, and declines as the average number of connections in the remaining susceptible population drops. Alarm systems currently in use for detecting the start of influenza seasons generally ignore this mechanism of disease spread, and, as a result, will miss out on some early warning signals. We investigate the performance of various alarms on epidemics simulated from an undirected network model with a power law degree distribution for a pathogen with a relatively short infectious period. We propose simple custom alarms for the disease system considered, and show that they can detect a change in the process sooner than some traditional alarms. Finally, we test our methods on observed rates of influenza-like illness from two sentinel providers (one French, one Spanish) to illustrate their use in the early detection of the flu season.

Background There has been significant interest in investigating genome-wide and epigenome-wide associations with lipids. Testing at the gene or region level may improve power in such studies. Methods We analyze chromosome 11 cytosine-phosphate-guanine (CpG) methylation levels and single-nucleotide polymorphism (SNP) genotypes from the original Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, aiming to explore the association between triglyceride levels and genetic/epigenetic factors. We apply region-based tests of association to methylation and genotype data, in turn, which seek to increase power by reducing the dimension of the gene-region variables. We also investigate whether integrating 2 omics data sets (methylation and genotype) into the triglyceride association analysis helps or hinders detection of candidate gene regions. Results Gene-region testing identified 1 CpG region that had been previously reported in the GOLDN study data and another 2 gene regions that are also associated with triglyceride levels. Testing on the combined genetic and epigenetic data detected the same genes as using epigenetic or genetic data alone. Conclusions Region-based testing can uncover additional association signals beyond those detected using single-variant testing.

Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.

Objectives: This article articulates the complexity of modeling in First Nations, Metis, and Inuit contexts by providing the results of a modeling exercise completed at the request of the First Nations Health and Social Secretariat of Manitoba. Methods: We developed a model using the impact of a previous pandemic (the 2009 H1N1) to generate estimates. Results: The lack of readily available data has resulted in a model that assumes homogeneity of communities in terms of health status, behaviour, and infrastructure limitations. While homogeneity may be a reasonable assumption for province-wide planning, First Nation communities and Tribal Councils require more precise information in order to plan effectively. Metis and urban Inuit communities, in contrast, have access to much less information, making the role of Indigenous organizations mandated to serve the needs of these populations that much more difficult. Conclusion: For many years, Indigenous organizations have advocated for the need to have access to current and precise data to meet their needs. The COVID-19 pandemic demonstrates the importance of timely and accurate community-based data to support pandemic responses.