@article {35759, title = {Examining the prevalence of depression in coronary artery disease patients: a cross-sectional analysis}, journal = {Journal of Emerging Investigators}, year = {2024}, abstract = {Coronary artery disease (CAD) is the narrowing or blockage of heart arteries due to atherosclerosis, an accumulation of fatty materials on the inner linings of arteries. CAD, also called coronary heart disease or heart disease, includes both angina and myocardial infarction. Many research studies have indicated a connection between depression and a heightened risk of chronic diseases, encompassing CAD. Our hypothesis suggests an association between CAD and depression, indicating that CAD patients may be at risk of depressive symptoms. This research aimed to assess depression prevalence and associated risk factors in CAD patients at a tertiary care hospital in Pakistan. A total of 360 patients were included in the study. The analysis, utilizing the Urdu version of the Patient Health Questionnaire-9 (PHQ-9), revealed a point prevalence of depression at 27\% in the overall sample. Of those screened positive, 71 were males (25\%) and 27 were females (36\%). Various factors such as old age, lower literacy levels, unemployment, rural residence, and comorbidities exhibited a positive association with depression. These findings highlight the significant prevalence of depression among CAD patients, emphasizing the need for increased awareness among treating physicians and cardiologists. Recognizing and effectively managing this comorbidity is crucial for comprehensive patient care. This study contributes valuable insights to the understanding of the psychological well-being of CAD patients in the Pakistani healthcare context for further research and targeted interventions.}, url = {http://dx.doi.org/10.59720/24-059}, author = {Muhammad Bin Saqib Qureshi and Jeremy VanderDoes and Kiran Saqib} } @article {35756, title = {Exploring the syndemic impact of COVID-19 and mental health on health services utilisation among adult Ontario population}, journal = {Public Health}, volume = {236}, year = {2024}, pages = {70-77}, abstract = {

Objectives

There is a need to consider COVID-19 a syndemic; which calls for a comprehensive approach to tackle the associated interconnected challenges. The objective of this study is to investigate the potential syndemic nature of COVID-19, with a specific focus on understanding how viral infection, mental health (such as anxiety and depression), and pre-existing comorbidities interact and influence each other.

Study Design

Retrospective population-based cohort study.

Methods

We conducted a population-based retrospective cohort study using linked health administrative data from the Institute for Clinical Evaluative Sciences, Ontario. The study included 2,863,423 Ontario residents from January 2020 to March 2021. We analysed healthcare services utilisation (physician visits, emergency visits, and hospitalisations) for chronic conditions among individuals with both COVID-19 and either anxiety or depression, to understand the syndemic impact of COVID-19 and mental health issues among Ontario population.

Results

Multiple regression models were used to explore the study{\textquoteright}s objective. In the final adjusted regression model for the sample, it was found that the individuals who were COVID-19 positive and had either anxiety or depression were more likely to utilise health services for chronic conditions of interest during the pandemic than those who were COVID-19-negative with mental health issues (odds ratio [OR]:, 1.33; 95\% confidence interval [CI]: 1.12{\textendash}1.58). A higher risk of morbidity was observed among males (OR: 1.28; CI: 1.16{\textendash}1.41), as well as in individuals with diverse ethnic backgrounds and low socioeconomic status.

Conclusions

The impact of COVID-19 on mental health, particularly among vulnerable populations with chronic diseases, can be seen as a syndemic. This complex interaction emphasises the need for integrated public health strategies.

}, url = {https://doi.org/10.1016/j.puhe.2024.07.008}, author = {K. Saqib and V. Goel and J.A. Dubin and J. VanderDoes and Z.A. Butt} } @article {10.1093/ectj/utae013, title = {The maximally selected likelihood ratio test in random coefficient models}, journal = {The Econometrics Journal}, volume = {27}, year = {2024}, month = {05}, pages = {384{\textendash}411}, abstract = {In a recent contribution, Horv{\'a}th and Trapani (2022) develop a family of CUSUM-based changepoint tests in the context of a Random Coefficient Autoregressive model of order 1. In this paper, we complement the results in that contribution by studying the (maximally selected) Likelihood Ratio statistic. We show that this has power versus breaks occurring even as close as O(log log N) periods from the beginning/end of sample; moreover, the use of Quasi Maximum Likelihood based estimates yields better power properties, with the added bonus of being nuisance-free. Our test statistic has the same distribution - of the Darling-Erd{\H o}s type - irrespective of whether the data are stationary or not, and can therefore be applied with no prior knowledge on this. Our simulations show that our test has very good power and, when applying a suitable correction to the asymptotic critical values, the correct size. We illustrate the usefulness and generality of our approach through applications to economic and epidemiological time series.}, issn = {1368-4221}, doi = {10.1093/ectj/utae013}, url = {https://doi.org/10.1093/ectj/utae013}, author = {Horv{\'a}th, Lajos and Trapani, Lorenzo and VanderDoes, Jeremy} } @book {35763, title = { Topics in Statistical Consulting }, year = {2024}, url = {https://scsru.github.io/Modules/}, author = {Trang Bui and Meixi Chen and Luke Hagar and Kelly Ramsay and Yuliang Shi and Grace Tompkins and Jeremy VanderDoes and Feiyu Zhu} } @article {35757, title = { Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments }, journal = {PLOS Computational Biology}, volume = {20}, year = {2024}, pages = {1-28}, abstract = {Tumor microenvironments (TMEs) contain vast amounts of information on patient{\textquoteright}s cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.}, url = {https://doi.org/10.1371/journal.pcbi.1011361}, author = {Jeremy VanderDoes and Pedro Mendes and Claire Marceaux and Kenta Yokote and Marie-Liesse Asselin-Labat and Gregory Rice and Jack D. Hywood} } @article {32353, title = {Inference in Functional Factor Models with Applications to Yield Curves}, journal = {Journal of Time Series Analysis}, volume = {43}, year = {2022}, pages = {872-894}, abstract = {This article develops a set of inferential methods for functional factor models that have been extensively used in modelling yield curves. Our setting accommodates both temporal dependence and heteroskedasticity. First, we introduce an estimation approach based on minimizing the least-squares loss function and establish the consistency and asymptotic normality of the estimators. Second, we propose a goodness-of-fit test that allows us to determine whether a specific model fits the data. We derive the asymptotic distribution of the test statistics, and this leads to a significance test. A simulation study establishes the good finite-sample performance of our inferential methods. An application to US and UK yield curves demonstrates the generality of our framework, which can accommodate both sparsely and densely observed yield curves.}, url = {http://dx.doi.org/10.1111/jtsa.12642}, author = {Lajos Horvath and Piotr Kokoszka and Jeremy VanderDoes and Shixuan Wang} } @article {32352, title = {Integrating the CARD (Comfort Ask Relax Distract) system in a mass vaccination clinic to improve the experience of individuals during COVID-19 vaccination: a pre-post implementation study}, journal = {Human Vaccines \& Immunotherapeutics}, volume = {18}, year = {2022}, pages = {2089500-2089500}, abstract = {Many people have negative experiences with vaccination due to stress-related reactions including fear and pain. We used a pre-post study design to evaluate the impact of implementing a modified version of the CARD (Comfort-Ask-Relax-Distract) system on stress-related reactions in individuals aged 12 y or older undergoing COVID-19 vaccinations in mass vaccination clinics. Vaccine recipients reported their level of pain, fear and dizziness during vaccination. Clinic staff reported their attitudes about CARD and use of CARD interventions. CARD improved client symptoms across genders and ages with an average reduction in needle pain, fear and dizziness of 75\%, 40\% and 44\%, respectively. CARD was more effective in younger individuals. Clinic staff reported positive attitudes about CARD and uptake of selected CARD interventions. In summary, the modified CARD system reduced stress-related responses in a general population undergoing COVID-19 vaccinations in a mass vaccination clinic, was feasible and acceptable to staff. Future implementation efforts are recommended that include more diverse cultural contexts and incorporate education of individuals about CARD ahead of time.}, url = {http://dx.doi.org/10.1080/21645515.2022.2089500}, author = {Moses Tetui and Kelly Grindrod and Nancy Waite and Jeremy VanderDoes and Anna Taddio} }