Ariel Mundo Ortiz

Background: The variability of vital signs thresholds has not been well described in relation to older age and sex assigned at birth. Significant variations may influence clinical interpretation of vital signs, most notably the thresholds at which investigations or treatments are initiated. Our objective was to compare, in stable health status, the percentage of abnormal values (flagging percentage) of standard vital signs thresholds of adults aged 45 years and older and to determine centile-based thresholds by age and sex. Methods: A retrospective observational study was conducted using data from the Centre for the Integration and Analysis of Medical Data (CITADEL) at the Centre hospitalier de l’Université de Montréal (CHUM), a tertiary care hospital. All inpatients and outpatients, from January 2012 to December 2021, were included. For individuals with multiple visits, a single hospitalization or a single outpatient visit was selected at random for inclusion. To reflect a stable health status, the last vital signs recorded before hospital discharge were selected and in-hospital deaths were excluded. Flagging percentages were identified using a quantile-based approach. Age and sex specific centile-based thresholds were determined by identifying values at the 2.5 ^th and 97.5 ^th centiles. Flagging percentages and centile values for systolic and diastolic blood pressure, heart rate, and temperature were analyzed by age group (45–54, 55–64, 65–74, 75–84, and 85 + years) and sex. Results: The sample comprised 123,420 individuals, including 90,813 inpatients. Flagging percentages for the standard threshold of upper systolic blood pressure (140 mmHg) ranged from 13.3 (12.7–13.9) % (45-54y assigned female at birth (AFAB)) to 39.8 (38.6–41.0) % (85y+ AFAB). Flagging percentages for standard thresholds ranged between 0.1 (0.1–0.2) to 1.2 (1.0–1.4) % for upper temperature (37.8°C), lower temperature (35.0°C), and lower systolic blood pressure (90 mmHg). Across subgroups, the 97.5 ^th centile-based adapted thresholds varied between 158 (156−159) – 178 (177−180) mmHg for upper systolic blood pressure and between 37.2 (37.1–37.2) – 37.3 (37.3–37.3) °C for upper temperature. Conclusions: Important differences in vital signs flagging percentages may affect their comparability by vital sign, age, and sex. Standard thresholds may need to be reassessed using clinical outcomes to factor age and sex differences.

Background: Mobility is a critical component of health status in hospitalized older adults. Adoption of routine mobility tracking in acute and subacute settings is hampered by lack of automated and standardized measurements. Advances in smartwatch technology and machine learning provide the opportunity to use heart rate (HR) and HR variability data to quantify mobility and activity. Methods: In this pilot study, we recruited 30 older adults aged 65 years and older in a tertiary care geriatric ward to develop (n = 8) and validate (n = 30) the automated Mobility and Activity Scale (MAS). Twelve features based on smartwatch HR data were used in a random forest model to predict 5 activity levels (0 = sleep to 4 = walking with at least a moderate effort or > 20 minutes). We examined concurrent validity with Hierarchical Assessment of Balance and Mobility (HABAM), gait speed, and functional status, as well as discriminant validity with frailty and multimorbidity. We assessed acceptability of watch wearing for patients and care staff. Results: Participants’ mean (SD) age was 86 years (8), 18 (60%) were female, and mean follow-up was 8.3 (5.2) days. Mean (SD) HABAM score was 36 (18) and gait speed was 0.53 (0.26) m/s. Across the cohort, mean (SD) MAS score was 1.2 (1.0) overall and 2.1 (0.7) for 10 most active hours of the day. MAS scores were moderately correlated with HABAM (r = 0.43 [95%CI = 0.07,0.69]) and functional status (r=-0.31 [95%CI=-0.60,0.06]), but not with gait speed (r = 0.02 [95%CI=-0.39,0.42]). MAS scores had no association with frailty or multimorbidity. Smartwatch wearing was acceptable. Conclusions: Smartwatch-derived HR data may quantity hourly mobility and activity of hospitalized older adults and facilitate automated and real-time monitoring.

Background: The frequency and severity of floods has increased in different regions of the world due to climate change. It is important to examine how adaptation measures impact the mental health of individuals affected by these disasters. Objective: The goal of this scoping review was to document the existing studies on the impact of flood adaptation measures in affected populations to identify the best preventive strategies and limitations that deserve further exploration. Methods: This study followed the PRISMA-ScR guidelines. Inclusion criteria focused on studies in English or French available in MEDLINE and Web of Science that examined the impact of adaptation measures on the mental health of flood victims. Literature reviews or non-study records were excluded from the analysis. Results: A total of 857 records were obtained from the examined databases. After 2 rounds of screening, 9 studies were included for full-text analysis. Six studies sought to identify the factors that drive resilience in flood victims, whereas 3 studies analyzed the impact of external interventions on their mental health. Conclusions: The limited number of studies demonstrates the need for public health policies to develop flood adaptation measures that can be used to support the mental health of flood victims.

The COVID-19 pandemic continues to be a worldwide public health concern. Although vaccines against this disease were rapidly developed, vaccination uptake has not been equal across all the segments of the population. In particular, it has been shown that there have been differences in vaccine uptake across different segments of the population. However, there are also differences in vaccination across geographical areas, which might be important to consider in the development of future public health vaccination policies. In this study, we examined the relationship between vaccination status (having received the first dose of a COVID-19 vaccine), and different socio-economic and geographical factors. Our results show that between October of 2021 and January of 2022, individuals from underrepresented communities were three times less likely to be vaccinated than White/Caucasian individuals across the province of Ontario in Canada, and that in some cases, within these groups, individuals in low-income brackets had significantly higher odds of vaccination when compared to their peers in high income brackets. Finally, we identified significantly lower odds of vaccination in the Central, East and West Health Regions of Ontario within certain underrepresented groups. This study shows that there is an ongoing need to better understand and address differences in vaccination uptake across diverse segments of the population of Ontario that the pandemic has largely impacted.

The frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

Background Mpox (monkeypox), a disease historically endemic to Africa, has seen its largest outbreak in 2022 by spreading to many regions of the world and has become a public health threat. Informed policies aimed at controlling and managing the spread of this disease necessitate the use of adequate mathematical modelling strategies. Objective In this scoping review, we sought to identify the mathematical models that have been used to study mpox transmission in the literature in order to determine what are the model classes most frequently used, their assumptions, and the modelling gaps that need to be addressed in the context of the epidemiological characteristics of the ongoing mpox outbreak. Methods This study employed the methodology of the PRISMA guidelines for scoping reviews to identify the mathematical models available to study mpox transmission dynamics. Three databases (PubMed, Web of Science and MathSciNet) were systematically searched to identify relevant studies. Results A total of 5827 papers were screened from the database queries. After screening, 35 studies that met the inclusion criteria were analyzed, and 19 were finally included in the scoping review. Our results show that compartmental, branching process, Monte Carlo (stochastic), agent-based, and network models have been used to study mpox transmission dynamics between humans as well as between humans and animals. Furthermore, compartmental and branching models have been the most commonly used classes. Conclusions There is a need to develop modelling strategies for mpox transmission that take into account the conditions of the current outbreak, which has been largely driven by human-to-human transmission in urban settings. In the current scenario, the assumptions and parameters used by most of the studies included in this review (which are largely based on a limited number of studies carried in Africa in the early 80s) may not be applicable, and therefore, can complicate any public health policies that are derived from their estimates. The current mpox outbreak is also an example of how more research into neglected zoonoses is needed in an era where new and re-emerging diseases have become global public health threats.

In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using therepeated measures analysis of variance(rm‐ANOVA) or more recently,linear mixed models(LMEMs). Although LMEMs are less restrictive than rm‐ANOVA as they can work with unbalanced data and non‐constant correlation between observations, both methodologies assume a linear trend in the measured response. It is common in biomedical research that the true trend response is nonlinear and in these cases the linearity assumption of rm‐ANOVA and LMEMs can lead to biased estimates and unreliable inference. In contrast, GAMs relax the linearity assumption of rm‐ANOVA and LMEMs and allow the data to determine the fit of the model while also permitting incomplete observations and different correlation structures. Therefore, GAMs present an excellent choice to analyze longitudinal data with non‐linear trends in the context of biomedical research. This paper summarizes the limitations of rm‐ANOVA and LMEMs and uses simulated data to visually show how both methods produce biased estimates when used on data with non‐linear trends. We present the basic theory of GAMs and using reported trends of oxygen saturation in tumors, we simulate example longitudinal data (2 treatment groups, 10 subjects per group, 5 repeated measures for each group) to demonstrate their implementation in R. We also show that GAMs are able to produce estimates with non‐linear trends even when incomplete observations exist (with 40% of the simulated observations missing). To make this work reproducible, the code and data used in this paper are available at:https://github.com/aimundo/GAMs‐biomedical‐research.

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.