Browsing by Author "Beykzadeh, Ali"

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    Graduate retention in New Brunswick: 2021 graduate cohort update
    (New Brunswick Institute for Research, Data and Training, 2024-06) McDonald, Ted; Miah, Pablo; Beykzadeh, Ali; Gorman-Asal, Madeleine
    This report is the third in a series of annual updates on post-secondary graduates’ retention in New Brunswick (NB) by the New Brunswick Institute for Research, Data and Training (NB-IRDT). These reports provide an overview of the retention patterns of graduates from post-secondary institutions in NB since the previous reporting period, and their updates on graduates’ retention trends can help guide public policy discussions around education and training strategies to attract younger individuals to study, work and live in NB. The current study, which includes data on the 2021 graduate cohort, provides an update to the graduate retention results released by NB-IRDT in 2023 (Balzer et al., 2023), which included data on individuals who graduated in 2019 and 2020. The methodology followed by Balzer et al. (2023) was replicated for this cohort update.
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    Multi-patch integrodifference models and their eigenvalue problems in spatial ecology
    (University of New Brunswick, 2024-10) Beykzadeh, Ali; Watmough, James
    In the realm of spatial ecology, we grapple with fundamental questions: How can we design effective nature reserves to safeguard the survival of species? In the context of fisheries, how wide can a fishing zone be without compromising the stability of fish populations? These inquiries have fueled my motivation to delve into the subject matter of this thesis. While we acknowledge that precise answers to such questions remain elusive, I have endeavored to contribute to our understanding of these critical issues. Our journey begins with an exploration of integrodifference equations (IDEs) in spatial ecology in Chapter 1. These mathematical models serve as powerful tools for unraveling the intricate spatial and temporal dynamics of populations characterized by discrete generations and continuous spatial domains. Imagine a population confined to a single isolated patch — a scenario akin to a lake surrounded by hard boundaries. Within this patch, there exists a gap devoid of reproduction, effectively separating the population. Consider, for instance, a protected fishing zone within a lake. Our focus in Chapter 2 lies on understanding the persistence of such populations. We model their life cycles using IDEs and present a method to calculate the maximum allowable gap size that ensures population persistence. The concept of critical patch size takes center stage in Chapter 3. It refers to the minimum favorable area below which a population faces the risk of extinction. Our investigation accounts for the demographic and dispersal traits of individuals, recognizing that these traits may vary across patches. Surprisingly, we find that the smallest critical patch size occurs when individuals exhibit a propensity to leave the patch. Conversely, the largest critical patch size arises when boundaries are more restrictive, limiting the chances of individuals leaving the patch. In the patchy landscape, Chapter 4 introduces an approximation method that simplifies equilibrium population calculations. Our approach involves a form of the redistribution approximation tailored for piecewise continuous kernels. The accuracy of our estimate improves as movement biases near patch boundaries intensify. Key factors influencing our estimate include the growth term’s derivative and the deviation of the equilibrium solution from its average across patches.
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    Multi-patch Laplace dispersal across biased interfaces
    (University of New Brunswick, 2019) Beykzadeh, Ali; Watmough, James
    In the study of the dispersal of species across a landscape, most previous models approximate heterogeneous landscapes by a set of homogeneous patches and allow for different demographic and dispersal rates within each patch. Some work has been done designing and analyzing models which also include a patch preference at the boundaries, which is commonly referred to as a degree of bias. Individuals dispersing across a patchy landscape can detect the changes in habitat at a neighbourhood of a patch boundary, and as a result, they might change the direction of their movement if they are approaching a bad patch. This thesis is devoted to the mathematical derivation of a generalization of the classic Laplace kernel, which includes different dispersal rates in each patch as well as different degrees of bias at the patch boundaries. The simple Laplace kernel and the truncated Laplace kernel most often used in classical work appear as special cases of this general kernel. The form of this general kernel is the sum of two different terms: the classic truncated Laplace kernel within each patch, and a correction accounting for the bias at patch boundaries.
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    New Brunswick population and demographic counts - December 2022
    (New Brunswick Institute for Research, Data and Training, 2023-03) Beykzadeh, Ali; McDonald, Ted
    This report is the third in a series of ongoing reports that will be published by the New Brunswick Institute for Research, Data and Training (NB-IRDT) twice each year. These reports measure the total population count of New Brunswick (NB) by different demographic and geographic characteristics, as well as the magnitude of interprovincial inflows and outflows. Each new release provides an overview of changes to the New Brunswick population that have occurred since the previous reporting period. The first report1 (October 2021) provides historical data on the NB population for the years 2010-2020 and serves as a benchmark for consecutive updates, including this one. It also contains additional background information on this project. The second report2 updates the descriptive statistics in the first report by extending the original study period to investigate population movement from January 1, 2020, to July 1, 2021 (Quarter 1, 2020 to Quarter 2, 2021). It also describes results for additional or modified measures not included in the first report. The study period for the current report is Quarter 3, 2021 (beginning July 1, 2021) to Quarter 2, 2022 (ending June 30, 2022). This report updates core statistics from the second report while comparing results from the current study period with earlier results from Quarter 3, 2020 to Quarter 2, 2021, when the methodologies align. It presents modified measures for some previous categories of interest, including migration periods, immigrant status, and NB geographies. It also adds new indicators, including NB Returnees and Intra-Provincial Migration. By providing detailed insight into population composition and migration trends in NB, these reports inform research on population dynamics in NB and, in turn, economic growth and development. 1 Balzer, A., McDonald, T., & Mokhtar, R. (2021). New Brunswick population and demographic counts: October 2021. Fredericton, NB: New Brunswick Institute for Research, Data and Training. 2 Balzer, A., McDonald, T., & Mokhtar, R. (2022). New Brunswick population and demographic counts: June 2022. Fredericton, NB: New Brunswick Institute for Research, Data and Training.
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    New Brunswick population and demographic counts: 2019-2022
    (New Brunswick Institute for Research, Data and Training, 2023-10) Beykzadeh, Ali; Jones, Bethany; McDonald, Ted; Miah, Pablo
    In 2021, the New Brunswick Institute for Research, Data and Training (NB-IRDT) released the first report in a Population and demographics count series that provides a snapshot of the population of New Brunswick each year. These reports measure the total population count of New Brunswick and include information on New Brunswickers' demographic and geographic characteristics, as well as movement to and from the province. Each new update provides an overview of how the population has changed since the previous report was released, allowing us to see whether NB is attracting new residents and if individuals who previously left are now returning. This is the fourth report in the series, and it updates our population snapshot by comparing annual results from January 2019 until December 2022.
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    Summary Report: New Brunswick population and demographic counts - December 2022
    (New Brunswick Institute for Research, Data and Training, 2023-03) Beykzadeh, Ali; McDonald, Ted
    This report is the third in a series of ongoing reports that will be published by the New Brunswick Institute for Research, Data and Training (NB-IRDT) twice each year. These reports measure the total population count of New Brunswick (NB) by different demographic and geographic characteristics, as well as the magnitude of interprovincial inflows and outflows. Each new release provides an overview of changes to the New Brunswick population that have occurred since the previous reporting period. The first report1 (October 2021) provides historical data on the NB population for the years 2010-2020 and serves as a benchmark for consecutive updates, including this one. It also contains additional background information on this project. The second report2 updates the descriptive statistics in the first report by extending the original study period to investigate population movement from January 1, 2020, to July 1, 2021 (Quarter 1, 2020 to Quarter 2, 2021). It also describes results for additional or modified measures not included in the first report. The study period for the current report is Quarter 3, 2021 (beginning July 1, 2021) to Quarter 2, 2022 (ending June 30, 2022). This report updates core statistics from the second report while comparing results from the current study period with earlier results from Quarter 3, 2020 to Quarter 2, 2021, when the methodologies align. It presents modified measures for some previous categories of interest, including migration periods, immigrant status, and NB geographies. It also adds new indicators, including NB Returnees and Intra-Provincial Migration. By providing detailed insight into population composition and migration trends in NB, these reports inform research on population dynamics in NB and, in turn, economic growth and development. 1 Balzer, A., McDonald, T., & Mokhtar, R. (2021). New Brunswick population and demographic counts: October 2021. Fredericton, NB: New Brunswick Institute for Research, Data and Training. 2 Balzer, A., McDonald, T., & Mokhtar, R. (2022). New Brunswick population and demographic counts: June 2022. Fredericton, NB: New Brunswick Institute for Research, Data and Training.
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    Summary Report: The Impacts of Flooding Events on Mental Health in New Brunswick
    (New Brunswick Institute for Research, Data and Training, 2023-10) Magalhaes, Sandra; Lundy, Adele; Youssef, Simon; Simmons, Haylie; Cameron, Jillian; Beykzadeh, Ali
    Flooding events are among the most devasting natural disasters – and as a result of climate change, natural disasters such as flooding are expected to occur more frequently and be more severe. The effects caused by flooding can create a lot of stress and uncertainty. Previous research studies consistently demonstrate negative mental health impacts associated with flooding, such as anxiety, depression, and post-traumatic stress disorder (PTSD) and increases in need for health services. However, the research in this area is limited by lower quality research methodology, including self-selected samples and uncontrolled statistical analyses. The research study presented in this report describes mental health impacts of flooding. In doing so, it fills an important knowledge gap, as it is among the few studies to use population-based sampling and multivariable regression models in estimating the impacts of flooding and to identify high-risk population sub-groups that are more vulnerable to the impacts of flooding. The specific objectives of this research are to: 1. Characterize populations affected by flooding in the province of New Brunswick. 2. Determine which mental health outcomes are negatively affected by flooding. 3. Identify high-risk population sub-groups that may be more vulnerable to the mental health impacts of flooding. A population-based longitudinal cohort study design was established using linked, pseudonymized person-level administrative data available for access through the New Brunswick Institute for Research, Data and Training (NB-IRDT). Seven significant flooding events in New Brunswick were examined: 2005, 2008, 2012, 2014, 2015, 2018, and 2019. Cohort members were defined as exposed if they lived in a geographic area identified to have any flooding based on a combination of flood-related data from the Government of Canada and the Government of New Brunswick. Six mental health outcomes and six alternate outcomes were compared between exposed and unexposed populations. Mental health outcomes include health service use for mental illness and more specifically for mood and/or anxiety disorders, hospitalization for mental illness-related reasons and for post-traumatic stress disorder (PTSD), and physician services for counselling/psychotherapy, as well as death by suicide. Several alternate outcomes were also examined to provide a fuller understanding of the experiences of the exposed population, including Emergency Department use, hospital service use, school attendance in children, and withdrawal from post-secondary education in youth. Risk factors for mental health and alternate outcomes were also considered in exposed populations, including flood-related, sociodemographic, and health-related characteristics. Advanced regression modeling techniques were used to compare outcomes during the same time period in an exposed population relative to an unexposed population that was similar with respect to age, sex, socioeconomic status, and pre-flooding mental health.
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    Technical Appendix: New Brunswick population and demographic counts: 2019-2022
    (New Brunswick Institute for Research, Data and Training, 2023) Beykzadeh, Ali; Jones, Bethany; McDonald, Ted; Miah, Pablo
    In 2021, the New Brunswick Institute for Research, Data and Training (NB-IRDT) released the first report in a Population and demographics count series that provides a snapshot of the population of New Brunswick each year. These reports measure the total population count of New Brunswick and include information on New Brunswickers' demographic and geographic characteristics, as well as movement to and from the province. Each new update provides an overview of how the population has changed since the previous report was released, allowing us to see whether NB is attracting new residents and if individuals who previously left are now returning. This is the fourth report in the series, and it updates our population snapshot by comparing annual results from January 2019 until December 2022.
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    The Impacts of Flooding Events on Mental Health in New Brunswick
    (New Brunswick Institute for Research, Data and Training, 2023-09) Magalhaes, Sandra; Lundy, Adele; Youssef, Simon; Simmons, Haylie; Cameron, Jillian; Beykzadeh, Ali
    Flooding events are among the most devasting natural disasters – and as a result of climate change, natural disasters such as flooding are expected to occur more frequently and be more severe. The effects caused by flooding can create a lot of stress and uncertainty. Previous research studies consistently demonstrate negative mental health impacts associated with flooding, such as anxiety, depression, and post-traumatic stress disorder (PTSD) and increases in need for health services. However, the research in this area is limited by lower quality research methodology, including self-selected samples and uncontrolled statistical analyses. The research study presented in this report describes mental health impacts of flooding. In doing so, it fills an important knowledge gap, as it is among the few studies to use population-based sampling and multivariable regression models in estimating the impacts of flooding and to identify high-risk population sub-groups that are more vulnerable to the impacts of flooding. The specific objectives of this research are to: 1. Characterize populations affected by flooding in the province of New Brunswick. 2. Determine which mental health outcomes are negatively affected by flooding. 3. Identify high-risk population sub-groups that may be more vulnerable to the mental health impacts of flooding. A population-based longitudinal cohort study design was established using linked, pseudonymized person-level administrative data available for access through the New Brunswick Institute for Research, Data and Training (NB-IRDT). Seven significant flooding events in New Brunswick were examined: 2005, 2008, 2012, 2014, 2015, 2018, and 2019. Cohort members were defined as exposed if they lived in a geographic area identified to have any flooding based on a combination of flood-related data from the Government of Canada and the Government of New Brunswick. Six mental health outcomes and six alternate outcomes were compared between exposed and unexposed populations. Mental health outcomes include health service use for mental illness and more specifically for mood and/or anxiety disorders, hospitalization for mental illness-related reasons and for post-traumatic stress disorder (PTSD), and physician services for counselling/psychotherapy, as well as death by suicide. Several alternate outcomes were also examined to provide a fuller understanding of the experiences of the exposed population, including Emergency Department use, hospital service use, school attendance in children, and withdrawal from post-secondary education in youth. Risk factors for mental health and alternate outcomes were also considered in exposed populations, including flood-related, sociodemographic, and health-related characteristics. Advanced regression modeling techniques were used to compare outcomes during the same time period in an exposed population relative to an unexposed population that was similar with respect to age, sex, socioeconomic status, and pre-flooding mental health.