Hasil untuk "Demography. Population. Vital events"

Estelle Lowry, Ian Shuttleworth, Peter Wilgar et al.

Objective The Northern Ireland Longitudinal Study (NILS) is a complex administrative data linkage system. Since its launch in 2006, it has expanded in both breadth and depth, allowing the development of new research agendas which deliver insights into the population of Northern Ireland and how their lives have changed over time. Methods The NILS uses a medical card spine to link census and other administrative datasets resulting in a cohort of c515,000 people representing approx. 28% of medical card registrations. It was originally created based on 2001 Census data, and has since undergone subsequent linkages with Census 2011, 1981 (partial), 1991 and most recently Census 2021 data resulting in a rich dataset spanning 40 years. This is bridged by further routine linkages to vital events and the provision of distinct linkage projects (DLP’s) enables opportunities for in depth exploration of health and social care. Results The 2021 Census introduced several new questions, alongside those asked for the second or subsequent time. This offers new opportunities for research topics such as sexual identity, national identity, passports held, religion, demographics, energy-efficient households, and health. We observe an aging population, housing tenure shows considerably higher proportions of shared ownership in later years, and the proportion of those holding a United Kingdom passport only had decreased at the time of the most recent Census. The number of people stating a long-term health condition has also increased. The Census serves as a valuable tool to examine the impact of the past decade, during which society has faced unprecedented events such as Brexit, the global pandemic, and crises in housing and cost-of-living. Conclusions We present an updated cohort profile, showcasing characteristics of the 2021 Census population, alongside highlighting recent research. As part of the wider UK Census Longitudinal Studies, the NILS aligns with the Scottish and ONS Longitudinal Studies, facilitating cross-UK analysis. New remote access arrangements can further widen our user base.

Jianhua Wu, Jing Kang, Chenyi Gao et al.

Objectives Traditionally, dental and medical records are collected and stored separately within the healthcare system, limiting opportunities for integrated research. This study explores the feasibility of integrating routinely collected administrative secondary care dental and hospital inpatient records to create a scalable research infrastructure for investigating associations between oral health and systemic disease. Methods We established a data linkage framework using routinely collected dental and hospital EHR from Leeds Teaching Hospitals NHS Trust. Patient records (2014–2023) were linked via NHS numbers, integrating dental treatment data with systemic disease diagnoses (ICD-10). Linkage success rates and data completeness were assessed. We examined systemic disease prevalence and multimorbidity patterns across dental specialties to validate the utility of administrative data for oral-systemic research. Results The linkage framework successfully integrated 32,675 dental patient records with hospital EHR, with over 90% achieving complete linkage. One in five patients (19.7%) had at least one systemic disease, with the highest burden in periodontal/restorative patients (50.9%). Cardiovascular and musculoskeletal conditions were significantly more prevalent in these patients, reinforcing established oral-systemic health associations. Data completeness analysis identified opportunities for further linkage with primary care records to enhance multimorbidity profiling. Conclusion By integrating routinely collected dental and hospital records, this study demonstrates the potential of administrative health data for investigating oral-systemic health relationships. This approach provides a scalable framework for future multimorbidity research, supporting the development of integrated healthcare strategies and informing evidence-based policy in oral and general health.

Chenxing Tan, Yuguan Hou, Hao Wang et al.

This paper presents a novel micro-Doppler energy-based framework for robust multi-target vital signs monitoring using 77-GHz Frequency-Modulated Continuous-Wave (FMCW) radar. Unlike conventional phase-based methods that are susceptible to environmental noise, random body movements, and stringent calibration requirements, our approach exploits the energy variations in radar returns induced by cardiopulmonary activities. The proposed system integrates a comprehensive processing pipeline including space-time adaptive processing (STAP) for target detection and tracking, MUSIC algorithm for high-resolution angle estimation, and an innovative adaptive spectral filtering technique for vital signs extraction. We establish a rigorous mathematical framework that formalizes the relationship between micro-Doppler energy variations and physiological activities, enabling robust separation of closely spaced targets. The key innovation lies in the micro-Doppler energy extraction methodology that provides inherent robustness to phase noise and motion artifacts. Experimental results using millimeter-wave radar datasets demonstrate that the system can accurately detect and separate vital signs of up to four targets within \SI{5}{\meter} range, achieving mean absolute errors of \SI{1.2}beats per minute and \SI{2.3} beats per minute for respiration and heart rates, respectively. The proposed approach demonstrates superior performance compared to traditional phase-based methods, particularly in challenging multi-target scenarios with environmental noise and subject movement.

Tommaso Polonelli, Manuel Glahn, Stefano Kron et al.

Monitoring physiological and behavioral parameters of laboratory rodents is fundamental for biomedical research, yet conventional techniques often rely on invasive sensors or frequent handling that can induce stress and compromise data fidelity. To address these limitations, this paper presents a contactless and non-invasive in-vivo monitoring system based on a low-power 60 GHz frequency-modulated continuous wave (FMCW) radar. The proposed system enables simultaneous detection of rodent activity and vital signs directly within home-cage environments, eliminating the need for implants, electrodes, or human intervention. The hardware platform leverages a compact Infineon BGT60 series radar sensor, optimized for low power consumption and continuous operation. We investigate sensor placement strategies and design a complete signal processing pipeline, including range bin selection, phase extraction, and frequency-domain estimation tailored to rodent vital signs. The system achieves 3 cm and 0.1 m/s sensitivity for motion and activity detection, while allowing discrimination of micro-movements associated with cardiopulmonary activity with a 2 um distance resolution. Experimental validation with two rodents in realistic in-vivo cages demonstrates that the radar can track animal position and extract respiration rates with 2 bpm accuracy. By minimizing stress and disturbance, this work improves both animal welfare and the reliability of physiological measurements, offering a refined alternative to traditional monitoring methods. This work represents the first demonstration of continuous radar-based vital sign monitoring in freely moving rodents within group-housed cages. The proposed approach lays the foundation for scalable, automated, and ethical monitoring solutions in preclinical and translational research.

Natasha S. Abrams, Jessica R. Lu, Casey Y. Lam et al.

Gravitational microlensing provides a unique opportunity to probe the mass distribution of stars, black holes, and other objects in the Milky Way. Population simulations are necessary to interpret results from microlensing surveys. The contribution from binary objects is often neglected or minimized in analysis of observations and simulations despite the high percentage of binary systems and microlensing's ability to probe binaries. To simulate the population effects we added multiple systems to Stellar Population Interface for Stellar Evolution and Atmospheres (SPISEA), which simulates stellar clusters. We then inject these multiples into Population Synthesis for Compact-object Lensing Events (PopSyCLE), which simulates Milky Way microlensing surveys. When making OGLE observational selection criteria, we find that 55% of observed microlensing events involve a binary system. Specifically, 14.5% of events have a multiple-lens and a single source, 31.7% have a single lens and a multiple-source, and 8.8% have a multiple-lens and a multiple-source. The majority of these events have photometric lightcurves that appear single and are fit well by a single-lens, single-source model. This suggests that binary source and binary lens-binary source models should be included more frequently in event analysis. The mean Einstein crossing time shifts from 19.1 days for single events only to 21.3 days for singles and multiple events, after cutting binary events with multiple peaks. The Einstein crossing time distribution of singles and single-peaked multiple events is better aligned with observed distributions from OGLE (arXiv:1707.07634) than singles alone, indicating that multiple systems are a significant missing piece between simulations and reality.

Kimberlyn McGrail, Ted McDonald, Nathan Nickel

Background Population data science has a long history with health care data. Some data centres are expanding to include “health-related” data, including information on other publicly funded services (e.g. education, income and housing supports), and systems (e.g. criminal justice, children in care). We describe what enabled this expansion through three examples from Health Data Research Network Canada.  Methods HDRN Canada members include data centres that provide access to linkable data sets for approved research projects. We use case studies to identify similarities, differences, and lessons learned from the process of expansion in the provinces of Manitoba, New Brunswick and British Columbia.  Results The Manitoba Centre for Health Policy has 30+ years of experience with linked data, with a consistent focus on population health. Data sets expanded incrementally, through partnerships across government agencies. Population Data BC has a nearly 30-year history, with  recent expansion to health-related data through partnership with a program of one government ministry that operates under distinct legislative authority. The New Brunswick Institute for Research, Data and Training is newer, and helped advocate for legislation changes that enabled an expanded set of linked data to be made available for research. All embed Five Safes principles, and commitments to inclusion, diversity, equity and accessibility and Indigenous data sovereignty in data governance.  Conclusion A greater variety of data enables research to answer complex research questions. This kind of expansion can be accomplished through different mechanisms, but in all cases requires attention to ethical and legal principles of population data science.

Wen Su, Alyson van Raalte, Jose Manuel Aburto et al.

BACKGROUND: The US life expectancy has been stagnating in recent decades, and along with this, the time trends of life span variation have shown stagnation and even increases with respect to historical levels. OBJECTIVE: We aim to disentangle contributions from subnational levels (US regions) to national changes in life expectancy and life span variation in 2010–2019 and 2019–2020. METHODS: A decomposition of the change in the national life expectancy and life disparity into the contribution of changing mortality and population structure among subnational regions is presented. The US Census regions are the Midwest, Northeast, South, and West. RESULTS: From 2010 to 2019, the South substantially contributed to the life span variation increase due to increasing mortality contributions. The old-age survival improvements across all regions further contributed to increasing life span variation at the national level. Different population growth patterns across regions, especially at older ages, are a further source of change in national life span variation and life expectancy. From 2019 to 2020, during the COVID-19 pandemic, an increase in life span variation and a decrease in life expectancy across all regions were observed. CONTRIBUTION: We present continuous-time decompositions for changes in life expectancy and life span variation. When decomposing subnational contributions to national changes, we also demonstrate the role of the composition effect through subnational–national growth differences. This paper quantifies and highlights the specific contributions of regions and age groups to the national mortality increase in the United States between 2010 and 2019, as well as between 2019 and 2020.

Maria Gabriella Cavalcante Basílio, Daniel Ratton Figueiredo

Coral reefs are crucial to marine biodiversity and rely on a delicate symbiotic relationship between corals and zooxanthellae algae. Water temperature variations, however, disrupt this association, leading to coral bleaching events that severely affect marine ecosystems. This study presents a mathematical model for the population dynamics of coral and symbiont species considering the coral symbiont network and recurrent warming events. The model incorporates thermal tolerances of species and coupled growth dynamics (between corals and symbionts) to investigate how network structure and thermal tolerance influence the species' growth. Using real data from different ocean regions, results reveal that network connectivity plays a significant role in population growth after successive warming events, with generalist species demonstrating greater growth across all regions analyzed. The comparatively higher correlation between node degree and final population also emphasizes the impact of ecological network structure on species growth, offering valuable insights into coral reef population dynamics under climate change. This research highlights the need to consider network structure beyond species' thermal tolerances when evaluating the ecological responses of corals to environmental changes.

Megan Munro, Patrycja Delong-Smith, Pete Matthews et al.

There is an appetite to improve the reproducibility of quantitative analysis undertaken across government. Our team supports conversion of regular publications into Reproducible Analytical Pipelines (RAP). To achieve this, we developed a roadmap to work with analysts in our organisation and help them transform their pipelines and build their skills. An audit of the current RAP status of all regular pipelines assisted in resource allocation and planning. The maturity of a RAP is evaluated on 7 criteria required to reach a minimum viable product (MVP) and 7 additional advanced criteria as outlined by the Analysis Function (AF). We use a combination of hands-on pair-coding with analysis teams, regular and ad hoc code reviews, and training sessions to convert existing pipelines into RAPs, while simultaneously upskilling the analysts. We have also developed guidance and training documentation to share internally and externally. Currently, out of 73 regular publications, 12 have reached the MVP, with an average score of 4.36 out of 7. This scoring is reassessed monthly, allowing us to track the progress in real-time. Self-assessment of technical skills increased by between 43% and 89% and 97% said their understanding of RAP principals improved because of the training and 77% said they are now able to implement best practice into their work. By working with the pipeline owners instead of just refactoring the code directly, we are ensuring business resilience. The in-depth knowledge of the pipeline and skills required to maintain it are present within the analysis team. Publishing our methods, documentation and tools facilitates adoption of RAP for those without a dedicated RAP team. We are on track to convert all our regular publications into RAPs and move to “RAP by default”, in line with the AF RAP Strategy. This will improve the reproducibility, quality, efficiency, transparency, and trustworthiness of analysis within government. We hope other organisations can learn from our methods.

Christoph Walker

The principle of linearized stability and instability is established for a classical model describing the spatial movement of an age-structured population with nonlinear vital rates. It is shown that the real parts of the eigenvalues of the corresponding linearization at an equilibrium determine the latter's stability or instability. The key ingredient of the proof is the eventual compactness of the semigroup associated with the linearized problem, which is derived by a perturbation argument. The results are illustrated with examples.

Daphné Giorgi, Sarah Kaakai, Vincent Lemaire

The R Package IBMPopSim aims to simulate the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs). The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications in fields including actuarial science, biology, ecology or epidemiology. IBMPopSim overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the Rcpp package while providing a user-friendly interface.

Eric W. Jones, Joshua Derrick, Roger M. Nisbet et al.

In exponential population growth, variability in the timing of individual division events and environmental factors (including stochastic inoculation) compound to produce variable growth trajectories. In several stochastic models of exponential growth we show power-law relationships that relate variability in the time required to reach a threshold population size to growth rate and inoculum size. Population-growth experiments in E. coli and S. aureus with inoculum sizes ranging between 1 and 100 are consistent with these relationships. We quantify how noise accumulates over time, finding that it encodes -- and can be used to deduce -- information about the early growth rate of a population.

Paola Di Giulio, Anne Goujon, Guillaume Marois

The introduction to the 2022 Special Issue presents the 20 articles that discuss the demographic aspects and the consequences of the COVID-19 pandemic. It synthesises the main findings from the contributions, emphasising the demographic, social and economic characteristics that influenced the spread of infections and determined the number of deaths. We highlight the specific focus on measurement issues, often with a comparative framework across several countries, and at the regional level as well, both within and beyond Europe. We also summarise the impact of the measures imposed to contain the spread of the virus, such as lockdowns. Moreover, we explore the impact of the pandemic on the quality of relationships, the intention and the motivation to have children, and realised fertility. In addition, we present the authors’ broader reflections on the risks faced by different communities of individuals, and the potential consequences for their life trajectories, including in relation to other current risks that overlap with the pandemic (recent armed conflicts), and for the achievability of the Sustainable Development Goals themselves.

Stephany Rajeh, Marinette Savonnet, Eric Leclercq et al.

It is of paramount importance to uncover influential nodes to control diffusion phenomena in a network. In recent works, there is a growing trend to investigate the role of the community structure to solve this issue. Up to now, the vast majority of the so-called community-aware centrality measures rely on non-overlapping community structure. However, in many real-world networks, such as social networks, the communities overlap. In other words, a node can belong to multiple communities. To overcome this drawback, we propose and investigate the "Overlapping Modularity Vitality" centrality measure. This extension of "Modularity Vitality" quantifies the community structure strength variation when removing a node. It allows identifying a node as a hub or a bridge based on its contribution to the overlapping modularity of a network. A comparative analysis with its non-overlapping version using the Susceptible-Infected-Recovered (SIR) epidemic diffusion model has been performed on a set of six real-world networks. Overall, Overlapping Modularity Vitality outperforms its alternative. These results illustrate the importance of incorporating knowledge about the overlapping community structure to identify influential nodes effectively. Moreover, one can use multiple ranking strategies as the two measures are signed. Results show that selecting the nodes with the top positive or the top absolute centrality values is more effective than choosing the ones with the maximum negative values to spread the epidemic.

Kiranmoy Chatterjee, Prajamitra Bhuyan

Population size estimation based on the capture-recapture experiment is an interesting problem in various fields including epidemiology, criminology, demography, etc. In many real-life scenarios, there exists inherent heterogeneity among the individuals and dependency between capture and recapture attempts. A novel trivariate Bernoulli model is considered to incorporate these features, and the Bayesian estimation of the model parameters is suggested using data augmentation. Simulation results show robustness under model misspecification and the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse real case studies on epidemiological surveillance. The results provide interesting insight on the heterogeneity and dependence involved in the capture-recapture mechanism. The methodology proposed can assist in effective decision-making and policy formulation.

Roselinde van der Wiel, Clara H. Mulder, Helga A.G. de Valk

This study examines the association between having children born before the current partnership and women’s and men’s likelihood of transitioning from living apart together (LAT) to co-residing. LAT partnerships are common among individuals with pre-partnership children, but have so far been under-researched. Our study not only focuses on those in LAT relations, but also takes the different pathways to becoming a single parent into account. Event-history analysis was performed using waves 1-4 from the Netherlands Kinship Panel Study. The results indicate that separated and widowed mothers were less likely to transition to co-residence with their LAT partner than childless women who had previously been in a co-residential union. Mothers who had previous out-of-union children were found to be even less likely to enter co-residence. Results were mostly similar for men and women. The only exception was the effect of being widowed with children; for men this resulted in higher chances of transitioning to co-residence with a new partner whereas for women the chances were lower. The findings suggest that individuals’ parenthood and union histories are associated with the development of their later partnerships, and that these patterns vary by gender. Given contemporary and future patterns of partnership separation, our study provides insights for better understanding how LAT relations develop for different sub-populations.

David Shapiro, Andrew Hinde

Josimar Gonçalves de Jesus, Rodolfo Hoffmann

Resumo Num primeiro momento, este estudo atualiza e reconstrói, sob alguns aspectos, a análise realizada por Soares (2008) sobre o fenômeno da mudança na identificação racial que vem ocorrendo no Brasil desde o início dos anos 2000. Num segundo momento, expandindo tal análise, investiga-se a existência de diferenças regionais nesse processo. Os resultados mostram que os aumentos observados na porcentagem de negros no total do país e nas suas cinco macrorregiões foram devidos quase que exclusivamente à mudança ocorrida no modo como as próprias pessoas se veem. Quanto ao efeito da mudança na identificação racial sobre o aumento no número de negros, verificou-se que este varia consideravelmente de região para região, sendo mais forte no Sul e Sudeste. Por fim, argumenta-se que qualquer análise intertemporal, a partir dos anos 2000, que envolva autodeclaração de cor ou raça, é potencialmente afetada por esse processo de mudança na identificação racial.

Anjali Singh, Rakesh Mishra, K. K. Singh et al.

Abstract This study tries to elicit the unexplored nexus between the contraceptive failure and abortions in India’s most populous state Uttar Pradesh. Condom being the most used modern method contributes to 47% of all observed episodes in the calendar period. Of all accidental pregnancies occurred because of modern contraceptive failures, 35.2% terminated with induced abortions. Gross failure rate of modern methods is conspicuously higher for urban poor, uneducated women, and marginalized section of population living in urban area. From a total of 7496 episodes of all reversible methods, abortions are estimated as high as 29.2%, whereas for the traditional method it accounts for around 21%. The empirical evidence of induced abortions due to contraceptive failure put serious concern regarding unintended pregnancies and hence discourages fertility intentions among the potential couples.

Kamran Ali, Mohammed Alloulah, Fahim Kawsar et al.

WiFi channel state information (CSI) has emerged as a plausible modality for sensing different human vital signs, i.e. respiration and body motion, as a function of modulated wireless signals that travel between WiFi devices. Although a remarkable proposition, most of the existing research in this space struggles to withstand robust performance beyond experimental conditions. To this end, we take a careful look at the dynamics of WiFi signals under human respiration and body motions in the wild. We first characterize the WiFi signal components - multipath and signal subspace - that are modulated by human respiration and body motions. We extrapolate on a set of transformations, including first-order differentiation, max-min normalization and component projections, that faithfully explains and quantifies the dynamics of respiration and body motions on WiFi signals. Grounded in this characterization, we propose two methods: 1) a respiration tracking technique that models the peak dynamics observed in the time-varying signal subspaces and 2) a body-motion tracking technique built with a multi-dimensional clustering of evolving signal subspaces. Finally, we reflect on the manifestation of these techniques in a practical sleep monitoring application. Our systematic evaluation with over 550 hours of data from 5 users covering both line-of-sight (LOS) and non-line-of-sight (NLOS) settings shows that the proposed techniques can achieve comparable performance to purpose-built pulse-Doppler radar.