Hasil untuk "Demography. Population. Vital events"

Daniel Roberts, Francesco Shankar, Vieri Cammelli et al.

Recent JWST observations have unveiled a numerous population of low-luminosity active galactic nuclei (AGN) at $4< z<10$, with space densities roughly an order of magnitude above pre-JWST estimates, and many of these AGN have masses orders of magnitude above the local black hole mass-stellar mass ($M_{\rm BH}-M_{*}$) scaling relations. We investigate the consistency of these observations within a data-driven framework that links the galaxy stellar mass function to the supermassive black hole (SMBH) mass function and AGN luminosity functions using different $M_{\rm BH}-M_{*}$ relations and the observed Eddington-ratio distribution. By comparing our predictions against observed AGN luminosity functions at $z\sim 5.5$ we find that observations can be reproduced either by highly-elevated $M_{\rm BH}-M_{*}$ relations paired with low duty cycles, or moderate relations with higher duty cycles. Through the Soltan argument, we find that $M_{\rm BH}-M_{*}$ relations that are modestly above the local relation for AGN produce consistency between multiple tracers of the SMBH demography at $z\sim 5.5$, while more extreme normalisations would require a weakly-evolving luminosity function at $z> 5.5$. Continuity-equation modelling shows that initially high $M_{\rm BH}-M_{*}$ relations predict a strong two-phase evolutionary scenario and very steep low-mass SMBH mass functions in tension with several current estimates, while more moderate relations generate local SMBH mass functions in better agreement with present determinations and near-constant scaling relations. Our results favour a scenario where SMBHs at $z \sim 5$ on average lie modestly above local AGN scaling relations, with elevated but physically plausible duty cycles. Future wide-field clustering and demographic studies will help break the remaining degeneracies between SMBH scaling relations and AGN duty cycles at early cosmic times.

Randall S. Wells, Aleta A Hohn, Michael D. Scott et al.

Studies of the resident community of bottlenose dolphins (Tursiops truncatus) of Sarasota Bay, Florida, have been conducted for more than 50 years. Detailed histories have been collected for resident individuals through integrated observations, systematic photographic identification surveys, tagging and tracking, catch-and-release health assessments, remote biopsy sampling, and stranding response. This has produced a unique dataset documenting life history milestones and vital rates of a small cetacean. Analyses of data from 482 resident Sarasota Bay dolphins have revealed estimated maximum life spans of 67 years for females and 52 years for males. For females, predicted age at sexual maturation is 8.5 years, with a predicted age at first reproduction of 9.6 years. Females were observed to give birth when 6-48 years of age, and have been documented with as many as 12 calves, with 45% observed post-separation. Ten percent of females were considered to be reproductively senescent, having gone >13 years without producing a calf. For males, predicted age at sexual maturation is 10 years. Males 10-43 years old sired calves, producing up to 7 calves each. The average calving interval was 3.5 years, albeit with effects due to mother’s age, birth order, and calf survival. Seasonal reproduction was evident, with 81% of births occurring during May-July. Mean annual birth rate was 0.071. Mean annual fecundity was 0.182 births/adult female (defined as females 6 yrs or older). Recruitment rate through reproduction was estimated to be 0.050 based on calves surviving their first year. Immigration was infrequent, with an estimated annual rate of 0.003-0.013. Estimated mean annual maximum loss rate, from mortality, emigration, and changed identification characteristics, was 0.072. Periods of increased loss rates were related to environmental events, and factors that may be important to long-term population resilience were suggested.

Giorgos Tzigkounakis, Jonathan Brown

Background: Despite the rapid development and distribution of COVID-19 vaccines, the pandemic continues to challenge global health systems. With vaccine inequity and hesitancy, especially in low-income populations and specific demographic cohorts, alternative therapeutic strategies to mitigate COVID-19 symptoms and reduce viral clearance time remain vital. Propolis, a natural bee product with immunomodulatory and antiviral properties, has demonstrated efficacy against other viral pathogens, suggesting potential as an adjunctive therapy for COVID-19. Objectives: This study protocol outlines a randomized, triple-blind, placebo-controlled clinical trial to assess the efficacy of a Greek propolis hydroalcoholic extract as an adjunct to standard care in hospitalized COVID-19 patients. The primary objectives are to evaluate the extract’s impact on viral clearance time and hospitalization duration, with secondary objectives examining body temperature, cough severity, quality of life, and safety. Methods: A total of 441 severe acute respiratory syndrome coronavirus 2-positive adult patients will be enrolled and stratified by age and vaccination status. Participants will be randomly assigned to one of three arms: (i) propolis extract, (ii) placebo, or (iii) control (standard care only). Primary outcomes include time to negative reverse transcription polymerase chain reaction tests and hospital discharge. Secondary measures involve cough severity and quality-of-life assessments through Visual Analog Scale and Leicester Cough Questionnaire scores, fever duration and resolution patterns, and safety through adverse events and mortality tracking. Statistical analysis will include Kaplan–Meier survival curves, Cox regression for confounders, and analysis of variance for quality-of-life scores. Conclusion: This study aims to validate the therapeutic potential of propolis as a natural, accessible adjunctive treatment for COVID-19. Findings may provide critical evidence supporting propolis in symptom relief, viral clearance, and healthcare burden reduction in resource-limited settings. Relevance for patients: Participants in the intervention arm may experience improved clinical outcomes, such as faster recovery and symptom alleviation, while all patients will continue to receive standard care in alignment with current clinical protocols.

Shuai Sun, Chong-Xi Liang, Chengwei Ye et al.

Accurate phase demodulation is critical for vital sign detection using millimeter-wave radar. However, in complex environments, time-varying DC offsets and phase imbalances can severely degrade demodulation performance. To address this, we propose a novel DC offset calibration method alongside a Hilbert and Differential Cross-Multiply (HADCM) demodulation algorithm. The approach estimates time-varying DC offsets from neighboring signal peaks and valleys, then employs both differential forms and Hilbert transforms of the I/Q channel signals to extract vital sign information. Simulation and experimental results demonstrate that the proposed method maintains robust performance under low signal-to-noise ratios. Compared to existing demodulation techniques, it offers more accurate signal recovery in challenging scenarios and effectively suppresses noise interference.

Jewel Benny, Narahari N. Moudhgalya, Mujeev Khan et al.

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system's potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

Eduardo Vital, Jean-Marc Gratien, Yassine Ayoun et al.

When simulating multiscale systems, where some fields cannot be fully prescribed despite their effects on the simulation's accuracy, closure models are needed. This phenomenon is observed in turbulent fluid dynamics, where Large Eddy Simulations (LES) depict global behavior while turbulence modeling introduces dissipation correspondent to smaller sub-grid scales. Recently, scientific machine learning techniques have emerged to address this problem by integrating traditional (physics-based) equations with data-driven (machine-learned) models, typically coupling numerical solvers with neural networks. This work presents a comprehensive workflow, encompassing high-fidelity data generation and post-processing, a priori learning, and a posteriori testing, where data-driven models enrich differential equations.

Iuliia Grotova, Arne Rau, Pietro Baldini et al.

This paper presents a systematic study of X-ray-selected canonical tidal disruption events (TDEs) discovered in the western Galactic hemisphere of the first two eROSITA all-sky surveys (eRASS1 and eRASS2) performed between Dec 2019 and Dec 2020. We compiled a TDE sample from the catalog of eROSITA's extragalactic transients and variables eRO-ExTra, which includes X-ray sources with a variability significance and fractional amplitude over four between eRASS1 and eRASS2, not associated with known AGNs. Each X-ray source is associated with an optical counterpart from the Legacy Survey DR10. Canonical TDEs were selected based on their X-ray light-curve properties (single flare or decline), soft X-ray spectra ($Γ>3$), and the absence of archival X-ray variability and AGN signatures in their host photometry and spectroscopy. The sample includes 31 X-ray-selected TDE candidates with redshifts of $0.02< z<0.34$ and luminosities of $5.7 \times 10^{41}<L_X<5.3 \times 10^{44}$ erg/s in the 0.2-6.0 keV rest frame, of which 30 are canonical TDEs and one is an off-nuclear TDE candidate. The derived X-ray luminosity function is best fit by a double power law with a luminosity break at $10^{44}$ erg/s, corresponding to the Eddington-limiting prediction. This corresponds to a TDE volumetric rate of $ (2.3^{+1.2}_{-0.9})\times10^{-7}\,Mpc^{-3} yr^{-1}$ ($\approx1.2\times 10^{-5}$ events per galaxy per year). TDE host galaxies show a green-valley overdensity. In addition, 20%, 30%, and 15% of the sample exhibit flares in the optical, mid-infrared (mid-IR), or radio bands, respectively. We discuss the differences between X-ray, optical, and mid-IR TDE populations and the origins of multiwavelength flares in the context of the obscuring envelope and stream-stream collision models. Finally, we highlight TDE subpopulations that are not included in the canonical sample and should be explored in the future.

Daniel M. Hartung, Caitlin M. McCracken, T. Nguyen et al.

INTRODUCTION Among individuals who are released from prison, opioid overdose is a leading cause of death with a risk more than ten-fold the general population. Although the epidemiology of opioid-related fatalities has been described, few studies have characterized both fatal and nonfatal opioid-related poisonings. The objective of this study was to estimate risk of fatal and nonfatal opioid overdose among adults released from prison. METHODS The study estimated fatal and nonfatal opioid overdose rates using linked corrections, Medicaid, hospital discharge, and vital statistics from the state of Oregon from 2014 to 2018. Multivariable proportional hazards models identified demographic and prison-related factors associated with overdose. RESULTS Between 2014 and 2017, 18,258 individuals were released from prison. A majority of individuals were male (87 %) and ages 26 to 64 (83 %). Two-thirds had a documented substance use disorder treatment need and 20 % demonstrated mental health treatment need. Following prison release, 579 opioid overdose events occurred; 65 (11 %) were fatal. The rate of opioid overdose was 1085.7 per 100,000 person-years (PY). Rates were highest in the first two weeks (2286.7 per 100,000 PY), among women (1582.9 per 100,000 PY), and those with mental health (1624.3 per 100,000 PY) or substance use disorder treatment needs (1382.6 per 100,100 PY). Only mental health (adjusted hazard ratio [aHR] 1.54, 95 % CI 1.24 to 1.90) and substance use need (aHR 2.59; 95 % CI 2.01 to 3.34) remained significant in multivariable models. CONCLUSIONS The rate of opioid overdose is markedly elevated after prison release, particularly in the first two weeks. In women, the higher rate of opioid overdose is mediated by a greater mental health burden.

Benjamin R. Griffin, Avinash Mudireddy, Benjamine Horne et al.

Rationale and Objective Acute kidney injury (AKI) is a common complication among hospitalized adults, but AKI prediction and prevention among adults has proved challenging. We used machine learning to update the nephrotoxic injury negated by just-in time action (NINJA), a pediatric program that predicts nephrotoxic AKI, to improve accuracy among adults. Study Design A retrospective cohort study. Setting and Population Adults admitted for > 48 hours to the University of Iowa Hospital from 2017 to 2022. Exposure A NINJA high-nephrotoxin exposure (≥3 nephrotoxins on 1 day or intravenous aminoglycoside or vancomycin for ≥3 days). Outcomes AKI within 48 hours of high-nephrotoxin exposure. Analytical Approach We collected 85 variables, including demographics, laboratory tests, vital signs, and medications. AKI was defined as a serum creatinine increase of ≥0.3 mg/dL. A gated recurrent unit (GRU)-based recurrent neural network (RNN) was trained on 85% of the data, and then tested on the remaining 15%. Model performance was evaluated with precision, recall, negative predictive value, and area under the curve. We used an artificial neural network to determine risk factor importance. Results There were 14,480 patients, 18,180 admissions, and 37,300 high-nephrotoxin exposure events meeting inclusion criteria. In the testing cohort, 29% of exposures developed AKI within 48 hours. The RNN-GRU model predicted AKI with a precision of 0.60, reducing the number of false alerts from 2.5 to 0.7 per AKI case. Lowest hemoglobin, lowest blood pressure, and highest white blood cell count were the most important variables in the artificial neural network model. Acyclovir, piperacillin-tazobactam, calcineurin inhibitors, and angiotensin-converting enzyme inhibitor/angiotensin receptor blockers were the most important medications. Limitations Clinical variables and medications were not exhaustive, drug levels or dosing were not incorporated, and Iowa’s racial makeup may limit generalizability. Conclusions Our RNN-GRU model substantially reduced the number of false alerts for nephrotoxic AKI, which may facilitate NINJA translation to adult hospitals by providing more targeted intervention.

T. McConnell, K. Gillespie, N. Potvin et al.

Informal carers of terminally ill patients play a vital role in providing palliative care at home, which impacts on their pre- and post-death bereavement experience and presents an up to 50% greater risk for mental-health problems. However, developing and implementing effective bereavement support remains challenging. There is a need to build the evidence base for music therapy as a potentially promising bereavement support for this vulnerable population. This study aimed to co-design an international best practice agenda for research into music therapy for informal carers of patients pre- and post-death bereavement. Online half day workshop using a World Café approach; an innovative method for harnessing group intelligence within a group of international expert stakeholders (music therapy clinicians and academics with experience of music therapy with informal carers at end-of-life). Demographics, experience, key priorities and methodological challenges were gathered during a pre-workshop survey to inform workshop discussions. The online workshop involved four rounds of rotating, 25-minute, small group parallel discussions using Padlet. One final large group discussion involved a consensus building activity. All data were analysed thematically to identify patterns to inform priorities and recommendations. Twenty-two consented and completed the pre-event survey (response rate 44%), from countries representing 10 different time zones. Sixteen participated in the workshop and developed the following best practice agenda. The effectiveness of music therapy in supporting informal carers across the bereavement continuum should be prioritised. This should be done using a mixed methods design to draw on the strengths of different methodological approaches to building the evidence base. It should involve service users throughout and should use a core outcome set to guide the choice of clinically important bereavement outcome measures in efficacy/effectiveness research. Findings should inform future pre- and post-death bereavement support research for informal caregivers of terminally ill patients. This is an important step in building the evidence base for commissioners and service providers on how to incorporate more innovative approaches in palliative care bereavement services.

Madeline E Moberg, Erin B Hamilton, S. Zeng et al.

Summary Background Unintentional carbon monoxide poisoning is a largely preventable cause of death that has received insufficient attention. We aimed to conduct a comprehensive global analysis of the demographic, temporal, and geographical patterns of fatal unintentional carbon monoxide poisoning from 2000 to 2021. Methods As part of the latest Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), unintentional carbon monoxide poisoning mortality was quantified using the GBD cause of death ensemble modelling strategy. Vital registration data and covariates with an epidemiological link to unintentional carbon monoxide poisoning informed the estimates of death counts and mortality rates for all locations, sexes, ages, and years included in the GBD. Years of life lost (YLLs) were estimated by multiplying deaths by remaining standard life expectancy at age of death. Population attributable fractions (PAFs) for unintentional carbon monoxide poisoning deaths due to occupational injuries and high alcohol use were estimated. Findings In 2021, the global mortality rate due to unintentional carbon monoxide poisoning was 0·366 per 100 000 (95% uncertainty interval 0·276–0·415), with 28 900 deaths (21 700–32 800) and 1·18 million YLLs (0·886–1·35) across all ages. Nearly 70% of deaths occurred in males (20 100 [15 800–24 000]), and the 50–54-year age group had the largest number of deaths (2210 [1660–2590]). The highest mortality rate was in those aged 85 years or older with 1·96 deaths (1·38–2·32) per 100 000. Eastern Europe had the highest age-standardised mortality rate at 2·12 deaths (1·98–2·30) per 100 000. Globally, there was a 53·5% (46·2–63·7) decrease in the age-standardised mortality rate from 2000 to 2021, although this decline was not uniform across regions. The overall PAFs for occupational injuries and high alcohol use were 13·6% (11·9–16·0) and 3·5% (1·4–6·2), respectively. Interpretation Improvements in unintentional carbon monoxide poisoning mortality rates have been inconsistent across regions and over time since 2000. Given that unintentional carbon monoxide poisoning is almost entirely preventable, policy-level interventions that lower the risk of carbon monoxide poisoning events should be prioritised, such as those that increase access to improved heating and cooking devices, reduce carbon monoxide emissions from generators, and mandate use of carbon monoxide alarms. Funding Bill & Melinda Gates Foundation.

Anthony J. Roberts, David L. Fronczak, F. Baldwin et al.

Attaching leg bands to birds directly before fall hunting seasons is a primary component of monitoring waterfowl in North America. While capture and recovery data are primarily used for estimating survival and harvest distribution, these data may be used to estimate age ratios and other demographic rates, especially if recapture data are available from subsequent trapping and release events. We estimated recapture rates of mallards Anas platyrhynchos and wood ducks Aix sponsa and used those rates to estimate differential capture vulnerability, which is vital to estimate true age ratios from banding data. Posterior estimates of mallard age and sex cohort and location-specific recapture rates varied among capture locations from a mean of 0.004 (0.002- 0.008 95% credible interval) to 0.547 (0.486-0.609). Ratios of recapture rates among cohorts also varied, meaning no single differential vulnerability estimate would be useable across the study area. Our estimates of differential capture vulnerability for mallards, using the ratio of recapture probabilities, averaged 2.64 for adult female to adult male and 5.42 for juvenile female to adult female, with significant variation. Wood duck cohort-specific recapture rates were similar across locations. Similar wood duck recapture rates resulted in similar estimates of differential vulnerability, 1.24 for the adult female to adult male ratio and 1.30 for juvenile female to adult female. The wide range of recapture rate estimates we found for mallards suggests location- specific characteristics may have a strong effect on capture probability. Differences in recapture rates and apparent survival likely resulting from emigration suggest if recapture data are to be used in population modeling, location-specific information is needed. The ability to monitor multiple demographic parameters using a single scheme improves continued assessment of population status. We recommend increased collection of in-season recapture data by biologists during active banding operations. Banders should be aware of the potential value of live, in- season, encounters in monitoring populations and modeling demographic rates.

Sarah J Aldridge, Utkarsh Agrawal, Siobhán Murphy et al.

Background and Objectives SARS-CoV-2 infection in children and young people (CYP) can lead to life threatening conditions including COVID-19, transmission to more vulnerable individuals, or even long COVID. Vaccination against COVID-19 reduces the chance of infection and transmission of the virus, but vaccine uptake in the UK has been shown to decrease with age. Approach We undertook a multistate-model approach to estimate hazard ratios on national cohorts constructed from linked health and administrative data, adjusting for several demographic factors. The models were applied to 3,433,483 CYP aged 5-17 years between 4th August 2021 and 31st May 2022. The results were combined in a random effects meta-analysis. Results Uptake of the first COVID-19 vaccine in CYP was lower compared to older age groups in the UK, and diminished further with subsequent doses (34%, 20% and 2% for 1st, 2nd, and booster dose respectively). Age of the CYP and vaccination status of the adults in the household were identified as important risk factors. For example, 5-11-year-olds were less likely to receive their first vaccine compared to 16-17-year-olds (adjusted Hazard Ratio [aHR]: 0.10 (95%CI: 0.06-0.19), and CYP in unvaccinated households were less likely to receive their first vaccine compared to partially vaccinated households (aHR: 0.19, 95%CI 0.13-0.29). Conclusions and Implications  Our work highlights the need for targeted strategies to increase COVID-19 vaccine acceptance and uptake among CYP, especially considering the influence of parental consent and household factors. Further research could help identify risk factors in household types to investigate more direct associations, which may be influenced by factors such as living with vulnerable people, familial structure, or deprivation.

Maryam Ahmadpoortorkamani, Alexei Cheviakov

Contemporary epidemiological models often involve spatial variation, providing an avenue to investigate the averaged dynamics of individual movements. In this work, we extend a recent model by Vaziry, Kolokolnikov, and Kevrekidis [Royal Society Open Science 9 (10), 2022] that included, in both infected and susceptible population dynamics equations, a cross-diffusion term with the second spatial derivative of the infected population density. Diffusion terms of this type occur, for example, in the Keller-Siegel chemotaxis model. The presented model corresponds to local orderly commute of susceptible and infected individuals, and is shown to arise in two dimensions as a limit of a discrete process. The present contribution identifies and studies specific features of the new model's dynamics, including various types of infection waves and buffer zones protected from the infection. The model with vital dynamics additionally exhibits complex spatiotemporal behaviour that involves the generation of quasiperiodic infection waves and emergence of transient strongly heterogeneous patterns.

Yuwei Liu, Chen Dan, Anubhav Bhatti et al.

Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict sepsis further aggravates this issue. While deep learning techniques have been advanced for early sepsis prediction, their 'black-box' nature obscures the internal logic, impairing interpretability in critical settings like ICUs. This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical time steps in the forecasting process, thus improving model interpretability and supporting clinical decision-making. We show that the attention mechanism could be adapted to various black box time series forecasting models such as N-HiTS and N-BEATS. Our method preserves the accuracy of conventional deep learning models while enhancing interpretability through attention-weight-generated heatmaps. We evaluated our model on the eICU-CRD dataset, focusing on forecasting vital signs for sepsis patients. We assessed its performance using mean squared error (MSE) and dynamic time warping (DTW) metrics. We explored the attention maps of N-HiTS and N-BEATS, examining the differences in their performance and identifying crucial factors influencing vital sign forecasting.

Brandon C. Cummings, Joseph M Blackmer, J. Motyka et al.

OBJECTIVES: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN: PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING: The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS: The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE’s performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861–0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275–0.320) at the first hospital; AUROC 0.875 (0.851–0.902), AUPRC 0.339 (0.281–0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS: Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.

Khandaker Jafor Ahmed, Yan Tan, Dianne M. Rudd

S. DellaPergola

As an essential prerequisite to the genealogical study of Jews, some elements of Jewish demographic history are provided in a long-term transnational perspective. Data and estimates from a vast array of sources are combined to draw a profile of Jewish populations globally, noting changes in geographical distribution, vital processes (marriages, births and deaths), international migrations, and changes in Jewish identification. Jews often anticipated the transition from higher to lower levels of mortality and fertility, or else joined large-scale migration flows that reflected shifting constraints and opportunities locally and globally. Cultural drivers typical of the Jewish minority interacted with socioeconomic and political drivers coming from the encompassing majority. The main centers of Jewish presence globally repeatedly shifted, entailing the intake within Jewish communities of demographic patterns from significantly different environments. During the 20th century, two main events reshaped the demography of the Jews globally: the Shoah (destruction) of two thirds of all Jews in Europe during World War II, and the independence of the State of Israel in 1948. Mass immigration and significant convergence followed among Jews of different geographical origins. Israel’s Jewish population grew to constitute a large share—and in the longer run—a potential majority of all Jews worldwide. Since the 19th century, and with increasing visibility during the 20th and the 21st, Jews also tended to assimilate in the respective Diaspora environments, leading to a blurring of identificational boundaries and sometimes to a numerical erosion of the Jewish population. This article concludes with some implications for Jewish genealogical studies, stressing the need for contextualization to enhance their value for personal memory and for analytic work.

Hannah Dickson, George Vamvakas, Roxanna Short et al.

Objectives The age-crime curve indicates that criminal behaviour peaks in adolescence and decreases in adulthood, but longitudinal studies suggest that this curve conceals distinct patterns of (re)-offending or trajectories. Some trajectories (e.g., life course persistent offenders) are reported to have distinct risk factors and more negative outcomes than others (e.g., adolescent limited offenders). Methods The current study had two main objectives: (1) To use UK administrative crime data to identify trajectories of (re)-offending; and (2) To prospectively identify (re)-offending trajectories using longitudinal administrative education and social care data. This project uses linked UK administrative data containing the anonymised education and social care records for individuals born between September 1985 and August 1999, which have been linked to later official crime records up to the end of 2017. To identify offending trajectories, we used information on offence type, age of first conviction/caution, age of last recorded conviction/caution and offending history at three age points (Juvenile: 10-17 years; Young adult: 18-20 years; Adult: 21-32 years). Results Latent Class Analyses with and without ‘Gender’ and ‘Ever served a custodial sentence’ as covariates was conducted to identify trajectories of (re)-offending. We are currently developing statistical models to see if we can use prospective longitudinal education and social care factors to discriminate between these trajectories. In my talk, I will share findings on the offending trajectories identified and present some early results on the key education and social care drivers of the offending trajectories. Conclusion Findings from this study has the potential to provide deeper insights into how these education and social care factors might affect (re)-offending patterns. This could inform education, social care and criminal justice system responses to offending behaviours which seek to reduce offending and its associated social and economic costs.

Оксана Альфредовна Хараева

В книге «Адаптация и интеграция мигрантов в России: вызовы, реалии, индикаторы» под редакцией В.И. Мукомеля и К.С. Григорьевой представлены результаты исследования, продолжавшегося в период с 2020 по 2022 г. в рамках Программы фундаментальных и прикладных научных исследований по теме «Этнокультурное многообразие российского общества и укрепление общероссийской идентичности» и посвященного изучению процессов адаптации и интеграции мигрантов в различных областях политико-правовой, социально-экономической и культурной сферы Российской Федерации. Основное внимание в работе уделено разработке системы индикаторов для оценки процесса интеграции иностранных граждан.