BACKGROUND: Mathematical kinship demography is an expanding area of research. Recent papers have explored the expected number of kin a typical individual should experience. Despite the uncertainty of the future number and distributions of kin, just one paper investigates it. OBJECTIVE: We aim to develop a new method for obtaining the probability that a typical population member experiences one or more of some kin at any age through the life course. METHODS: Combinatorics, matrix algebra, and convolution theory are combined to find discrete probability distributions of kin number. We propose closed form expressions, illustrating the recursive nature of kin replenishment, using composition of matrix operations. Our model requires as inputs age-specific mortality and fertility. CONCLUSIONS: We derive probabilities of kin number for fixed age of kin and over all possible ages of kin. From these the expectation, variance, and other moments of kin number can be found. We demonstrate how kinship structures are conditional on familial events. CONTRIBUTION: The paper presents the first analytic approach allowing the projection of a full probability distribution of the number of kin of arbitrary type that a population member has over the life course.
Objectives
Record linkage can be viewed as a classification task where pairs of records are classified as matches (same individual) or non-matches (different individuals). Alternatively, clustering methods generate groups of records each referring to one person. We discuss methods to evaluate the quality of large-scale population linkages without ground truth data.
Methods
In practical applications of record linkage, ground truth data are often not available, or they can be incomplete or biased, making quality evaluation challenging. To overcome this gap, we present multiple methods to evaluate the quality of a record linkage outcome. These methods are either applicable to one-to-one linkages, or they consider clustering results by taking the similarities (match scores) calculated between records and the structure of a similarity graph into account. We apply our methods on linkage outcomes from a large historical population database where we consider multiple one-to-one, one-to-many, and many-to-many linkage types.
Results
We apply our methods on both publicly available data sets where the linkage ground truth is available, as well as a large historical population database. With the former data sets, depending upon the structure and quality of the similarity graph they are applied on, our methods can accurately estimate precision (positive predictive value) and recall (sensitivity) results. While precision is sometimes overestimated, the opposite happens with recall. As a result, on average our best methods provide estimated F-measure results within 7% of the corresponding ground truth-based results. We then apply our methods on different linkage types conducted on a large population database containing over 20 million records, and we report on the quality obtained when several different linkage methods are applied on this large database.
Conclusion
Evaluating the quality obtained for a linkage outcome is challenging when no ground truth data are available. We have shown how our unsupervised evaluation methods can provide linkage quality estimates close to the actual ground truth results, and how our methods can be applied on large-scale population linkage outcomes.
Li Rong Wang, Thomas C. Henderson, Yew Soon Ong
et al.
Vital signs, such as heart rate and blood pressure, are critical indicators of patient health and are widely used in clinical monitoring and decision-making. While deep learning models have shown promise in forecasting these signals, their deployment in healthcare remains limited in part because clinicians must be able to trust and interpret model outputs. Without reliable uncertainty quantification -- particularly calibrated prediction intervals (PIs) -- it is unclear whether a forecasted abnormality constitutes a meaningful warning or merely reflects model noise, hindering clinical decision-making. To address this, we present two methods for deriving PIs from the Reconstruction Uncertainty Estimate (RUE), an uncertainty measure well-suited to vital-sign forecasting due to its sensitivity to data shifts and support for label-free calibration. Our parametric approach assumes that prediction errors and uncertainty estimates follow a Gaussian copula distribution, enabling closed-form PI computation. Our non-parametric approach, based on k-nearest neighbours (KNN), empirically estimates the conditional error distribution using similar validation instances. We evaluate these methods on two large public datasets with minute- and hour-level sampling, representing high- and low-frequency health signals. Experiments demonstrate that the Gaussian copula method consistently outperforms conformal prediction baselines on low-frequency data, while the KNN approach performs best on high-frequency data. These results underscore the clinical promise of RUE-derived PIs for delivering interpretable, uncertainty-aware vital sign forecasts.
This paper explores the deployment of mm-wave Frequency Modulated Continuous Wave (FMCW) radar for vital sign detection across multiple scenarios. We focus on overcoming the limitations of traditional sensing methods by enhancing signal processing techniques to capture subtle physiological changes effectively. Our study introduces novel adaptations of the Prony and MUSIC algorithms tailored for real-time heart and respiration rate monitoring, significantly advancing the accuracy and reliability of non-contact vital sign monitoring using radar technologies. Notably, these algorithms demonstrate a robust ability to suppress noise and harmonic interference. For instance, the mean absolute errors (MAE) for MUSIC and Prony in heart rate detection are 1.8 and 0.81, respectively, while for respiration rate, the MAEs are 1.01 and 0.8, respectively. These results underscore the potential of FMCW radar as a reliable, non-invasive solution for continuous vital sign monitoring in healthcare settings, particularly in clinical and emergency scenarios where traditional contact-based monitoring is impractical.
The discovery of supermassive black holes (SMBHs) at high redshifts has intensified efforts to understand their early formation and rapid growth during the cosmic dawn. Using a semi-analytical cosmological framework, we investigate the role of tidal disruption events (TDEs) involving Population III (Pop-III) stars in driving the growth of heavy seed black holes (10^4-10^6 solar mass). Our results indicate that Pop-III TDEs significantly accelerate the growth of relatively lighter massive black holes (~ 10^4-10^5 solar mass), allowing them to increase their mass by roughly an order of magnitude within the first 10 Myr. Cosmological evolution modeling further supports that such Pop-III TDE-driven growth scenarios are consistent with the formation pathways of observed luminous high-redshift quasars originating from seed black holes at 10<z<15. We also discuss the future observational probes of these early-stage growth processes that future facilities, including space-based gravitational wave observatories and infrared telescopes like JWST, could potentially detect. These findings provide a clear observational framework to test the critical role of Pop-III star interactions in the rapid buildup of SMBHs during the earliest epochs.
In the emerging post-pandemic era (the ‘wavelet’ era), humans must coexist with viruses for the foreseeable future, and personal protective behaviors will largely replace national-level preventive measures. In this new normal, encouraging the public to implement proper personal protective behaviors against the coronavirus disease (COVID-19) is vital to the sustainable development of cities and communities. This knowledge–attitude–practice (KAP) survey conducted in Chengdu (N = 900) narrowed the knowledge gap regarding post-pandemic public practices of protective behavior. Findings show that:(1) approximately 1/3 of the respondents are currently not concerned about COVID-19 at all; (2) respondents with different demographics and individual COVID-19-related factors showed significant differences in practice behaviors indoors and outdoors; (3) vulnerable groups performed better in practice behavior indoors/outdoors; (4) because the public may relax their vigilance outdoors, public places may become a transmission threat in the next outbreak; (5) attitudes are important, but limited incentives for practice; and (6) when knowledge increases beyond a threshold (68.75–75% in this study), protective behaviors decrease. Our results suggest that authorities must continue to educate and motivate the public, extending measures to cover personal protective practices, and have targeted policies for specific demographics to ensure equity in healthcare in the event of another pandemic (COVID-19 and alike crisis). Besides, comparing the results of the current study with similar studies conducted in other parts of the world can provide insights into how different populations respond to and adopt COVID-19 protective behaviors. The epidemiologists can use the data collected by this and other KAP surveys to refine epidemiologic models, which can help predict the spread of the virus and the impact of interventions in different settings.
Can Yücel Karabay, Hakan Taşolar, Ayşegül Ülgen Kunak
et al.
Background: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is associated with an increased risk of thromboembolism, ischemic stroke, impaired quality of life, and mortality. The latest research that shows the prevalence and incidence of AF patients in Türkiye was the Turkish Adults’ Heart Disease and Risk Factors study, which included 3,450 patients and collected data until 2006/07.The Turkish Real Life Atrial Fibrillation in Clinical Practice (TRAFFIC) study is planned to present current prevalence data, reveal the reflection of new treatment and risk approaches in our country, and develop new prediction models in terms of outcomes. Methods: The TRAFFIC study is a national, prospective, multicenter, observational registry. The study aims to collect data from at least 1900 patients diagnosed with atrial fibrillation, with the participation of 40 centers from Türkiye. The following data will be collected from patients: baseline demographic characteristics, medical history, vital signs, symptoms of AF, ECG and echocardiographic findings, CHADS2-VASC2 and HAS-BLED (1-year risk of major bleeding) risk scores, interventional treatments, antithrombotic and antiarrhythmic medications, or other medications used by the patients. For patients who use warfarin, international normalized ratio levels will be monitored. Follow-up data will be collected at 6, 12, 18, and 24 months. Primary endpoints are defined as systemic embolism or major safety endpoints (major bleeding, clinically relevant nonmajor bleeding, and minor bleeding as defined by the International Society on Thrombosis and Hemostasis). The main secondary endpoints include major adverse cardiovascular events (systemic embolism, myocardial infarction, and cardiovascular death), all-cause mortality, and hospitalizations due to all causes or specific reasons. Results: The results of the 12-month follow-up of the study are planned to be shared by the end of 2023. Conclusion: The TRAFFIC study will reveal the prevalence and incidence, demographic characteristics, and risk profiles of AF patients in Türkiye. Additionally, it will provide insights into how current treatments are reflected in this population. Furthermore, risk prediction modeling and risk scoring can be conducted for patients with AF.
Background: A challenge in administering BsAb is the step-up dosing (SUD) and careful monitoring required for signs and symptoms of cytokine release syndrome (CRS). Levine Cancer Institute has launched a care quality improvement initiative to explore the feasibility of giving BsAb SUD in an outpatient setting with support from our Hospital at Home (HaH) program. Herein, we describe our experience using this remote monitoring program as well as strategic prophylactic dexamethasone to mitigate the incidence and severity of CRS. Methods: HaH is an established program equipped with a home monitoring kit that includes a blood pressure cuff, pulse oximeter and thermometer. Patients recorded vital signs every 4 hours while awake or when feeling unwell and inputted them into an electronic tablet for the HaH team to review. Patients had in-person visits from a paramedic on the days between infusions in conjunction with a video visit with a HaH internist. The paramedics conducted the Immune Effector Cell Encephalopathy Score and provided oxygen, medications and/or intravenous fluids if necessary. A nurse was available 24/7 via electronic tablet to provide guidance to the patient. Patients took prophylactic dexamethasone 8mg on the day after each SUD to mitigate high-grade CRS. Patients could take acetaminophen or additional doses of Dex for breakthrough fever at the discretion of HaH or the primary oncologist. To be eligible, patients had to have a 24h caregiver, needed to reside within 1 hour from the hospital, could not have rapidly progressive disease or significant tumor burden, an eGFR <40 mL/min/1.73m2 or an absolute neutrophil count <1000. Demographic and clinical characteristics of all patients were reported via descriptive statistics. Continuous variables were reported as medians and ranges, while categorical variables were reported as frequencies and percentages. Results: Eighteen patients were included in this analysis of whom 10 received Teclistamab and 8 received Talquetamab. Seventy-two percent were male, and 22.2% were African American. The median age was 72.5 (range 56-83). Of the evaluable patients, 43.75% had ISS stage III disease and 58.8% harbored high-risk cytogenetics. The median number of prior lines of therapy was 5 (range 4-9). Eleven patients developed CRS, 7 of which were grade 1 and were managed entirely as an outpatient. Four patients developed grade 2 CRS requiring admission, of which 3 were administered a single dose of Tocilizumab. All 4 patients completed subsequent SUDs as an outpatient. The incidence of CRS was highest following earlier SUDs; only 1 patient developed CRS after SUD #3 and SUD#4 for Teclistamab and Talquetamab, respectively. Of the 11 patients who developed CRS, 89% had a single event. There were no ICANS or high-grade CRS. The median duration of CRS was 1 day (range 1-3). Four patients had dose delays due to ongoing CRS. During the follow up period, 2 patients visited the emergency room for concerns unrelated to CRS and 8 patients required admission: 4 for CRS management, 2 for CRS observation, and 2 for unrelated symptoms. The median number of days in the hospital across all admissions was 3 (range 1-6). Additional dex did lead to expected toxicities: hyperglycemia in 4 patients, hypertension in 3 patients and insomnia in 1 patient, all managed by the HaH program. Conclusions: If traditional SUD observation strategies had been used in this cohort, a total of 116 days in the hospital would have been recorded rather than the 19 days under the HaH program. A remote monitoring program can be safely implemented in a selected population of RRMM patients with few admissions for CRS. Prophylactic dexamethasone may mitigate the risk of high-grade CRS though precautions may need to be taken in patients with certain comorbidities.
Over the past two decades fatalities from opioid overdoses (OD) in the United States have increased at an alarming rate, and there exist regional disparities in the number of opioid fatalities within the US. A comprehensive understanding of the underlying factors contributing to this geographic variation remains elusive however. In this study, we consider the Social Vulnerability Index (SVI) census variables and their influence on opioid overdose within diverse geographic regions, each exhibiting different socio-economic factors. To investigate these disparate outcomes, we use Social Vulnerability Index data from the year 2018 alongside mortality data obtained from the CDC WONDER database.
The factors that contribute to opioid OD fatalities can vary from one region to another due to differences in socio-economic characteristics. To investigate these regional differences, we have chosen a set of variables for each region which are relevant to understanding the opioid OD fatalities in three different geographical regions: the Washington D.C metropolitan area, Wyoming, Tennessee. The impact of each factor on the epidemic's propagation varies between these regions however. To identify patterns in socioeconomic factors driving the opioid crisis, we applied methods from topological data analysis to identify potential correlations between social vulnerability factors and the distribution of drug overdose fatalities. In conclusion, our work highlights the importance of the socio-economic factors in understanding the dynamics of the opioid epidemic.
Objective
Construct an innovative open-learning solution that provides comprehensive training specific to Trusted Research Environments (TRE) and the broader research community of administrative data users, irrespective of their proficiency levels. DOL offers training opportunities tailored to meet each user's unique learning needs, enabling them to utilise complex, linked administrative datasets confidently and effectively for meaningful research outcomes, thereby building capacity for sustainable national data infrastructure.
Approach
DOL’s innovative open-learning solution offers two learning formats: adaptive and experiential. Adaptive learning provides registered users with bitesize self-paced training based on Administrative Data Research UK's priorities. Experiential learning involves online workgroups with real-world context and practical application. They meet twice a year and are designed around specific topics with frequent guest speakers who are experts in their fields.
Conclusion
DOL’s innovative open-learning solution empowers TREs, such as SAIL Databank, to provide well-rounded learning that fosters community support, knowledge-sharing, and networking opportunities for its users, while gathering valuable user feedback. Users can personalise learning and test their knowledge in a flexible training environment, allowing them to take charge of their learning journey.
Implication
In response to the increasing demand for training services from novice to advanced users, SAIL Databank adopted DOL's dual learning approach by (1) developing training courses that cover access, process, methodology, integration, and analytical tools for SAIL TRE users, (2) engaging users in a series of workgroups focused on themed datasets including Justice, Environmental, Maternal, Education, and Core Health.
Mariona Lozano, Albert Esteve, Diederik Boertien
et al.
BACKGROUND: Spain has one of the most enduring low levels of fertility in the world, but desired fertility there is still close to two children. OBJECTIVE: We document recent fertility trends and examine the reasons that women and men provide for not achieving their desired fertility. METHODS: We use data from the 2018 Spanish Fertility Survey (14,556 women and 2,619 men). We provide a cohort and age perspective and compare women and men. We use retrospective information and classify the reasons people report for not having (more) children. RESULTS: Estimates on observed fertility, employment, and partnerships show that having a stable partner between the ages of 25 and 35 seems key in the transition to childbearing. Work–family conflicts and insufficient economic resources are the main reasons women and men give for not having their desired number of children. These are followed by partnership reasons (not having a stable partner) and health (infertility). CONCLUSIONS: Our findings, although descriptive, shed light on the multiple and age-varying obstacles that prevent women and men from achieving desired levels of fertility. CONTRIBUTION: The Spanish population indicates that the most important preconditions for having (more) children are sufficient economic resources, stability, and having a partner.
Kanika Dheman, Marco Giordano, Cyriac Thomas
et al.
Sepsis is a significant cause of early mortality, high healthcare costs, and disability-adjusted life years. Digital interventions like continuous cardiac monitoring can help detect early warning signs and facilitate effective interventions. This paper introduces i-CardiAx, a wearable sensor utilizing low-power high-sensitivity accelerometers to measure vital signs crucial for cardiovascular health: heart rate (HR), blood pressure (BP), and respiratory rate (RR). Data collected from 10 healthy subjects using the i-CardiAx chest patch were used to develop and evaluate lightweight vital sign measurement algorithms. The algorithms demonstrated high performance: RR (-0.11 $\pm$ 0.77 breaths\min), HR (0.82 $\pm$ 2.85 beats\min), and systolic BP (-0.08 $\pm$ 6.245 mmHg). These algorithms are embedded in an ARM Cortex-M33 processor with Bluetooth Low Energy (BLE) support, achieving inference times of 4.2 ms for HR and RR, and 8.5 ms for BP. Additionally, a multi-channel quantized Temporal Convolutional Neural (TCN) Network, trained on the open-source HiRID dataset, was developed to detect sepsis onset using digitally acquired vital signs from i-CardiAx. The quantized TCN, deployed on i-CardiAx, predicted sepsis with a median time of 8.2 hours and an energy per inference of 1.29 mJ. The i-CardiAx wearable boasts a sleep power of 0.152 mW and an average power consumption of 0.77 mW, enabling a 100 mAh battery to last approximately two weeks (432 hours) with continuous monitoring of HR, BP, and RR at 30 measurements per hour and running inference every 30 minutes. In conclusion, i-CardiAx offers an energy-efficient, high-sensitivity method for long-term cardiovascular monitoring, providing predictive alerts for sepsis and other life-threatening events.
Trajectory forecasting in healthcare data has been an important area of research in precision care and clinical integration for computational methods. In recent years, generative AI models have demonstrated promising results in capturing short and long range dependencies in time series data. While these models have also been applied in healthcare, most of them only predict one value at a time, which is unrealistic in a clinical setting where multiple measures are taken at once. In this work, we extend the framework temporal fusion transformer (TFT), a multi-horizon time series prediction tool, and propose TFT-multi, an end-to-end framework that can predict multiple vital trajectories simultaneously. We apply TFT-multi to forecast 5 vital signs recorded in the intensive care unit: blood pressure, pulse, SpO2, temperature and respiratory rate. We hypothesize that by jointly predicting these measures, which are often correlated with one another, we can make more accurate predictions, especially in variables with large missingness. We validate our model on the public MIMIC dataset and an independent institutional dataset, and demonstrate that this approach outperforms state-of-the-art univariate prediction tools including the original TFT and Prophet, as well as vector regression modeling for multivariate prediction. Furthermore, we perform a study case analysis by applying our pipeline to forecast blood pressure changes in response to actual and hypothetical pressor administration.
Priscilla Yu, Michael A. Skinner, Ivie D. Esangbedo
et al.
Background: Children with congenital and acquired heart disease are at a higher risk of cardiac arrest compared to those without heart disease. Although the monitoring of cardiopulmonary resuscitation quality and extracorporeal resuscitation technologies have advanced, survival after cardiac arrest in this population has not improved. Cardiac arrest prevention, using predictive algorithms with machine learning, has the potential to reduce cardiac arrest rates. However, few studies have evaluated the use of these algorithms in predicting cardiac arrest in children with heart disease. Methods: We collected demographic, laboratory, and vital sign information from the electronic health records (EHR) of all the patients that were admitted to a single-center pediatric cardiac intensive care unit (CICU), between 2010 and 2019, who had a cardiac arrest during their CICU admission, as well as a comparator group of randomly selected non-cardiac-arrest controls. We compared traditional logistic regression modeling against a novel adaptation of a machine learning algorithm (functional gradient boosting), using time series data to predict the risk of cardiac arrest. Results: A total of 160 unique cardiac arrest events were matched to non-cardiac-arrest time periods. Using 11 different variables (vital signs and laboratory values) from the EHR, our algorithm’s peak performance for the prediction of cardiac arrest was at one hour prior to the cardiac arrest (AUROC of 0.85 [0.79,0.90]), a performance that was similar to our previously published multivariable logistic regression model. Conclusions: Our novel machine learning predictive algorithm, which was developed using retrospective data that were collected from the EHR and predicted cardiac arrest in the children that were admitted to a single-center pediatric cardiac intensive care unit, demonstrated a performance that was similar to that of a traditional logistic regression model. While these results are encouraging, future research, including prospective validations with multicenter data, is warranted prior to the implementation of this algorithm as a real-time clinical decision support tool.
The birth and death rates of a population are among the crucial vital statistics for socio-economic policy planning in any country. Since the under-five mortality rate is one of the indicators for monitoring the health of a population, it requires regular and accurate estimation. The national demographic and health survey data, that are readily available to the puplic, have become a means for answering most health-related questions among African populations, using relevant statistical methods. However, many of such applications tend to ignore survey design effect in the estimations, despite the availability of statistical tools that support the analyses. Little is known about the amount of inaccurate information that is generated when predicting under-five mortality rates. This study estimates and compares the bias encountered when applying unweighted and weighted logistic regression methods to predict under-five mortality rate in Malawi using nationwide survey data. The Malawi demographic and health survey data of 2004, 2010, and 2015-16 were used to determine the bias. The analyses were carried out in R software version 3.6.3 and Stata version 12.0 . A logistic regression model that included various bio- and socio-demographic factors concerning the child, mother and households was used to estimate the under-five mortality rate. The results showed that accuracy of predicting the national under-five mortality rate hinges on cluster-weighting of the overall predicted probability of child-deaths, regardless of whether the model was weighted or not. Weighting the model caused small positive and negative changes in various fixed-effect estimates, which diffused the result of weighting in the fitted probabilities of deaths. In turn, there was no difference between the overall predicted mortality rate obtained using the weighted model and that obtained in the unweighted model. We recommend considering survey cluster-weights during the computation of overall predicted probability of events for a binary health outcome. This can be done without worrying about the weights during model fitting, whose aim is prediction of the population parameter.
BACKGROUND In the era of healthcare digital transformation, using electronic health record (EHR) data to generate various endpoint estimates for active monitoring is highly desirable in chronic disease management. However, traditional predictive modeling strategies leveraging well-curated data sets can have limited real-world implementation potential due to various data quality issues in EHR data. METHODS We propose a novel predictive modeling approach, GRU-D-Weibull, which models Weibull distribution leveraging gated recurrent units with decay (GRU-D), for real-time individualized endpoint prediction and population level risk management using EHR data. EXPERIMENTS We systematically evaluated the performance and showcased the real-world implementability of the proposed approach through individual level endpoint prediction using a cohort of patients with chronic kidney disease stage 4 (CKD4). A total of 536 features including ICD/CPT codes, medications, lab tests, vital measurements, and demographics were retrieved for 6879 CKD4 patients. The performance metrics including C-index, L1-loss, Parkes' error, and predicted survival probability at time of event were compared between GRU-D-Weibull and other alternative approaches including accelerated failure time model (AFT), XGBoost based AFT (XGB(AFT)), random survival forest (RSF), and Nnet-survival. Both in-process and post-process calibrations were experimented on GRU-D-Weibull generated survival probabilities. RESULTS GRU-D-Weibull demonstrated C-index of ~0.7 at index date, which increased to ~0.77 at 4.3 years of follow-up, comparable to that of RSF. GRU-D-Weibull achieved absolute L1-loss of ~1.1 years (sd≈0.95) at CKD4 index date, and a minimum of ~0.45 year (sd≈0.3) at 4 years of follow-up, comparing to second-ranked RSF of ~1.4 years (sd≈1.1) at index date and ~0.64 years (sd≈0.26) at 4 years. Both significantly outperform competing approaches. GRU-D-Weibull constrained predicted survival probability at time of event to smaller and more fixed range than competing models throughout follow-up. Significant correlations were observed between prediction error and missing proportions of all major categories of input features at index date (Corr ~0.1 to ~0.3), which faded away within 1 year after index date as more data became available. Through post training recalibration, we achieved a close alignment between the predicted and observed survival probabilities across multiple prediction horizons at different time points during follow-up. CONCLUSION GRU-D-Weibull shows advantages over competing methods in handling missingness commonly encountered in EHR data and providing both probability and point estimates for diverse prediction horizons during follow-up. The experiment highlights the potential of GRU-D-Weibull as a suitable candidate for individualized endpoint risk management, utilizing real-time clinical data to generate various endpoint estimates for monitoring. Additional research is warranted to evaluate the influence of different data quality aspects on prediction performance. Furthermore, collaboration with clinicians is essential to explore the integration of this approach into clinical workflows and evaluate its effects on decision-making processes and patient outcomes.
In Moldova, there has been a long-term decline in the population, mainly due to high levels of emigration. The article presents an analysis of population dynamics in Moldova over the last three decades, and estimates the contributions of fertility, mortality and migration to this process. Using population censuses, data on the population with usual residence, vital statistics and data on Moldovan immigrants from the host countries’ statistical institutes,we estimate population changes between 1991–2021, and present demographic projections up to 2040. The results show that migration outflows account for more than 90% of the depopulation trend, with high levels of premature mortality accelerating the natural decline. The fall in births is associated with a decrease in the reproductive-age population. The total fertility rate has been decreasing gradually, while the cohort fertility rates have not fallen below 1.75 live births per woman. Past migration and low fertility are projected to result in long-term population decline. Demographic ageing is expected to increase. While population decline cannot be stopped, its scale can be limited through reductions in emigration and mortality. This study on population decline in Moldova helps to complete the demographic picture of Europe in the 20th century and into the 21st century.
<p><strong>Background</strong></p> <p>As global climate change transforms average temperature and rainfall, species distributions may meet, increasing the potential for hybridization and altering individual fitness and population growth. Altered rainfall specifically may shift the strength and direction of selection, also manipulating population trajectories. Here, we investigated the role of interspecific hybridization and selection imposed by rainfall on the evolution of weedy life-history in non-hybrid (<em>Raphanus raphanistrum</em>) and hybrid (<em>R. raphanistrum x R. sativus</em>) populations using a life table response experiment.</p> <p><strong>Results</strong></p> <p>In documenting long-term population dynamics, we determined intrinsic (<em>r</em>) and asymptotic (<em>λ</em>) population growth rates and sensitivities, a measure of selection imposed on demographic rates. Hybrid populations experienced 8.7-10.3 times stronger selection than wild populations for increased seedling survival. Whereas crop populations generally exhibit little dormancy and wild populations often exhibit dormancy, non-hybrid populations experienced 10% stronger selection than hybrid populations for exhibiting seed dormancy. Selection on survival-to-flowering in wild, not hybrid, populations declined marginally with increasing soil moisture. Hybrid populations exhibited greater <em>r</em>, but not <em>λ</em>, than wild populations regardless of moisture environment. In general, fecundity contributed most to differences in λ but fecundity only contributed positively to hybrid λ relative to wild λ when precipitation was altered (either higher or lower than control) and not under control watering conditions.</p> <p><strong>Conclusions</strong></p> <p>Selection on key demographic traits may not change dramatically in response to rainfall, and hybridization may more strongly influence the demography of these weedy species than rainfall. If hybrid populations can respond to selection for increased dormancy, this may make it more difficult to deplete weed seed banks and increase the persistence of crop genes in weed populations.</p>
<b>Background</b>: Although literacy rates in India have improved for both men and women, less is known about the evolution of gender disparities across different levels of educational attainment. <b>Objective</b>: The goal is to determine whether gender gaps in schooling outcomes have narrowed, widened, or remained unchanged across birth cohorts. <b>Methods</b>: With a multinomial logit specification, we compare six education outcomes for people born to the 1956‒1960, 1961‒1965, …, and 1986‒1990 cohort groups. Our empirical tests indicate whether the gender gaps have narrowed, widened, or remained unchanged across the cohort groups. <b>Results</b>: We find evidence of narrowing gender gaps for some but not all education outcomes. The gender gaps narrow for not attending school, attending primary school, and primary school completion, but they persist for secondary school completion, attending college, and college completion. <b>Conclusions</b>: Although we observe improvements in the gender gaps in schooling outcomes toward the lower end of the education spectrum, gender inequities associated with higher levels of schooling persist across cohort groups. It is important to understand the causes of these patterns, as there are likely important policy considerations for India as it grapples with the interactions among technological change, a relatively young workforce, and persistent gendered norms and attitudes. <b>Contribution</b>: The paper makes two noteworthy contributions. First, we show that gender progress in schooling outcomes is not uniform across different levels of educational attainment. Second, our cohort study framework provides a simple test for progress (or lack thereof) in education and other settings.
В статье представлены результаты описательного исследования соотношения возрастов пользователей онлайновой социальной сети «ВКонтакте», установивших дружеские отношения, на основе случайной выборки (число пар N = 1 433 160). Выявлена существенная возрастная гомофилия, степень которой, однако, зависит от возраста пользователей и возраста присоединения к социальной сети. Результаты показывают, что склонность к гомофилии наиболее характерна для молодых пользователей.