Longitudinal Dataset Research Articles

Longitudinal Sentiment Analysis with Conversation Textual Data

AbstractThe inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models. This paper presents a two-stage process for analyzing longitudinal textual data, offering a solution to this inherent challenge. The proposed model comprises (1) initial data preprocessing and sentiment extraction, followed by (2) applying a growth curve model to analyze the extracted sentiments directly. The paper also explores four distinct approaches for extracting sentiment scores in the dialogue, providing versatility to the proposed framework. The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset. This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data, providing a robust methodology for extracting and analyzing sentiments longitudinally.

Dropout intention: a valid predictor of actual dropout?

Evidence on the extent to which dropout intention can serve as a valid predictor of dropout decisions remains scarce. This study first presents the results of a systematic literature review of 14 studies examining the relationship between dropout intention and actual dropout in post-secondary education (vocational education and training [VET] or higher education). Second, we examine a longitudinal dataset of prospective chefs surveyed at the beginning (t1), the second half of the first year (t2), and the second year of the dual VET programme (t3). We examine the relationship between self-reported dropout intention and trainees’ actual dropout behaviour. Additionally, we analyse development profiles of trainees’ dropout intention using latent profile analysis. The review results provide strong support for a significant positive relationship between dropout intention and actual dropout (effect sizes vary between studies and contexts/types of dropout). In line, aspiring chefs’ dropout intention is significantly positively correlated with actual dropout (r = 0.201). Three development profiles were identified during VET: (1) stable, low dropout intention, (2) stable, medium dropout intention, and (3) increasing dropout intention. We conclude that dropout intention is positively related to actual dropout. As the extent to which dropout intention actually leads to dropout behaviour in VET also depends on external factors (e.g., labour market situation, alternative options), the predictive power of dropout intention may be limited. This is suggested by the weak correlation between dropout intention and actual dropout and the profile of trainees with increasing dropout intention who nevertheless persisted until year 2 of VET.

Continuity in child mental health from preschool years to adolescence in parental migration contexts: Evidence from a longitudinal study in Thailand

Our analysis employed the life-course approach to examine whether and how mental health during the preschool stage could predict mental health during adolescence in the Thai context, where migration of parents is common. We used the longitudinal data set of the 2008 and 2021 Child Health and Migrant Parents (CHAMPSEA)-Thailand. The baseline survey of CHAMSEA-Thailand gathered data from 1030 households that met the eligibility criteria, each having a child within one of the two specified age ranges (aged 3–5 or 9–11). Our analysis used the baseline data of children aged 3 to 5 years old who were 16 to 18 years old in the subsequent survey (N = 404). In both surveys, the mental health of children was measured using the SDQ (total difficulties scores), a global standard tool for assessing children's mental health. Parental migration measured whether the parent(s) were international migrants when the child was 3 to 5 years old at the baseline. Results showed a significant, positive impact of the SDQ total difficulties scores of children aged 3 to 5 on their SDQ total difficulties scores when they grew up to 16 to 18 years old. Findings also revealed that experiencing parental international migration during the early life stage led to adverse effects on individuals' mental health when they became adolescents.

Longitudinal within-person variability around personality trajectories.

Decades of research have identified average patterns of normative personality development across the lifespan. However, it is unclear how well these correspond to trajectories of individual development. Past work beyond general personality development might suggest these average patterns are oversimplifications, necessitating novel examinations of how personality develops and consideration of new individual difference metrics. This study uses five longitudinal data sets from Germany, Australia, the Netherlands, and the United States (N = 128,345; Mage = 45.42; 53% female) to examine personality development using mixed-effects location scale models. These models quantify individual differences in within-person residual variability, or sigma, around trajectories-thereby testing if models that assume sigma is homogeneous, unsystematic noise are appropriate. We investigate if there are individual differences in longitudinal within-person variability for Big Five trajectories, if there are variables associated with this heterogeneity, and if person-level sigma values can uniquely predict an outcome. Results indicated that, across all models, there was meaningful heterogeneity in sigma-the magnitude of which was comparable to and often even greater than that of intercepts and slopes. Individual differences in sigma were further associated with covariates central to personality development and had robust predictive utility for health status, an outcome with long-established personality associations. Collectively, these findings underscore the presence, degree, validity, and potential utility of heterogeneity in longitudinal within-person variability and indicate the typical linear model does not adequately depict individual development. We suggest it should become the default to consider this individual difference metric in personality development research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dimensionality reduction analysis of the renewable energy sector in Azerbaijan: nonparametric analyses of large datasets

Although the number of econometric analyses related to the renewable energy sector in Azerbaijan is increasing, studies on nonparametric dimensionality reduction are rather sparse. Principal component analysis (PCA) and multiple correspondence analysis (MCA) were chosen to fill this apparent research gap. As a result, a large dataset including the renewable energy sector and selected key macroeconomic indicators was evaluated. The PCA procedure yielded four distinct principle components reflecting the main macroeconomic variables, renewable energy production, industry-energy relations and natural resource revenues. The PCA method offers the possibility to examine the precise correlations and the underlying patterns between the displayed clusters of variables. Meanwhile, the MCA-based cross-country assessment of Azerbaijan’s wind, solar and hydropower has struck somewhat pessimistic notes, as the country lags behind neighbouring and other post-Soviet countries (e.g. Estonia, Iran, Latvia, Russia) in developing its green energy sector. These findings are of great interest to policymakers, businesses and academics who wish to gain deep insight into the Azerbaijani economy in terms of renewable energy production. The practical value of the present work also lies in the fact that it analyses a multidimensional and relatively longitudinal dataset (1990–2022), which is an example of a methodological application of two nonparametric approaches.

Does digital-industrial technology integration reduce corporate carbon emissions?

The integration of digital technology and industrial technology emerges as a pivotal avenue for enterprises to attain Sustainable Development Goals (SDGs), promoting the transition towards a sustainable and green trajectory of development. The panel data utilized in this paper encompasses financial, operational, and carbon emission metrics of publicly listed companies in China from 2008 to 2019. This longitudinal dataset enables us to perform robust statistical analyses to evaluate the impacts of Digital-Industrial technology integration on carbon emission intensity over time. The results indicate that: (i) Digital-Industrial technology integration significantly reduces the carbon emission intensity of enterprises, the conclusion remains robust when subjected to endogeneity and sensitivity analyses. (ii) Digital-Industrial technology integration mainly reduces the marginal cost of production and enhances the accumulation of knowledge to reduce the carbon emission intensity of enterprises. (iii) Digital-Industrial Technology Integration manifests more pronounced effects in curbing carbon emissions in regions characterized by robust property rights protection and well-developed digital infrastructure. (iv) Further research finds that Digital-Industrial technology integration increases the green technology innovation and ESG (Environmental, Social, and Governance) performance of enterprises. The results of this study provide empirical evidence for the carbon emission reduction function of the Digital-Industrial technology integration, and also verify the positive externality impact of the Digital-Industrial technology integration on the enterprise green innovation and SDGs.

Machine learning predicts upper secondary education dropout as early as the end of primary school

Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students’ academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators’ processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.

Identifying stationary microbial interaction networks based on irregularly spaced longitudinal 16S rRNA gene sequencing data

IntroductionThe microbial interactions within the human microbiome are complex, and few methods are available to identify these interactions within a longitudinal microbial abundance framework. Existing methods typically impose restrictive constraints, such as requiring long sequences and equal spacing, on the data format which in many cases are violated.MethodsTo identify microbial interaction networks (MINs) with general longitudinal data settings, we propose a stationary Gaussian graphical model (SGGM) based on 16S rRNA gene sequencing data. In the SGGM, data can be arbitrarily spaced, and there are no restrictions on the length of data sequences from a single subject. Based on the SGGM, EM -type algorithms are devised to compute the L1-penalized maximum likelihood estimate of MINs. The algorithms employ the classical graphical LASSO algorithm as the building block and can be implemented efficiently. ResultsExtensive simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms if the correlations among the longitudinal data are reasonably high. When the assumptions in the SGGM areviolated, e.g., zero inflation or data from heterogeneous microbial communities, the proposed algorithms still demonstrate robustness and perform better than the other existing algorithms. The algorithms are applied to a 16S rRNA gene sequencing data set from patients with cystic fibrosis. The results demonstrate strong evidence of an association between the MINs and the phylogenetic tree, indicating that the genetically related taxa tend to have more/stronger interactions. These results strengthen the existing findings in literature. DiscussionThe proposed algorithms can potentially be used to explore the network structure in genome, metabolome etc. as well.

Paternal and induced gut microbiota seeding complement mother-to-infant transmission

Microbial colonization of the neonatal gut involves maternal seeding, which is partially disrupted in cesarean-born infants and after intrapartum antibiotic prophylaxis. However, other physically close individuals could complement such seeding. To assess the role of both parents and of induced seeding, we analyzed two longitudinal metagenomic datasets (health and early life microbiota [HELMi]: N= 74 infants, 398 samples, and SECFLOR: N= 7 infants, 35 samples) with cesarean-born infants who received maternal fecal microbiota transplantation (FMT). We found that the father constitutes a stable source of strains for the infant independently of the delivery mode, with the cumulative contribution becoming comparable to that of the mother after 1 year. Maternal FMT increased mother-infant strain sharing in cesarean-born infants, raising the average bacterial empirical growth rate while reducing pathogen colonization. Overall, our results indicate that maternal seeding is partly complemented by that of the father and support the potential of induced seeding to restore potential deviations in this process.

Can primary health care mitigate the effects of economic crises on child health in Latin America? An integrated multicountry evaluation and forecasting analysis

Latin American and Caribbean countries are dealing with the combined challenges of pandemic-induced socicoeconomic stress and increasing public debt, potentially leading to reductions in welfare and health-care services, including primary care. We aimed to evaluate the impact of primary health-care coverage on child mortality in Latin America over the past two decades and to forecast the potential effects of primary health-care mitigation during the current economic crisis. This multicountry study integrated retrospective impact evaluations in Brazil, Colombia, Ecuador, and Mexico from 2000 to 2019 with forecasting models covering up to 2030. We estimated the impact of coverage of primary health care on mortality rates in children younger than 5 years (hereafter referred to as under-5 mortality) across different age groups and causes of death, adjusting for all relevant demographic, socioeconomic, and health-care factors, with fixed-effects multivariable negative binomial models in 5647 municipalities with an adequate quality of vital statistics. We also performed several sensitivity and triangulation analyses. We integrated previous longitudinal datasets with validated dynamic microsimulation models and projected trends in under-5 mortality rates under alternative policy response scenarios until 2030. High primary health-care coverage was associated with substantial reductions in post-neonatal mortality rates (rate ratio [RR] 0·72, 95% CI 0·71-0·74), toddler (ie, aged between 1 year and <5 years) mortality rates (0·75, 0·73-0·76), and under-5 mortality rates (0·81, 0·80-0·82), preventing 305 890 (95% CI 251 826-360 517) deaths of children younger than 5 years over the period 2000-19. High primary health-care coverage was also associated with lower under-5 mortality rates from nutritional deficiencies (RR 0·55, 95% CI 0·52-0·58), anaemia (0·64, 0·57-0·72), vaccine-preventable and vaccine-sensitive conditions (0·70, 0·68-0·72), and infectious gastroenteritis (0·78, 0·73-0·84). Considering a scenario of moderate economic crisis, a mitigation response strategy implemented in the period 2020-30 that increases primary health-care coverage could reduce the under-5 mortality rate by up to 23% (RR 0·77, 95% CI 0·72-0·84) when compared with a fiscal austerity response, and this strategy would avoid 142 285 (95% CI 120 217-164 378) child deaths by 2030 in Brazil, Colombia, Ecuador, and Mexico. The improvement in primary health-care coverage in Brazil, Colombia, Ecuador, and Mexico over the past two decades has substantially contributed to improving child survival. Expansion of primary health-care coverage should be considered an effective strategy to mitigate the health effects of the current economic crisis and to achieve Sustainable Development Goals related to child health. UK Medical Research Council. For the Spanish and Portuguese translations of the abstract see Supplementary Materials section.

Understanding adjustment profiles among Mexican-origin adolescents over time: A focus on cultural risk and resilience factors.

This study used a three-wave longitudinal dataset to: identify adjustment profiles of U.S. Mexican-origin adolescents based on their physical, academic, and psychosocial health adjustment; track adjustment profile changes throughout adolescence; and examine the associations between cultural stressors, family obligation, and adjustment profile membership over time. Participants were 604 Mexican-origin adolescents (54% female, Mage = 12.41, SD = 0.97) in Texas (Wave 1: 2012-2015; Wave 2: 2013-2016; Wave 3: 2017-2020). Three concurrent profiles (Well-adjusted, Moderate, and Poorly-adjusted) emerged at each wave, whereas three transition profiles (Improved, Stable well-adjusted, and Overall poorly-adjusted) were identified across three waves. The results suggest that cultural stressors pose risks for Mexican-origin adolescents' adjustment, and family obligation values play a protective role in these associations.

The impact of social isolation on functional disability in older people: A multi-cohort study

ObjectivesWe assessed the relationship between social isolation and functional disability in older people. DesignComparison of longitudinal cohort studies. Setting and participantsHarmonised longitudinal datasets from the United States, England, European countries, Japan, Korea, China and Hong Kong. MethodsSocial isolation was operationalised as a composite score with five domains, such as marital status, living alone, and social contact with others. Functional disability was defined as whether the cohort participant had any difficulty in activities of daily living (ADL). In each dataset, we used robust Poisson regression models to obtain the relative risks (RRs) and the corresponding 95 % confidence intervals (CI). We combined the RRs to synthesize a pooled estimate using meta-analysis with random-effects models. ResultsOverall, the social isolation composite score was not associated with ADL disability (pooled RR = 1.05, 95 % CI [0.97–1.14], n = 40,119). Subgroup analysis suggested social isolation composite score was associated with ADL disability in Asian regions (pooled RR = 1.09, 95 % CI [1.02, 1.16], but not in Western regions (pooled RR = 1.01, 95 % CI [0.96, 1.07]). The relationships between different domains of social isolation and ADL disability were heterogeneous, except that no participation in any social clubs or religious groups was consistently associated with ADL disability (pooled RR = 1.12, 95 % CI [1.04, 1.21]). ConclusionTargeting social isolation may prevent decline in functional abilities in older adults, providing an avenue to active and healthy ageing. Nonetheless, interventions tackling social isolation should tailor to the unique cultural and social underpinnings. A limitation of the study is that reverse causality could not be ruled out definitively.

Loss of white matter integrity mediates the association between cortical cerebral microinfarcts and cognitive dysfunction: A longitudinal study

Cortical cerebral microinfarcts (CMIs) are associated with loss of white matter (WM) integrity and cognitive impairment in cross-sectional studies, while further investigation using longitudinal datasets is required. This study aims to establish the association between cortical CMIs and WM integrity assessed by diffusion-tensor imaging (DTI) measures and to investigate whether DTI measures mediate the relationship between cortical CMIs and cognitive decline. Cortical CMIs were graded on 3T MRI. DTI measures were derived from histogram analysis of mean diffusivity (MD) and fractional anisotropy (FA). Cognitive function was assessed using a neuropsychological test battery. Linear mixed-effect models were employed to examine associations of cortical CMIs with longitudinal changes in DTI measures and cognitive function. Final analysis included 231 patients (71.14 ± 7.60 years). Presence of cortical CMIs at baseline was associated with longitudinal changes in MD median and peak height and FA median and peak height, as well as global cognition (β = −0.50, 95%CI: −0.91, −0.09) and executive function (β = −0.77, 95%CI: −1.25, −0.28). MD median mediated the cross-sectional association between cortical CMIs and global cognition. Further studies are required to investigate whether cortical CMIs and loss of WM integrity are causally related or if they are parallel mechanisms that contribute to cognitive decline.

A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool’s generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.

Students With Growth Mindset Learn More in School: Evidence From California’s CORE School Districts

Previous research provides evidence that developing a growth mindset—believing that one’s capabilities can improve—promotes academic achievement. Although this phenomenon has undergone prior study in a representative sample of ninth graders in the United States, it has not been studied in representative samples of other grade levels or with standardized assessment measures of achievement rather than more subjective grades. Using a rich longitudinal data set of more than 200,000 students in Grades 4 through 7 in California who we followed for a year until they were in Grades 5 through 8, this article describes growth mindset gaps across student groups and confirms, at a large scale, the predictive power of growth mindset for achievement gains. We estimate that a student with growth mindset who is in the same school and grade level and has the same background and achievement characteristics as a student with a fixed mindset learns 0.066 SD more annually in English language arts, approximately 18% of the average annual growth or 33 days of learning if we assume learning growth as uniform across the 180 days of the academic year. For mathematics, the corresponding estimates are 0.039 SD, approximately 17% of average annual growth or 31 days of learning.

Short- and long-term effects of incentives on prosocial behavior: The case of ride vouchers to a blood collection agency

This study addresses the challenge of low blood donation rates in developing countries by examining the effectiveness of a barrier-removal incentive—a one-day transportation voucher—to promote blood donation. Utilizing a longitudinal dataset of 23,750 donors from a Brazilian blood collection agency (BCA) collected between March 2018 and May 2020, we examine the short and long-term effects of this campaign on donation rates. Our results show that the incentive had a large positive influence on both donation attempts and successful donations on the day of the campaign. However, the short-term success of the intervention had an unintended consequence: the significant increase in prospective donors’ waiting time at the BCA during the intervention day, which may help explain the negative impact on return rates in the 24-month follow-up. Despite these opposing outcomes, the net effect of the one-day blood donation incentive was still positive, offering valuable insights for BCAs aiming to enhance donor recruitment and retention strategies and emphasizing the need to balance immediate benefits with potential long-term impacts.

Clustering Pseudo Time Series: Exploring Trajectories in the Ageing Process.

Investigating the natural ageing process typically involves the use of extensive longitudinal datasets that can capture changes associated with the progression of ageing. However, they are often resource-intensive and time-consuming to conduct. Cross-sectional data, on the other hand, provides a snapshot of a population at many different ages and can capture many disease processes but do not incorporate the time dimension. Pseudo time series can be reconstructed from cross sectional data, with the aim to explore dynamic processes (such as the ageing process). In this paper we focus on employing pseudo time series analysis on cross-sectional population data that we constrain using age information to create realistic trajectories of people with different degrees of cardiovascular disease. We then use clustering methods to construct and label trajectory-based phenotypes, aiming to enhance our understanding of ageing and disease progression.

No change in substance use disorders or overdose after implementation of state Earned Income Tax Credit (EITC)

BackgroundInadequate income is associated with higher likelihood of experiencing a substance use disorder (SUD). This study tests whether the earned income tax credit (EITC), which issues supplemental income for workers with children in the U.S., is associated with lower rates of SUD and fatal overdose. MethodsWe examined the effects of state-level refundable EITC presence and generosity (i.e., state EITC rate as a % of federal rate) on SUD-related outcomes (SUD prevalence and intentional and unintentional fatal overdose) using a difference-in-difference methodology, with both two-way fixed-effects models and event study plots. Several sensitivity analyses were conducted to assess the robustness of findings. Five data sources were used to create a combined state-level longitudinal dataset. ResultsWe did not find significant effects of refundable EITC presence or generosity on unintentional or intentional fatal overdose or SUD prevalence in two-way models. Event study models detected a very slight upward shift in SUD prevalence following refundable EITC implementation (not seen in sensitivity analyses) and no significant effects of EITC implementation on any of the fatal overdose outcomes. ConclusionsEvidence regarding income support programs is being highly sought by policy makers as income support programs have become increasingly popular policy levers since the start of the COVID-19 pandemic. Our study indicates EITC policies likely have no impact on SUD or overdose, however, other income support programs without family restrictions are important to investigate further.

A Longitudinal Corpus Analysis of the Quantity, Syntactic, and Lexical Characteristics of Maternal and Paternal Parentese

ABSTRACT Research on parentese, the acoustically exaggerated, slower, and higher-pitched speech directed toward infants, has mostly focused on maternal contributions, although it has long been known that fathers also produce parentese. Given recent societal changes in family dynamics, it is necessary to revise these mother-centered models of language input. Using a longitudinal dataset of daylong recordings collected with the same group of USA-born English-speaking infants (N = 21) at 6, 10, 14, 18, and 24 months, we investigate the similarities and differences between maternal and paternal parentese quantity, as well syntactic and lexical characteristics of parentese. We find that mothers are producing quantitatively more parentese than fathers at all ages. We find that the syntactic aspects do not differ substantially between mothers and fathers, however lexical differences do emerge. Specifically, we see that mothers may use more question and mental state words than fathers. Furthermore, use of these types of words increase as the child develops. This research highlight the continued need to further study fathers in order to have a full understanding of child language development.

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes. We hypothesized that applying MOFA to multiomic data obtained from blood might uncover hidden sources of variance and provide pathophysiological insights linked to clinical needs. Here we compile a longitudinal multiomic dataset of the systemic immune landscape in both ACS and CCS (n = 62 patients in total, n = 15 women and n = 47 men) and validate this in an external cohort (n = 55 patients in total, n = 11 women and n = 44 men). MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates and immune-communication pathways that explain a large proportion of inter-patient variance. We also identify specific factors that reflect disease state or associate with treatment outcome in ACS as measured using left ventricular ejection fraction. Hence, this study provides proof-of-concept evidence for the ability of MOFA to uncover multicellular immune programs in cardiovascular disease, opening new directions for mechanistic, biomarker and therapeutic studies.