Individual Heterogeneity Research Articles

Public attention and psychological trends towards waste reduction: A large-scale data analysis based on social media

In the digital era, using social media big data to capture the true views and psychological trends shown by the public regarding waste reduction could considerably improve the formulation of targeted waste reduction policies and guide residents to participate in environmental governance from the source side. This study used big data mining technology to trawl 617,771 pieces of waste reduction text from a typical social media site (Sina Microblog). A machine learning algorithm model was used to identify the psychological and cognitive focus of the public and their preferences based on large-scale text data. The temporal and spatial differences in public attention trends, hot topic trends, and sentiment trends were also investigated. The results showed that the public attention level was related to the release of policies by government and that public attention in the southeast coastal areas was higher than that in the northwest inland areas. Moreover, waste reduction had a “working attribute” because the public paid more attention to waste reduction during working hours (i.e., 9:00–12:00 and 15:00–18:00) than during leisure hours. In terms of individual heterogeneity, males were initiators of the topic, whereas females were followers. In particular, participation by young people in waste reduction discussions was higher than for other groups. The topic analysis showed that public attention followed a cooperative construction pattern that had multiple entities, including the individual, community, and government. Overall, the public sentiment score towards waste reduction increased year by year during the study period, with positive sentiment posts accounting for over 70% of the total number of blog posts, and that the vast majority of residents had a positive attitude towards waste reduction. This study expanded current research knowledge by exploring the public attitude response to waste reduction from a social media perspective. The study will help the government to effectively intervene in public behavior tendencies toward waste reduction from the psychological perspective and provided important implications about how the government can enhance its use of social media to effectively guide public opinion and improve policies.

Functional connectome through the human life span.

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers.

Comparing the Efficacy of Fixed-Effects and MAIHDA Models in Predicting Outcomes for Intersectional Social Strata

This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed-effects models when performing intersectional studies. The research questions are as follows: (1) What are typical strata representation rates and outcomes on physics research-based assessments? (2) To what extent do MAIHDA models create more accurate predicted strata outcomes than fixed-effects models? and (3) To what extent do MAIHDA models allow the modeling of smaller strata sample sizes? We simulated 3,000 data sets based on real-world data from 5,955 students on the LASSO platform. We found that MAIHDA created more accurate and precise predictions than fixed-effects models. We also found that using MAIHDA could allow researchers to disaggregate their data further, creating smaller group sample sizes while maintaining more accurate findings than fixed-effects models. We recommend using MAIHDA over fixed-effects models for intersectional investigations.

Genetic Testing for Global Developmental Delay in Early Childhood

Global developmental delay (GDD) is characterized by a complex etiology, diverse phenotypes, and high individual heterogeneity, presenting challenges for early clinical etiologic diagnosis. Cognitive impairment is the core symptom, and despite the pivotal role of genetic factors in GDD development, the understanding of them remains limited. To assess the utility of genetic detection in patients with GDD and to examine the potential molecular pathogenesis of GDD to identify targets for early intervention. This multicenter, prospective cohort study enrolled patients aged 12 to 60 months with GDD from 6 centers in China from July 4, 2020, to August 31, 2023. Participants underwent trio whole exome sequencing (trio-WES) coupled with copy number variation sequencing (CNV-seq). Bioinformatics analysis was used to unravel pathogenesis and identify therapeutic targets. The main outcomes of this study involved enhancing the rate of positive genetic diagnosis for GDD, broadening the scope of genetic testing indications, and investigating the underlying pathogenesis. The classification of children into levels of cognitive impairment was based on the developmental quotient assessed using the Gesell scale. The study encompassed 434 patients with GDD (262 [60%] male; mean [SD] age, 25.75 [13.24] months) with diverse degrees of cognitive impairment: mild (98 [23%]), moderate (141 [32%]), severe (122 [28%]), and profound (73 [17%]). The combined use of trio-WES and CNV-seq resulted in a 61% positive detection rate. Craniofacial abnormalities (odds ratio [OR], 2.27; 95% CI, 1.45-3.56), moderate or severe cognitive impairment (OR, 1.69; 95% CI, 1.05-2.70), and age between 12 and 24 months (OR, 1.57; 95% CI, 1.05-2.35) were associated with a higher risk of carrying genetic variants. Additionally, bioinformatics analysis suggested that genetic variants may induce alterations in brain development and function, which may give rise to cognitive impairment. Moreover, an association was found between the dopaminergic pathway and cognitive impairment. In this cohort study of patients with GDD, combining trio-WES with CNV-seq was a demonstrable, instrumental strategy for advancing the diagnosis of GDD. The close association among genetic variations, brain development, and clinical phenotypes contributed valuable insights into the pathogenesis of GDD. Notably, the dopaminergic pathway emerged as a promising focal point for potential targets in future precision medical interventions for GDD.

Navigating the challenges of extending working life: a study of employment support services for older workers in Hong Kong

PurposeThis research highlights age-specific barriers to employment for older workers and the gap between activation policy design and implementation, focusing on difficulties in extending working life under employment support services.Design/methodology/approachThis research employed a qualitative approach, utilizing semi-structured interviews to gather perspectives on extending the working lives of older adults. Eighteen experienced social workers who worked in employment support services for older workers who aged fifty-five and above participated in the study.FindingsThis study underscores the importance of comprehensively understanding the heterogeneity of individuals' circumstances and emphasizes the impact of intersectionality on the development of extending working life. It incorporates individual and familial difficulties, including considering caregiving responsibilities, health issues and housing challenges before exploring employment-related concerns. The study also highlights external factors influencing older workers' employment prospects, including employer perspectives, government support levels and public recognition of the necessity to extend working life.Research limitations/implicationsThis research sheds light on the impact of welfare-related stigma, which imposes obstacles and inflexibility on older workers seeking employment. It ultimately contributes to the sustainability and dynamism of the Asia–Pacific workforce while also informing regional discussions on social security and welfare services.Originality/valueIt uniquely employs the perspectives of social workers providing employment support services to highlight challenges and policy gaps in extending the working lives of older workers in Hong Kong. It also provides practical insights into strategies and mechanisms for extending working life across diverse social contexts.

Association of Income and Wealth with Self-reported Health Status: Analysis of European Countries during the Financial Crisis

In this paper, we evaluate the association of changes in income and wealth with self-perceived health for the European Union (EU) countries, using a longitudinal sample of individuals. We estimated generalized linear mixed models for three waves of the Eurosystem Household Finance and Consumption Survey (2011, 2015, 2017), adjusting for family and individual heterogeneity and for temporal trends. Results show that variations in income have a positive and significant impact on changes in self-perceived health during the financial crisis, but not after 2015. In conclusion, we find that income, rather than wealth, played an important role in protecting health.

Family-centered decision making: A culturally responsive collaborative approach among Asians living in the United States.

Compared to Western cultures, self-determination needs are expressed and pursued differently in Asian cultures, where interdependence and achieving greater good for the group are prioritized. To accommodate these needs, we propose the use of family-centered decision making (FCDM) to complement the shared decision-making (SDM) practice, fostering collaborative psychiatric care for Asian individuals residing in the United States. This article synthesizes various literature to outline the similarities and differences between SDM and FCDM, discuss implementation steps, challenges associated with implementation, potential solutions, and future research considerations. Our review suggests that FCDM is more responsive to and inclusive of Asian cultural experience, better reflecting these cultures' expression of self-determination. We propose a five-step framework for FCDM implementation in psychiatric rehabilitation for Asian and Asian American individuals, while identifying three further practical considerations: logistical difficulties, intrafamilial differences, and making the decision to use FCDM or not. Given the heterogeneity of Asian individuals in the United States, we urge providers to allow flexibility in practicing FCDM. We outline the important components for providers to help individuals with psychiatric disabilities distinguish between the characteristics of FCDM and SDM, evaluate the potential pros and cons of utilizing FCDM, and then initiate FCDM if appropriate or requested by the individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Age-Specific Changes in the Effects of Social Connectedness and Loneliness on Depressive Symptoms: Evidence From the Korean Longitudinal Study of Ageing Before and During the COVID-19 Pandemic.

This study investigates the longitudinal effects of changes in the associations between two distinct aspects of social connections-i.e., social connectedness and loneliness-on depressive symptoms among community-dwelling Korean adults. This study also examines whether these associations vary across three age groups (45-64, 65-74, and 75 or above). Using data from the Korean Longitudinal Study of Ageing collected between 2014 and 2020 (n=3,642 individuals), fixed effects models were used to examine the age-specific associations between the two distinct aspects of social connections and depressive symptoms (Center for Epidemiologic Studies-Depression Scale) while, accounting for time-invariant individual heterogeneity. Social connectedness is measured by asking the frequency of interactions with friends, relatives, or neighbors. The findings indicate that the impact of loneliness on depressive symptoms outweighs that of social disconnectedness. Notably, this study unveils age-specific patterns concerning the impact of the coronavirus disease-2019 pandemic on depressive symptoms and changes in the association between loneliness and depressive symptoms. Specifically, middle-aged individuals reported higher levels of depressive symptoms and loneliness along with a heightened impact of loneliness on depressive symptoms, despite maintaining stable social connections. Conversely, the oldest adults experienced reductions in both depressive symptoms and loneliness, despite a significant decrease in socializing. These findings shed light on the differential effects of loneliness on depressive symptoms within distinct age groups before and during the pandemic. The implications of these findings are discussed with a focus on informing the development of targeted policy interventions tailored to the specific needs of different age groups.

Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm

Chemotherapy is one of the most effective treatments for cancer, but the efficacy of standard chemotherapy regimens is often limited by toxicities and the individual heterogeneity of cancers. Precise dosing is an important tool to improve efficacy and reduce significant differences in toxicity. However, most of the existing studies on chemotherapy optimization fail to fully consider the toxic side effects, drug resistance, and drug combinations, and thus the chemotherapy regimens obtained may face difficulty in achieving the expected efficacy and also affect the subsequent treatment. Therefore, this paper establishes a tumor growth model for the combination chemotherapy of cell cycle-specific and non-cycle-specific drugs and includes the factors of acquired drug resistance and toxic side effects, proposing an improved multi-objective Squirrel Search Algorithm, the TA-MOSSA, to solve the problem of accurate chemotherapy drug optimization. In this paper, experiments were conducted to analyze the efficacy of chemotherapy dosing regimens obtained by the TA-MOSSA based on the tumor growth model, and the results show that the TA-MOSSA can provide effective chemotherapy regimens for patients who take different treatment approaches.

Individual heterogeneity promotes the evolution of cooperation on temporary networks

Individual heterogeneity promotes the evolution of cooperation on temporary networks

Individual and ecological heterogeneity promote complex communication in social vertebrate group decisions.

To receive the benefits of social living, individuals must make effective group decisions that enable them to achieve behavioural coordination and maintain cohesion. However, heterogeneity in the physical and social environments surrounding group decision-making contexts can increase the level of difficulty social organisms face in making decisions. Groups that live in variable physical environments (high ecological heterogeneity) can experience barriers to information transfer and increased levels of ecological uncertainty. In addition, in groups with large phenotypic variation (high individual heterogeneity), individuals can have substantial conflicts of interest regarding the timing and nature of activities, making it difficult for them to coordinate their behaviours or reach a consensus. In such cases, active communication can increase individuals' abilities to achieve coordination, such as by facilitating the transfer and aggregation of information about the environment or individual behavioural preferences. Here, we review the role of communication in vertebrate group decision-making and its relationship to heterogeneity in the ecological and social environment surrounding group decision-making contexts. We propose that complex communication has evolved to facilitate decision-making in specific socio-ecological contexts, and we provide a framework for studying this topic and testing related hypotheses as part of future research in this area. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Intersectional inequalities in the transition to grandparenthood and cognitive functioning: A longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA).

In aging societies, more people become vulnerable to experiencing cognitive decline. Simultaneously, the role of grandparenthood is central for older adults and their families. Our study investigates inequalities in the level and trajectories of cognitive functioning among older adults, focusing on possible intersectional effects of social determinants and grandparenthood as a life course transition that may contribute to delaying cognitive decline. Using longitudinal data from the Survey of Health, Ageing and Retirement in Europe, we analyzed a sample of 19,953 individuals aged 50-85 without grandchildren at baseline. We applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy to investigate variation in cognitive functioning across 48 intersectional strata, defined by sex/gender, migration, education, and occupation. We allowed the impact of becoming a grandparent on cognitive functioning trajectories to vary across strata by including random slopes. Intersectional strata accounted for 17.43% of the overall variance in cognitive functioning, with most of the stratum-level variation explained by additive effects of the stratum-defining characteristics. Transition to grandparenthood was associated with higher cognitive functioning, showing a stronger effect for women. Stratum-level variation in the grandparenthood effect was modest, especially after accounting for interactions between grandparenthood and the stratum-defining variables. This study highlights the importance of social determinants for understanding heterogeneities in older adults' level of cognitive functioning and its association with the transition to grandparenthood. Cumulative disadvantages negatively affect cognitive functioning, hence adopting an intersectional lens is useful to decompose inequalities and derive tailored interventions to promote equal healthy aging.

Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK

The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life course trajectories. We discuss the conceptualization of intersectional groups in this context: how they are changeable over the life course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data (2009–2021). The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond.

Predictors of male loneliness across life stages: an Australian study of longitudinal data

BackgroundDespite growing recognition of loneliness as a global public health concern, research on its occurrence and precipitants among men across different life stages remains limited and inconclusive. This study aims to address this gap by investigating the prevalence and predictors of loneliness among a large, representative data set of Australian adult men.MethodsThe study used longitudinal data from waves 2–21 of the Household, Income and Labour Dynamics in Australia (HILDA) Survey, including men aged 15–98. Estimating linear fixed effects regressions that account for unobserved time-invariant individual heterogeneity, a single-item measure of loneliness was regressed on a set of selected explanatory variables over different parts of the life course.ResultsIncreased social isolation, romantic partnership dissolution, having a long-term disability, and stronger beliefs that the man, rather than the woman, should be the breadwinner of the household, are associated with greater loneliness. Frequent social connection, having a romantic partner, and high neighbourhood satisfaction are protective against loneliness. The findings also reveal several differences in the predictors of loneliness over the life course. Job security is especially important for younger men, whereas for older men volunteering and less conservative gender role attitudes are important factors that can decrease loneliness.ConclusionsThe results emphasise the need to consider age-specific factors and societal expectations in understanding and addressing loneliness amongst men. Additionally, the findings underscore the importance of raising awareness about the impact of societal norms and expectations on men's mental health. The results offer valuable insights for policymakers, healthcare providers, and researchers to develop effective strategies and support systems to combat loneliness and promote well-being among men.

An Ad Hoc Procedure for Testing Serial Correlation in Spatial Fixed-Effects Panels

We consider testing for error persistence in spatial panels with (potentially correlated) individual heterogeneity. We propose two variants of an ad hoc testing procedure based on first transforming out the individual effects, either by time-demeaning or by taking forward orthogonal deviations, then estimating an encompassing spatio-temporal model. The procedure can also be employed under the random effects assumption, in which case, although suboptimal, it can be computationally cheaper and safer than existing tests. We report Monte Carlo simulations demonstrating satisfactory empirical properties.

Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China.

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

The role of social-evaluative threat for cortisol profiles in response to psychosocial stress: A person-centered approach.

Heterogeneity in individuals' physiological stress responses is central to theories linking stress with vulnerability to disease. Although multiple cortisol profiles have been reported in response to acute psychological stress, most prior work focuses on a single, average pattern and relative deviations from it, such as greater or lesser response peaks or reactivity. The present aims were to identify cortisol stress response trajectory classes using a data-driven approach and test whether social-evaluative threat (SET), a reliable elicitor of cortisol, predicted a greater likelihood of membership in the more reactive profiles. Data were pooled from 13 acute laboratory stressor studies from two geographically distinct U.S. university communities. Participants included 1,258 adults ranging from 18 to 52 years (Mage = 20.5; 62% women; 38% men) with diverse racial/ethnic identities and socioeconomic statuses. Studies included a version of the Trier Social Stress Test and at least three salivary cortisol assessments. SET was tested in three ways: study conditions with evaluators present, perceptions of evaluation, and ratings of shame-related emotions. Latent group-based trajectory modeling was applied to identify cortisol response patterns that best fit the data. Results revealed five unique cortisol response profiles. Consistent with hypotheses, SET conditions, greater perceived evaluation, and greater shame-related emotions predicted membership in the most reactive response trajectories. The findings highlight the high degree of heterogeneity that characterizes cortisol stress response profiles, which has important implications for theories of stress and health and methodological approaches in future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Estimating the Lifetime Costs and Benefits of the Incredible Years Teacher Classroom Management Intervention Using Data From 30 Months Follow-Up of the Supporting Teachers and childRen in Schools Trial

ObjectivesThe Incredible Years Teacher® Classroom Management (IY-TCM) intervention is associated with short-term improvements in mental health difficulties in young people. The aim was to estimate the long-term impact and cost-effectiveness of the IY-TCM intervention compared with no intervention. MethodsAn existing health economic model (LifeSim 1.0) was used to translate short-term changes in the Strength and Difficulties Questionnaire (SDQ), based on the Supporting Teachers and childRen in Schools cluster randomized controlled trial of the IY-TCM intervention in schools, into estimated medium- and long-term effects using multiple longitudinal data sets. LifeSim 1.0 was adapted to incorporate teacher-reported SDQ and account for individual heterogeneity. Cost-effectiveness analyses were conducted using the trial-based intervention cost with subgroup analyses on deprivation, conduct scores and parental depression in the simulated baseline population. ResultsRegression analyses show significant predictor variables for intervention effectiveness, including deprivation and baseline SDQ. LifeSim results indicate small gains in long-term outcomes, and cost-effective analyses estimated that the IY-TCM intervention could be cost-effective, but there was a large amount of uncertainty (net monetary benefit = £10, Estimated CI = −£134, £156). Benefits and certainty of cost-effectiveness were greater for some subgroups, such as those with high conduct scores at baseline (net monetary benefit = £206, Estimated CI = £26, £318). ConclusionsIY-TCM could be cost-effective, but there was a large amount of uncertainty around costs and benefits. Greater benefits for pupils with difficulties at baseline suggest that the intervention may be more cost-effective for schools in more deprived areas with high levels of conduct problems.

Individualized image region detection with total variation

AbstractMedical image data have emerged to be an indispensable component of modern medicine. Different from many general image problems that focus on outcome prediction or image recognition, medical image analysis pays more attention to model interpretation. For instance, given a list of medical images and corresponding labels of patients' health status, it is often of greater importance to identify the image regions that could differentiate the outcome status, compared to simply predicting labels of new images. Moreover, medical image data often demonstrate strong individual heterogeneity. In other words, the image regions associated with an outcome could be different across patients. As a consequence, the traditional one‐model‐fits‐all approach not only omits patient heterogeneity but also possibly leads to misleading or even wrong conclusions. In this article, we introduce a novel statistical framework to detect individualized regions that are associated with a binary outcome, that is, whether a patient has a certain disease or not. Moreover, we propose a total variation‐based penalization for individualized image region detection under a local label‐free scenario. Considering that local labeling is often difficult to obtain for medical image data, our approach may potentially have a wider range of applications in medical research. The effectiveness of our proposed approach is validated by two real histopathology databases: Colon Cancer and Camelyon16.