• Individual carbon footprints from online shopping are estimated using survey data. • The top 20% of emitters generate between 65% and 70% of emissions. • Top emitters shop more, travel farther, use cars more, and have higher emissions. • Environmental footprints rise as travel linked to online shopping increases. • Density’s effect on distance and emissions varies with delivery conditions. What is the environmental footprint of shopping-related mobility and how are emissions distributed among individuals? We approach this question by estimating individual pollutant footprints, using a survey of travel practices linked to goods purchases by French households, which we combine with emission factors for NOx, PM 2.5 and CO 2 . The results show that the top 20% of emitters account for between 65% and 74% of emissions from representative trips. At the individual level, these top emitters have higher purchase frequencies, travel longer distances, are highly dependent on cars, and have high emission intensities when traveling by car. The characteristics most strongly associated with high emission levels are living in a high-density area, being female, having a low income, and preferring home delivery. In addition, increased frequency of trips to relay points is correlated with higher emissions of local pollutants (NOx, PM2.5). In summary, individual environmental footprints associated with online purchases increase with the intensification of shopping trips, which is itself driven by a higher frequency of online purchases and a greater propensity to use the car.

With global population aging, the number of older adults in Chinese nursing homes is rising rapidly, and depression is the most prevalent mental health problem in this population. Most previous studies assessed depression via total scale scores, ignoring individual heterogeneity of depressive symptoms. This study aimed to identify distinct depressive symptom profiles and their associated factors in this population. Data were derived from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), with 353 valid nursing home older adults included. Depressive symptoms, anxiety and functional status were assessed using the CESD-10, GAD-7 and IADL scales. Latent profile analysis (LPA), univariate tests and multinomial logistic regression were performed, with supplementary effect size and sensitivity analyses to verify result robustness. Three distinct depressive symptom profiles were identified: low level (39%, n = 135), medium level (52%, n = 187) and high level (9%, n = 31). Town residence and anxiety were risk factors for moderate depression, while good self-rated health, regular exercise and social activity participation were protective factors. Good self-rated health protected against severe depression, while occasional television/radio viewing and anxiety were risk factors. Anxiety was the only independent correlate of high-level versus medium-level depression (OR = 1.322, p < 0.001). Supplementary analyses confirmed the robustness of core findings. The CESD-10, as a screening tool, has limited diagnostic efficacy for clinical depression, and the cross-sectional design cannot confirm causal relationships. Depressive symptoms in Chinese nursing home older adults show significant heterogeneity with three distinct latent profiles. Early screening and targeted stratified interventions should be implemented for this population to improve quality of life and promote healthy aging.

Intersectionality offers a critical framework for understanding how multiple axes of social identity shape health outcomes. Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) has emerged as a popular method for operationalizing intersectionality in social epidemiology. By structuring individuals into intersecting social categories (strata), and modeling both within- and between-stratum variation, MAIHDA offers a way to quantify stratum-level heterogeneity while mitigating issues of sparse data through partial pooling. In this paper, we argue that the statistical structure of MAIHDA, and its subsequent interpretation, carries epistemic commitments that are rarely made explicit. First, shrinkage in MAIHDA induces an implicit reweighting of intersectional strata toward a population of equally sized groups. As a result, estimated between-stratum variation reflects a hypothetical target population rather than the empirical population distribution, raising questions about the interpretation of stratum-level effects. Second, we argue that the variance partition coefficient and proportional change in variance are the most informative metrics specifically with regards to intersectionality, and careful interpretation of these is required. In addition, observed heterogeneity is underdetermined by any single social theory, meaning that MAIHDA findings may be consistent with multiple explanatory frameworks beyond identity-based mechanisms, such as intersectionality. We conclude that MAIHDA is best understood as a descriptive tool that identifies stratified heterogeneity. This may provide empirical guidance for when intersectional explanations are relevant, but should remain open to alternative theoretical interpretations. This perspective encourages careful epistemic reflection on the assumptions and inferences made when applying MAIHDA to intersectionality-motivated research questions.

Body image in the age of GLP-1s: Emerging questions for research and practice.

Intersectional inequalities in healthcare utilisation in informal settlements in Freetown, Sierra Leone: a multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

Monitoring wildlife health is essential for conservation and management, wildlife and livestock welfare, and public health in a One Health framework. Yet, wildlife health monitoring often requires long-term fieldwork and intensive sampling, which can be costly or logistically challenging, especially for remote, rare, or elusive populations. To address these challenges, we evaluated the feasibility and utility of integrating camera traps with a marked subset of Nubian ibex (Capra nubiana) to monitor five diseases that produce visible signs. Over six consecutive years, we deployed 51 camera traps and monitored 43 uniquely identifiable individuals (25 males and 18 females). We estimated apparent prevalence annually and modeled temporal trends with generalized additive models, which allowed flexible nonlinear relationships between disease prevalence and time. To assess recurrence dynamics and mortality risk, we fitted joint frailty models that simultaneously estimated rates of recurrent disease events and survival while accounting for individual heterogeneity. Apparent prevalence ranged from 2% to 38% depending on disease and year. For the most prevalent disease, caseous lymphadenitis (CLA), prevalence was higher in males (β=2.08, p=0.041) and did not vary across years. Despite small sample size (n=9), joint frailty models yielded epidemiologically meaningful inference. Recurrence of CLA was positively associated with mortality (association parameter =2.32, p=0.012), and male sex was a significant prognostic factor for terminal events (hazard ratio=0.07, p=0.019). Our proof of concept demonstrates that combining camera traps with marked individuals can provide a practical auxiliary or stand-alone tool for wildlife health monitoring. This approach is particularly suited to rare, elusive, or logistically inaccessible species and will gain further strength once its diagnostic specificity and sensitivity are validated.

Social-emotional development is an important part of early childhood development and appears to have a socioeconomic gradient. Going beyond conventional approaches, this study aimed to examine intersectional inequalities in social-emotional problems in three-year-old children in relation to their parents' income, education and place of birth and to consider the implications for public health. A cross-sectional design was used in this population-based study in Västerbotten County of Sweden with an effective sample of 8,823 children. Social-emotional problems were assessed using the parent/caregiver-report Ages and Stages Questionnaires: Social-Emotional (ASQ: SE) 36-month interval in Child Health Services over the years of 2014-2018 and linked to parents' sociodemographic characteristics obtained from national population registers. An analysis of individual heterogeneity and discriminatory accuracy (AIHDA) approach was combined with additive binomial regression, estimating risk differences for social-emotional problems across 27 intersectional categories. In the intersectional categories where multiple dimensions of social disadvantage overlapped, average risk differences generally increased. For instance, when comparing with the most advantaged category a risk difference as high as 25.4% (95% CI 13.7 to 37.0%) was found for the children whose parents' income was in the lowest tercile, only one parent had a higher educational level and whose parents were both born outside of Sweden. Discriminatory accuracy was estimated as moderate for the three included regression models, although it improved slightly for the model including dimensions of social disadvantage. The addition of intersectional categories provided no further significant improvement. The intersectional approach used in this study improves our understanding of complex social inequalities in social-emotional problems in preschool children in northern Sweden. Consistent with the concept of proportionate universalism, the results of this study indicate that universal public health policies are needed when addressing this issue in addition to policies targeting disadvantaged groups. Research that considers individual heterogeneity and discriminatory accuracy has the potential to advance our knowledge of health inequities and increase the effectiveness of public health policy.

Late-life depression is characterized by cumulative intersectional inequalities resulting from multiple social determinants, which also shape the retirement transition. This study quantified longitudinal inequalities in depressive symptoms across intersectional strata constituted by gender, educational background, hukou status, and childhood health among older adults in China. It also examined whether retirement's impact on depression varied across these strata. Data were drawn from the China Health and Retirement Longitudinal Study (2011-2020). The sample included 13,605 participants aged ≥45 and employed at baseline, yielding 56,239 person-years. Using a longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy framework, this research assessed intersectional inequalities in depressive symptoms (measured by the CES-D-10 scale) and the immediate and long-term impacts of retirement. The latter was modelled as trajectories in time relative to retirement (before vs. after), distinguishing preretirement anticipation from postretirement adjustment. Reflecting predominantly additive effects of the social determinants, intersectional strata explained 17.73% and 6.10% of baseline and trajectory variances, respectively. Small but meaningful multiplicative components representing stratum-level heterogeneity beyond the additive effects emerged, revealing distinctively exacerbating or protective patterns in baseline depression for specific strata. Retirement predicted a small but statistically significant immediate increase in depression (4.5% of a standard deviation), with no evidence of long-term worsening in the full sample. The impact was largely uniform across strata, although immediate shocks were weaker among men and urban registrants. Intersectional inequalities strongly pattern later-life depression in China. Enhancing retirement-related support requires recognizing both persistent additive disparities and residual stratum-specific vulnerabilities.

The neurobiological mechanisms of major depressive disorder with suicidal ideation (MDDSI) remain unclear, partly due to individual heterogeneity among patients with MDDSI. We developed a multi-level framework to extract individual-shared (IShN) and individual-specific brain networks (ISpN) using personalized principal component analysis (perPCA), construct structure-function coupling (SFC) network via graph embedding, and map network alterations to transcriptomic and neurotransmitter distributions. Structural, functional, and SFC networks were examined in 528 participants and replicated in 123 participants of an independent cohort. After removing individual heterogeneity, patients with MDDSI showed convergent disruptions within the default-mode network and action mode network across structural, functional, and SFC networks. These alterations corresponded to 5-HT2a and to the expression of genes involved in neurotransmitter transport, synaptic signalling, and neurodevelopmental pathways. By disentangling subject-specific components, the ISpN captured symptom-relevant variations that were obscured in the original brain networks, enabling more accurate diagnostic classification. Our findings identify reproducible, cross-modal network abnormalities and their molecular correlates underlying MDDSI, demonstrating the importance of disentangling individual heterogeneity for advancing the neurobiological understanding of MDDSI.

Studying the demographic processes that shape how populations respond to environmental changes has long provided insights for conservation biology. Recent theoretical advances have deepened our understanding of these processes, yet their application in conservation remains unclear. We conducted a literature search to examine how six key demographic concepts — life-history trade-offs, the fast–slow continuum, temporal covariation among demographic parameters, demographic buffering and lability, individual heterogeneity and transient dynamics — have been used in conservation, and discussed their potential benefits and limitations. Their applications fall into three main categories: improving estimates of demographic parameters, population dynamics, and extinction risk; predicting the magnitude and duration of population responses to disturbances or conservation actions; and identifying the demographic processes most relevant for guiding conservation decisions. Individual heterogeneity and the fast–slow continuum were widely used, likely due to their low data and analytical requirements, allowing broad predictions of species' vulnerability and informing conservation decisions. Trade-offs explained how populations adapt to anthropogenic disturbances, invasions or conservation actions. Conversely, temporal covariation and buffering–lability were rarely applied, despite their value for improving projections and assessing populations' capacity to cope with environmental variability. Limited use reflects data and modelling needs, and, for temporal covariation, lack of direct conservation guidance. Transient dynamics, highlighting short-term responses and demographic resilience, are relevant because they match the timescale of many conservation projects. We argue that even modest monitoring efforts can capture essential demographic processes, and that their systematic integration, directly or via inference from related systems, could strengthen long-term conservation outcomes. • Literature review of six demographic concepts to assess their use in conservation • Commonly applied or discussed, though use varies among demographic concepts • Variation likely due to data and modelling needs, concept maturity and operability • Improve extinction-risk estimates, predict responses to stressors and guide actions • Moderate monitoring allows estimating many processes that support conservation

Temporal mood dynamics and individual heterogeneity: a computational framework for human-environment affective dynamics

Causal effects of typhoons on mobility perturbations: environment and individual heterogeneity

While previous research has focused on individual risk factors, few studies have utilized an intersectionality-informed framework to examine how multiple social factors collectively impact depression risk, particularly in the context of childhood adversities. This study investigates the association between adverse childhood experiences (ACEs) and depression while considering the intersecting roles of sex, socioeconomic status (SES), and ethnicity in identifying high-risk population subgroups. Data were drawn from the Canadian Longitudinal Study on Aging (CLSA), a nationally representative prospective cohort comprising over 50,000 adults aged 45 and older. We included 20,559 participants from the Comprehensive cohort who completed both baseline and first follow-up assessments. Depression was measured using a validated scale (CES-D-10). Key variables included ACE exposure, sex, SES, ethnicity, and other sociodemographic covariates. Latent class analysis was conducted to explore latent ACEs groups. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was employed to investigate whether ACE patterns and intersectional social groups predicted differential risks of depression. Three distinct patterns of ACEs were identified. The high ACE group was associated with an elevated risk of depression, especially among females, individuals with low SES, and non-White groups. This risk was compounded among those with multiple marginalized identities, such as females with low SES and exposure to high ACE, highlighting the cumulative nature of depression vulnerability. In contrast, male sex and high SES were protective, particularly in the absence of ACE exposure. Integrating ACEs with an intersectional framework reveals the complex etiology of depression and underscores the need for targeted, equity-oriented prevention strategies considering cumulative disadvantage.

The combined and interactive effects of multiple lifestyle behaviours on obesity risk are not well understood. We used Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to examine how adherence to public health recommendations for five lifestyle behaviours affects BMI and obesity risk. The sample included 139,540 men and 125,455 women from the UK Biobank. We categorized fruit and vegetable intake, physical activity, sleep duration and alcohol intake as binary variables (meeting vs. not meeting guidelines), and smoking status into three categories (previous, current, never). These categories were combined to form 48 unique strata, representing all possible combinations of the five behaviours. Linear and binary logistic MAIHDA models were used, with individuals nested within strata, and BMI and obesity status (obesity vs. normal weight) as outcomes. Three models were employed: Model 1 (null), Model 2 (with fixed effects for lifestyle behaviours), and Model 3 (with confounders and fixed effects). Variance Partition Coefficient (VPC), Proportional Change in Variance (PCV), and predicted BMI and obesity risk were estimated. For both sexes, strata with the lowest obesity risk were associated with meeting most recommendations, while strata with the highest risk were linked to meeting few. Logistic Model 1 VPCs revealed 7% of variance in obesity risk among males and 5% among females was explained by between-strata differences. In Model 3, VPCs attenuated to 0.5% among males and 0.1% among females, suggesting differences in obesity risk were largely additive effects. PCVs from Model 3 also indicated primarily additive rather than interactive effects. Results were similar for BMI in the linear models. Using a novel statistical approach, this study shows that additive effects of multiple lifestyle behaviours predominantly explain differences in BMI and obesity risk. Meeting more public health lifestyle recommendations is important in mitigating obesity risk.

Epizootics can profoundly impact population dynamics, affecting both survival and reproduction over relatively short periods. However, the longer-term demographic patterns that may follow an outbreak, as well as the underlying mechanisms, remain poorly documented. To investigate these dynamics, we compared the demographic parameters of females in an Alpine ibex population before (10 years), during (2 years) and after (two subsequent 7-year periods) a severe pneumonia outbreak. Using an integrated population model combining female capture-mark-recapture and census data, and accounting for individual heterogeneity, we estimated the age-specific breeding probability, survival and female population growth rate. We observed an increase in breeding probability after the epizootic, rising from pre-epizootic values of 0.63 to over 0.79 in high-quality females and from 0.03 to over 0.24 in low-quality females that had not bred the previous year. The age of primiparity decreased for most females, and while high-quality females that had previously reproduced were less likely to reproduce again compared to non-reproductive females before the epizootic, the opposite pattern was observed after the epizootic. These findings are consistent with the expected reduced impact of density-dependent processes following the 62% decline in female population size during the epizootic, which affected female survival across all age categories, except for prime-age non-reproductive individuals. This greater reproductive investment contrasted with a decline in survival, which was 6.7% lower for two-thirds of the adult female population (and 2% lower for the entire adult female population) during all post-epizootic periods. The persistent presence of the pathogen in the population, potentially indicating chronic or latent infection, combined with recent environmental changes (e.g. warmer conditions), may have prevented adult survival from returning to pre-epizootic levels. This sustained shortfall in adult survival compared to pre-epizootic levels resulted in a weak post-epizootic recovery of the population (+2.4% year-1) driven only by high-quality females. These findings illustrate that long-term monitoring populations during adverse environmental conditions, such as disease outbreaks, can provide insights into how long-lived iteroparous females navigate survival-reproduction trade-offs in response to pathogen exposure and access to resources. This can help anticipate demographic responses to emerging infectious diseases and climate change.

Inequities in systolic blood pressure (SBP), a widely used biomarker, have been shown to be patterned by age, sex, and socioeconomic position, but few studies have investigated how they combine to result in differential SBP risk. This study brings new insights by simultaneously considering sex, age, education, as well as race/ethnicity - a dimension seldom investigated in French health studies- in an intersectional perspective. Using data from the CONSTANCES cohort (2012-2021) in the French general population, we applied intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to examine SBP levels among 150,739 adults, not under BP lowering treatment, nested within 126 intersectional strata. Our models revealed substantial heterogeneity in SBP across strata, mainly driven by age and sex additive main effects. Older age, male sex, lower education, and Subsaharan African (SSA) and Overseas France (DROMs) groups were associated with increased SBP. SSA and DROMs individuals with fewer years of formal education consistently exhibited among the highest SBP values within each sex-age combination. Although age explained most of the between-strata variance, 25-39-year-old SSA and DROMs with fewer years of formal education displayed higher SBP levels than some 40-59-year-old individuals from other ethnoracial backgrounds, suggesting a premature increase of SBP levels for these strata. Our results show that SBP varies according to socially structured experiences, to the disadvantage of marginalized social groups. They emphasize the need for more intersectionality-grounded research on a wider range of biomarkers, and advocate for a more systematic inclusion of racism as a major axis of oppression in health inequities studies.

Oral health and social engagement of older adults: a longitudinal study.

Neuroligin-3 (NLGN3)was first identified as a risk gene associated with autism spectrum disorder (ASD). The initial variant, p.R451C, associatingNLGN3with ASD has been heavily investigated, yet little is known about the functional consequences of otherNLGN3variants. Furthermore, while most of the identified variants are present in males with maternally inherited variants from unaffected mothers, severalde novovariants were observed in females, suggesting a possible functional difference betweende novoand maternally inherited variants. To address the functional consequences ofNLGN3variantsin vivo, we generated transgenicDrosophilamodels corresponding to onede novovariant (p.R175W) and two maternally inherited variants (p.R451C and p.R597W). InDrosophila, loss of the fly homolog,Nlg3, altered sleep patterns, synaptic architecture, and vesicle dynamics, which were rescued by the expression of the humanNLGN3Refallele. When comparing the variants, thede novop.R175W variant and the maternally inherited p.R451C variant altered synapse morphology and sleep patterns, with minimal effects on vesicle dynamics, and the p.R597W variant altered sleep and vesicle dynamics with minimal impact on synapse morphology. Using overexpression models, humanNLGN3Refaltered sleep patterns and synaptic morphology. Moreover, the p.R175W variant exacerbated sleep phenotypes, and the p.R175W and p.R451C variants exacerbated synapse morphology phenotypes. Together, our findings suggest thatde novo NLGN3variants identified in females are likely gain-of-function, while maternally inherited variants have mixed loss- and gain-of-function effects. Moreover, the location of the variants may contribute to the distinct functional differences we observed. SomeNLGN3variants disrupt synaptic development, while other variants alter synaptic function, suggesting thatNLGN3variants have differential effects. These functional differences may provide insight into the heterogeneity of individuals with ASD.

The global epidemic of obesity and related metabolic disorders underscores the pressing need for safe and effective intervention strategies. The gut microbiota serves as a key regulatory target, with individual heterogeneity in its composition and function significantly influencing the efficacy of probiotic interventions. This review systematically outlines the distinct features and drivers of gut dysbiosis in various overweight populations, including children, pregnant women, the elderly, and adults, and reveals that microbiota heterogeneity is a central factor contributing to inconsistent outcomes in probiotic studies. Furthermore, it elucidates the specific molecular mechanisms through which certain probiotic strains, such as Lactobacillus, Bifidobacterium, and next-generation probiotics, modulate lipid metabolism by promoting lipolysis and fatty acid oxidation while inhibiting de novo lipogenesis, regulate glucose homeostasis via α-glucosidase inhibition, satiety hormones' regulation, and glucose metabolic pathway modulation, and alleviate oxidative and endoplasmic reticulum stress. This review also argues that establishing a precision probiotic intervention framework could offer both theoretical and practical support for the clinical translation of probiotic-based strategies in managing overweight conditions.

Large inequalities in fruit and vegetable consumption (FVC) persist, yet it remains unclear how intersecting factors such as socio-economic status, ethnicity and sex influence FVC in the UK. Using an intersectional framework allows us to explore complex realities and double burdens faced by certain population groups. Cross-sectional data from the UK Household Longitudinal Study Wave 9 (2017-2018) were analyzed. FVC was measured as a binary variable, indicating whether individuals met the recommended five daily portions of fruits and vegetables (400 grams in total). An intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy was used, nesting participants into forty-eight social strata based on sex, ethnicity, age and educational level. United Kingdom. A total of 16 275 individuals from the UK Household Longitudinal Study sample were included, with one adult randomly selected per household. Overall, 69·2 % of the sample did not meet the recommended daily FVC. Inequalities were predominantly explained by additive effects of sex, ethnicity, age and educational level. Men, individuals with lower educational levels, ethnic minority groups and younger participants were at higher risk of insufficient FVC, particularly those experiencing combinations of these factors. Low FVC across the population, combined with strong additive effects of social determinants, underscore the need for proportionate universal interventions. Policies targeting improved access to fruits and vegetables across all neighbourhoods, especially those predominantly inhabited by individuals with lower educational levels, are warranted to reduce these inequalities.