Mixture Model Approach Research Articles

Identifying Subpopulations of Cells in Single-Cell Transcriptomic Data: A Bayesian Mixture Modeling Approach to Zero Inflation of Counts.

In the study of single-cell RNA-seq (scRNA-Seq) data, a key component of the analysis is to identify subpopulations of cells in the data. A variety of approaches to this have been considered, and although many machine learning-based methods have been developed, these rarely give an estimate of uncertainty in the cluster assignment. To allow for this, probabilistic models have been developed, but scRNA-Seq data exhibit a phenomenon known as dropout, whereby a large proportion of the observed read counts are zero. This poses challenges in developing probabilistic models that appropriately model the data. We develop a novel Dirichlet process mixture model that employs both a mixture at the cell level to model multiple populations of cells and a zero-inflated negative binomial mixture of counts at the transcript level. By taking a Bayesian approach, we are able to model the expression of genes within clusters, and to quantify uncertainty in cluster assignments. It is shown that this approach outperforms previous approaches that applied multinomial distributions to model scRNA-Seq counts and negative binomial models that do not take into account zero inflation. Applied to a publicly available data set of scRNA-Seq counts of multiple cell types from the mouse cortex and hippocampus, we demonstrate how our approach can be used to distinguish subpopulations of cells as clusters in the data, and to identify gene sets that are indicative of membership of a subpopulation.

Trajectories of cognitive flexibility through kindergarten and first grade: Implications for externalizing and internalizing behavior problems in the second grade.

Developmental delays in cognitive flexibility early in elementary school can potentially increase vulnerability for subsequent externalizing and internalizing psychopathology. The first goal of the current study was to identify latent subgroups of children characterized by different developmental trajectories of cognitive flexibility throughout kindergarten and first grade using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011 dataset. The second goal was to examine whether identified longitudinal developmental trajectories of cognitive flexibility could be associated with internalizing and externalizing behaviors in the second grade, while accounting for background child (age, gender, and Spanish-speaking) and family (family income and mother's education) covariates. The analytic sample consisted of 15,827 kindergarteners (51.20% male; 48.50% White, 13.5% Black/African American, 24.3% Hispanic/Latino, 7.60% Asian, and 6.1% other), who were approximately 5.62 years old (SD = 4.48 months) at the study's outset. Most children lived in households with medium family income of approximately $50,000-$55,000. Using a growth mixture modeling approach, our analyses identified normative (91.05%; 50.4% male) and delayed (8.95%; 59.4% male) cognitive flexibility groups and demonstrated that delayed developers have higher levels of externalizing and internalizing behaviors in the second grade, even after adjusting for background covariates. Our findings, in conjunction with research on cognitive flexibility training, suggest that caregivers may lower the risk for externalizing and internalizing behaviors in delayed developers by correcting inflexible thinking, encouraging alternative solutions, and providing emotional support when children face challenging problems. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Analysis of heterogeneous growth changes in longitudinal height of children

BackgroundThere have been methodologies developed for a wide range of longitudinal data types; nevertheless, the conventional growth study is restricted if individuals in the sample have heterogeneous growth trajectories across time. Using growth mixture modeling approaches, we aimed to investigate group-level heterogeneities in the growth trajectories of children aged 1 to 15 years.MethodThis longitudinal study examined group-level growth heterogeneities in a sample of 3401 males and 3200 females. Data were analyzed using growth mixture modeling approaches.ResultsWe examined different trajectories of growth change in children across four low- and middle-income countries using a data-driven growth mixture modeling technique. The study identified two-group trajectories: the most male samples group (n = 4260, 69.7%) and the most female samples group (n = 2341, 81.6%). The findings show that the two groups had different growth trajectories. Gender and country differences were shown to be related to growth factors; however, the association varied depending on the trajectory group. In both latent groups, females tended to have lower growth factors (initial height and rate of growth) than their male counterparts. Compared with children from Ethiopia, children from Peru and Vietnam tended to exhibit faster growth in height over time: In contrast, children from India showed a lower rate of change in both latent groups than that of children from Ethiopia.ConclusionsThe height of children in four low- and middle-income countries showed heterogeneous changes over time with two different groups of growth trajectories.

Inferring the age and sex of ancient potters from fingerprint ridge densities: A data-driven, Bayesian mixture modelling approach

The density of epidermal ridges in a fingerprint varies predictably by age and sex. Archaeologists are therefore interested in using recovered fingerprints to learn about the ancient people who produced them. Recent studies focus on estimating the age and sex of individuals by measuring their fingerprints with one of two similar metrics: mean ridge breadth (MRB) or ridge density (RD). Yet these attempts face several critical problems: expected values for adult females and adolescent males are inherently indistinguishable, and inter-assemblage variation caused by biological and technological differences cannot be easily estimated. Each of these factors greatly decreases the accuracy of predictions based on individual prints, and together they condemn this strategy to relative uselessness. However, information in fingerprints from across an assemblage can be pooled to generate a more accurate depiction of potter demographics. We present a new approach to epidermal ridge density analysis using Bayesian mixture models with the following key benefits:•Age and sex are estimated more accurately than existing methods by incorporating a data-driven understanding of how demographics and ridge density covary.•Uncertainty in demographic estimates is automatically quantified and included in output.•The Bayesian framework can be easily adapted to fit the unique needs of different researchers.

Using mixture modeling to examine differences in perceptual decision-making as a function of the time and method of participant recruitment

We examine whether perceptual decision-making differs as a function of the time in the academic term and whether the participant is an undergraduate participating for course credit, a paid in-person participant, or a paid online participant recruited via Amazon Mechanical Turk. We use a mixture modeling approach within an evidence accumulation framework that separates stimulus-driven responses from contaminant responses, allowing us to distinguish between performance when a participant is engaged in the task and the consistency in this task focus. We first report a survey showing cognitive psychologists expect performance and response caution to be lower among undergraduate participants recruited at the end of the academic term compared to those recruited near the start, and highest among paid in-person participants. The findings from two experiments using common paradigms revealed very little evidence of time-of-semester effects among course credit participants on accuracy, response time, efficiency of information processing (when engaged in the task), caution, and non-decision time, or consistency in task focus. However, paid in-person participants did tend to be more accurate than the other two groups. Groups showed similar effects of speed/accuracy emphasis on response caution and of discrimination difficulty on information processing efficiency, but the effect of speed/accuracy emphasis on information processing efficiency was less consistent among groups. We conclude that online crowdsourcing platforms can provide quality perceptual decision-making data, but recommend that mixture modeling be used to adequately account for data generated by processes other than the psychological phenomena under investigation.

Patterns of computed tomography utilisation in injury management: latent classes approach using linked administrative data in Western Australia

PurposeWhilst computed tomography (CT) imaging has been a vital component of injury management, its increasing use has raised concern regarding ionising radiation exposure. This study aims to identify latent classes (underlying patterns) of CT use over a 3-year period following the incidence of injury and factors predicting the observed patterns.MethodA retrospective observational cohort study was conducted in 21,544 individuals aged 18 + years presenting to emergency departments (ED) of four tertiary public hospitals with new injury in Western Australia. Mixture modelling approach was used to identify latent classes of CT use over a 3-year period post injury.ResultsAmongst injured people with at least one CT scan, three latent classes of CT use were identified including a: temporarily high CT use (46.4%); consistently high CT use (2.6%); and low CT use class (51.1%). Being 65 + years or older, having 3 + comorbidities, history with 3 + hospitalisations and history of CT use before injury were associated with consistently high use of CT. Injury to the head, neck, thorax or abdomen, being admitted to hospital after the injury and arriving to ED by ambulance were predictors for the temporarily high use class. Living in areas of higher socio-economic disadvantage was a unique factor associated with the low CT use class.ConclusionsInstead of assuming a single pattern of CT use for all patients with injury, the advanced latent class modelling approach has provided more nuanced understanding of the underlying patterns of CT use that may be useful for developing targeted interventions.

Spatiotemporal Interaction Pattern Recognition and Risk Evolution Analysis During Lane Changes

In complex lane change (LC) scenarios, semantic interpretation and safety analysis of dynamic interaction pattern are necessary for autonomous vehicles to make appropriate decisions. This study proposes a learning framework that combines primitive-based interaction pattern recognition and risk analysis. The Hidden Markov Model with the Gaussian mixture model (GMM-HMM) approach is developed to decompose the LC scenarios into primitives. Then K-means clustering with Dynamic Time Warping (DTW) is applied to gather the primitives into 13 LC interaction patterns. Finally, this study considers time-to-collision (TTC) of two conflict types involved in the LC process. And the TTC is used to analyze the risk of interaction patterns and extract high-risk LC interaction patterns. The LC events obtained from the Highway Drone Dataset (highD) demonstrate that the identified LC interaction patterns contain interpretable semantic information. This study identifies the dynamic spatiotemporal characteristics and risk formation mechanism of the LC interaction patterns. The findings are useful to comprehensively understand the latent interaction patterns, which can then be used to design and improve the decision-making process during lane changes and enhance the safety of autonomous vehicle.

Leveraging genetic overlap between irritability and psychiatric disorders to identify genetic variants of major psychiatric disorders

Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples (105,975 cases and 273,531 controls) from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [n] of additional SNPs = 105), BD I (n = 54), MDD (n = 107), and irritability (n = 157). Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.

Using Response Times for Joint Modeling of Careless Responding and Attentive Response Styles

Questionnaires are by far the most common tool for measuring noncognitive constructs in psychology and educational sciences. Response bias may pose an additional source of variation between respondents that threatens validity of conclusions drawn from questionnaire data. We present a mixture modeling approach that leverages response time data from computer-administered questionnaires for the joint identification and modeling of two commonly encountered response bias that, so far, have only been modeled separately—careless and insufficient effort responding and response styles (RS) in attentive answering. Using empirical data from the Programme for International Student Assessment 2015 background questionnaire and the case of extreme RS as an example, we illustrate how the proposed approach supports gaining a more nuanced understanding of response behavior as well as how neglecting either type of response bias may impact conclusions on respondents’ content trait levels as well as on their displayed response behavior. We further contrast the proposed approach against a more heuristic two-step procedure that first eliminates presumed careless respondents from the data and subsequently applies model-based approaches accommodating RS. To investigate the trustworthiness of results obtained in the empirical application, we conduct a parameter recovery study.

Differences in the longitudinal change of behaviours related to dementia in long-term care: a growth mixture modelling approach

BackgroundThere is still a need for more information about the different trajectories of responsive behaviours that people living with dementia present in long-term care homes (LTC). Objective. This study identified subgroups of individuals with similar trajectories of responsive behaviours related to dementia in LTC and evaluated the role of demographic variables, depressive symptomatology, social engagement, cognitive functioning, and activities of daily living (ADL) on class membership.MethodsGrowth mixture models were run using data from the Continuing Care Reporting System.ResultsResults suggest that change in responsive behaviours is best represented by seven classes of trajectories. The largest class was composed of individuals who presented the lowest frequency of behaviours upon entry in LTC that increased at a slow linear rate. The other classes were composed of individuals who presented different frequencies of behaviours upon entry in LTC and varying rates of change (e.g., individuals who presented a low frequency of behaviours upon entry in LTC that increased at a linear rate followed by a decrease in the later months, individuals who presented a high frequency of responsive behaviours upon entry in LTC and that remained stable). Cognitive functioning, social engagement, depressive symptomatology, and ADL were markers of class membership.ConclusionsThese findings can help identify individuals at increased risk of presenting a high frequency of responsive behaviours and highlight interventions that could decrease behaviours in LTC.

Trajectories of college students' general self-efficacy, the related predictors, and depression: A piecewise growth mixture modeling approach

General self-efficacy plays a critical role in the development of college students, and mastering the development of students' general self-efficacy is helpful to explain students' behavior and psychological performance. Based on the data from the same cohort of college students for four consecutive years, this study used the piecewise growth mixture model to identify the developmental trajectories of general self-efficacy, built a multinomial logistic regression model to analyze the related predictors on different trajectories, and further compared the differences in depression symptoms in general self-efficacy trajectories. Three trajectories of college students' general self-efficacy were identified: stable-rising (8.7%), stable-decreasing (2.4%), and moderate and stable (88.9%). With the moderate and stable class as the reference, gender and extraversion are the predictors of students in the stable-increasing class; gender, extraversion, mother’s education level, and university tier significantly predict students who fall into the stable-decreasing class. With the stable-increasing class as the reference, gender also has a significant predictive effect on students who belong to the stable-decreasing class. However, age, ethnicity, siblings, hometown location, father’s education level, BMI, sleep, and major were not related predictors. Furthermore, mean differences in depression between latent classes of general self-efficacy trajectories were significant, and the depression scores of the stable-decreasing class were beyond the normal range in the third and fourth years. To promote college students' mental health, we suggest that colleges provide more specific psychological interventions to students based on the classification.

Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach

To effectively reduce the number of red-light violations and crashes, it is crucial to explore RLR behavior at local intersections, understand the contributing factors, and identify the riskiest intersections by estimating RLR frequency. In this study, a finite mixture modeling method was utilized to understand the contributing factors to RLR behavior and estimate this violating behavior. To develop the RLR estimation models, performance metrics and signal phasing data were collected from the Automated Traffic Signal Performance Measures (ATSPMs) system in two jurisdictions in Arizona: Pima County and the Town of Marana. The results from calibrated models showed that an increase in traffic flow, intersection delay, number of approach lanes, and split failure is associated with an increase in the likelihood of observing red-light violations. In addition, it was found that an increase in cycle length is associated with a decrease in the likelihood of observing the red-light violation. The results of comparing the proposed RLR estimation method with several conventional methods, the Poisson Generalized Linear Model (PGLM), Zero-inflated Poisson Regression Model (ZIPM), and Zero-inflated Negative Binomial Regression Model (ZINB) showed the proposed method outperforms all the models in terms of both model fit and accuracy. The application of the proposed method could be used to analyze the intersections with the highest number of red-light violations. Furthermore, the presented transferability results can be advantageous to transportation agencies within Arizona and urban areas with similar characteristics by providing insight into which model specifications may provide the best RLR estimation accuracy.

Repetition learning is neither a continuous nor an implicit process

Learning advances through repetition. A classic paradigm for studying this process is the Hebb repetition effect: Immediate serial recall performance improves for lists presented repeatedly as compared to nonrepeated lists. Learning in the Hebb paradigm has been described as a slow but continuous accumulation of long-term memory traces over repetitions [e.g., Page & Norris, Phil. Trans. R. Soc. B 364, 3737-3753 (2009)]. Furthermore, it has been argued that Hebb repetition learning requires no awareness of the repetition, thereby being an instance of implicit learning [e.g., Guérard etal., Mem. Cogn. 39, 1012-1022 (2011); McKelvie, J. Gen. Psychol.114, 75-88 (1987)]. While these assumptions match the data from a group-level perspective, another picture emerges when analyzing data on the individual level. We used a Bayesian hierarchical mixture modeling approach to describe individual learning curves. In two preregistered experiments, using a visual and a verbal Hebb repetition task, we demonstrate that 1) individual learning curves show an abrupt onset followed by rapid growth, with a variable time for the onset of learning across individuals, and that 2) learning onset was preceded by, or coincided with, participants becoming aware of the repetition. These results imply that repetition learning is not implicit and that the appearance of a slow and gradual accumulation of knowledge is an artifact of averaging over individual learning curves.

Metals and per- and polyfluoroalkyl substances mixtures and birth outcomes in the New Hampshire Birth Cohort Study: Beyond single-class mixture approaches

We aimed to investigate the joint, class-specific, and individual impacts of (i) PFAS, (ii) toxic metals and metalloids (referred to collectively as “metals”), and (iii) essential elements on birth outcomes in a prospective pregnancy cohort using both established and recent mixture modeling approaches. Participants included 537 mother-child pairs from the New Hampshire Birth Cohort Study. Concentrations of 6 metals and 5 PFAS were measured in maternal toenail clippings and plasma, respectively. Birth weight, birth length, and head circumference at birth were abstracted from medical records. Joint, index-wise, and individual associations of the metals and PFAS concentrations with birth outcomes were evaluated using Bayesian Kernel Machine Regression (BKMR) and Bayesian Multiple Index Models (BMIM). After controlling for potential confounders, the metals-PFAS mixture was associated with a larger head circumference at birth, which was driven by manganese. When using BKMR, the difference in the head circumference z-score when changing manganese from its 25th to 75th percentiles while holding all other mixture components at their medians was 0.22 standard deviations (95% posterior credible interval [CI]: −0.02, 0.46). When using BMIM, the posterior mean of index weight estimates assigned to manganese for head circumference z-score was 0.72 (95% CI: 0, 0.99). Prenatal exposure to the metals-PFAS mixture was not associated with birth weight or birth length by either BKMR or BMIM. Using both traditional and new mixture modeling approaches, prenatal exposure to manganese was associated with a larger head circumference at birth after accounting for exposure to PFAS and multiple toxic and essential metals.

Evolution of the proportion of colistin-resistant isolates in animal clinical Escherichia coli over time - A hierarchical mixture model approach.

Colistin resistance has been the subject of much attention since mcr genes encoding plasmid-mediated colistin resistance description in 2015. To date, surveillance data about resistance levels encountered in food-producing animals are scarce. In France, the Resapath dataset, consisting in a large collection of disk diffusion antibiogram results transmitted by a network of laboratories. It offers a unique opportunity to study the evolution of resistance towards colistin over the past 15 years in Escherichia coli isolated from diseased food-producing animals. This study used a Bayesian hierarchical Gaussian mixture model to estimate the resistant proportions from those data. This non-classical approach deals with the colistin-specific problem of overlapping distributions of diameters measured for susceptible and resistant isolates that makes the definition of epidemiological cut-off very hard. This model also considers the variability observed between the measurements performed by different laboratories. Proportion of resistant isolates has been calculated for several food-producing animals and most encountered diseases. From those estimations, a marked evolution of the proportions of resistant isolates is noticeable, for swine suffering from digestive disorders. In this group, an increase over the 2006-2011 period from 0.1% [ 0.0%, 1.2%] in 2006-28.6% [25.1%, 32.3%] in 2011 was followed by a decrease to reach 3.6% [2.3%;5.3%] in 2018. For isolates related to digestive disorders in calves, percentages increased and reached 7% in 2009 then decreased as for swine. In contrast, for poultry productions, estimated proportions and credibility intervals were constantly very close to zero.

A latent profile approach for classifying internet gamers based on motives for online gaming.

Online gaming motives have proven to be useful in differentiating problematic engagement in online gaming. However, the mixture modeling approach for classifying problematic subtypes based on gaming motives remains limited. This study attempted to differentiate heterogeneous online gamers into more homogenous subtypes based on gaming motives using latent profile analysis (LPA). We also compared various psychological and gaming/leisure related variables across the derived profiles. A total of 674 Korean online game users (mean age = 21.81 years, male= 76%) completed self-report questionnaires, including the Korean version of the Motives for Online Gaming Questionnaire (K-MOGQ). After the LPA, the relationships between latent profile membership and auxiliary variables were explored. Four latent profiles were identified, that were further classified into one problematic (highly motivated-dissatisfied gamer), one highly engaged (highly motivated-satisfied gamer), and two casual (moderately-motivated casual gamer and lowly-motivated casual gamer) gamer profiles. Inter-profile comparisons revealed that highly motivated-dissatisfied gamer had the most pathological profile, characterized by high Internet gaming disorder (IGD) tendency, neuroticism, and impulsivity, but the lowest recreation motive. While highly motivated-satisfied gamer also demonstrated a heightened IGD tendency, they showed positive patterns of psychological and gaming/leisure-related variables, which indicated they could be better considered as high engaged instead of problematic gamers. These results indicate that the recreation motive, in addition to fantasy or escape motives, is an important factor in differentiating maladaptive online gamers. Classifying online gamers based on gaming motives can contribute to a clearer conceptualization of heterogeneousgamers, paving the way for individualized assessment and treatment planning.

Latent class analysis of multigroup heterogeneity in propensity for academic dishonesty

ABSTRACT Latent class analysis (LCA) is a cross-sectional latent variable mixture modeling (LVMM) approach. Like all LVMM approaches, LCA aims to find heterogeneity within the population by identifying homogenous subgroups of individuals, with each subgroup (called latent class) possessing a unique set of characteristics that differentiate it from other subgroups. LCA can be carried out with categorical latent and indicator variables. But, LCA is unable to examine the association between respective items and the latent variable among categories of individuals. Multiple-group LCA, in particular, is a useful extension of LCA which enables the testing of homogeneity of the class patterns between groups of the individual through a series of constraints. In this paper, we have performed a multi-group latent class analysis for measuring self reported academic dishonesty among the students of University of Jammu. From the analysis, three general behaviors of academic cheaters are identified as rare, frequent, and instant cheaters. Further, from the multi-group LCA, it is envisaged that female students of University of Jammu are more instantaneous cheaters than male students. Students who are self-reported cheaters from sciences and humanities of the University of Jammu are persistent in cheating whereas from professional courses they are more occasional.

Developmental Trajectories and Predictors of Self-Esteem in Childhood: Growth Mixture Modeling Approach

Objectives: The four-wave longitudinal study explored developmental trajectories of self-esteem of elementary school children and examined the predictive factors such as the mother’s parenting behavior, children’s happiness and school adjustment associated with these trajectories.Methods: Trajectories of self-esteem from grades 1 to 4 were modeled using 1,659 children drawn from the Panel Study on Korean Children (PSKC) 2015~2018. Growth Mixture Modeling (GMM) analysis was used to estimate trajectories of self-esteem in grades 1~4. Using multiple logistic regression, the identified trajectories were then used to analyze whether the mother’s parenting, children’s happiness, and school adjustment affected the latent class classification.Results: First, the self-esteem of grades 1 to 4 revealed a linear growth pattern over time. Second, GMM identified four developmental trajectories in childhood self-esteem, of which the low-increasing, low-decreasing, high-increasing, or moderate-decreasing group reflected the normative trajectories. Third, the mother’s warm parenting, the academic performance of school adjustment, and the happiness of the children were significant in predicting the classification of latent class in the change trajectories of self-esteem.Conclusion: The results showed individual differences and heterogeneity in the development of selfesteem. Groups’ initial different developmental levels at first grade continue until grade 4. Developmental trajectories provide the direction for interventions by the level of self-esteem for elementary children. Especially, educational programs and interventions in mothers’ warm parenting, children’s happiness, and academic performance, which affect self-esteem trajectories, would be needed. These findings provide a meaningful foundation for designing programs and practical suggestions to encourage the self-esteem of elementary children.

Associations between body image patterns and body image-related pathology in sexual minority individuals: A mixture-modeling approach

Body image concerns are associated with disordered eating, body dysmorphic disorder (BDD), and illicit appearance and performance enhancing drug (APED) misuse. Sexual minority individuals are a vulnerable population for body image concerns. Latent profile analyses were used to identify body image patterns in sexual minority men (n = 479) and women (n = 483) and to investigate associations between latent profiles and body image-related pathology. A 5-profile solution demonstrated best fit for men and a 4-profile solution for women. Among women, co-occurring high thinness/muscularity concerns had higher probability of vomiting and dietary restriction than the high thinness/low muscularity concerns profile. The probability of driven exercise did not differ across latent profiles for men and only differed between low thinness/low muscularity and high thinness/high muscularity concerns profiles in women. Men with low thinness/high muscularity concerns had significantly lower probability of vomiting and laxative misuse than all other profiles but did not significantly differ from co-occurring high or moderate thinness/muscularity concerns profiles in binge eating and illicit APED misuse. This study may have implications for future research, treatment, and prevention of body image-related pathology in sexual minority individuals.

A deconvolution approach to modelling surges in COVID-19 cases and deaths

The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures, which provides a retrospective framework for responding to future pandemics and disease outbreaks.