Social media use and mental health indicators among US adolescents: A population-based study

Social media use among young people has raised concerns about its potential impact on mental health. However, research is limited regarding whether certain subgroups may be differently affected. This study uses data from the 2022 National Youth Tobacco Survey, a nationally representative sample of middle and high school students aged approximately 11-18 years (n = 23,366). Mental health conditions were assessed using the Patient Health Questionnaire-4, and social media use was categorized by frequency levels. We employed multinomial logistic regression and a finite mixture Poisson model to explore the relationship between social media use, sexual identity, and mental health status. The study found a consistent association between social media use and mental health conditions, particularly among frequent users. The finite mixture model revealed two latent groups based on mental health status: a 'better' group with minimal or no poor mental health indicators and a 'worse' group with more indicators. For both groups, social media use was associated with mental health conditions, with a stronger association among frequent users. Notably, sexual minorities, especially bisexual students, were more likely to report poor mental health indicators. This study suggests that frequent social media use may adversely affect young people's mental health and that different youth subgroups may respond differently to social media use and mental health conditions.

Model Selection for Exponential Power Mixture Regression Models

Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for FMLR models via an exponential power error distribution, which includes normal distributions and Laplace distributions as special cases. Under some regularity conditions, the consistency of order selection and the consistency of variable selection are established, and the asymptotic normality for the estimators of non-zero parameters is investigated. In addition, an efficient modified expectation-maximization (EM) algorithm and a majorization-maximization (MM) algorithm are proposed to implement the proposed optimization problem. Furthermore, we use the numerical simulations to demonstrate the finite sample performance of the proposed methodology. Finally, we apply the proposed approach to analyze a baseball salary data set. Results indicate that our proposed method obtains a smaller BIC value than the existing method.

MODELING CELL POPULATIONS MEASURED BY FLOW CYTOMETRY WITH COVARIATES USING SPARSE MIXTURE OF REGRESSIONS

The ocean is filled with microscopic microalgae, called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small- and large-scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the northeast Pacific in the spring of 2017.

Semiparametric finite mixture of regression models with Bayesian P-splines

Mixture models provide a useful tool to account for unobserved heterogeneity and are at the basis of many model-based clustering methods. To gain additional flexibility, some model parameters can be expressed as functions of concomitant covariates. In this Paper, a semiparametric finite mixture of regression models is defined, with concomitant information assumed to influence both the component weights and the conditional means. In particular, linear predictors are replaced with smooth functions of the covariate considered by resorting to cubic splines. An estimation procedure within the Bayesian paradigm is suggested, where smoothness of the covariate effects is controlled by suitable choices for the prior distributions of the spline coefficients. A data augmentation scheme based on difference random utility models is exploited to describe the mixture weights as functions of the covariate. The performance of the proposed methodology is investigated via simulation experiments and two real-world datasets, one about baseball salaries and the other concerning nitrogen oxide in engine exhaust.

Impact of COVID-19 on the Volume of Acute Stroke Admissions: A Nationwide Survey in Japan

This study aimed to measure the impact of the COVID-19 pandemic on the volumes of annual stroke admissions compared with those before the pandemic in Japan. We conducted an observational, retrospective nationwide survey across 542 primary stroke centers in Japan. The annual admission volumes for acute stroke within 7 days from onset between 2019 as the pre-pandemic period and 2020 as the pandemic period were compared as a whole and separately by months during which the epidemic was serious and prefectures of high numbers of infected persons. The number of stroke patients declined from 182,660 in 2019 to 178,083 in 2020, with a reduction rate of 2.51% (95% confidence interval [CI], 2.58%-2.44%). The reduction rates were 1.92% (95% CI, 1.85%-2.00%; 127,979-125,522) for ischemic stroke, 3.88% (95% CI, 3.70%-4.07%, 41,906-40,278) for intracerebral hemorrhage, and 4.58% (95% CI, 4.23%-4.95%; 13,020-12,424) for subarachnoid hemorrhage. The admission volume declined by 5.60% (95% CI, 5.46%-5.74%) during the 7 months of 2020 when the epidemic was serious, whereas it increased in the remaining 5 months (2.01%; 95% CI, 1.91%-2.11%). The annual decline in the admission volume was predominant in the five prefectures with the largest numbers of infected people per million population (4.72%; 95% CI, 4.53%-4.92%). In conclusion, the acute stroke admission volume declined by 2.51% in 2020 relative to 2019 in Japan, especially during the months of high infection, and in highly infected prefectures. Overwhelmed healthcare systems and infection control practices may have been associated with the decline in the stroke admission volume during the COVID-19 pandemic.

Does dropout from school matter in taking antenatal care visits among women in Bangladesh? An application of marginalized poisson-poisson mixture model

Background

There exists a lack of research in explaining the link between dropout from school and antenatal care (ANC) visits of women during pregnancy in Bangladesh. The aim of this study is to investigate how the drop out from school influences the ANC visits after controlling the relevant covariates using an appropriate count regression model.

Methods

The association between the explanatory variables and the outcome of interest, ANC visits, have been performed using one-way analysis of variance/independent sample t-test. To examine the adjusted effects of covariates on the marginal mean of count data, Marginalized Poison-Poisson mixture regression model has been fitted.

Results

The estimated incidence rate of antenatal care visits was 10.6% lower for the mothers who were not continued their education after marriage but had at least 10 years of schooling (p-value <0.01) and 20.2% lower for the drop-outed mothers (p-value <0.01) than the mothers who got continued their education after marriage.

Conclusions

To ensure the WHO recommended 8+ ANC visits for the pregnant women of Bangladesh, it is essential to promote maternal education so that at least ten years of schooling should be completed by a woman and dropout from school after marriage should be prevented.

A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification

The vital data about the electrical activities of the brain are carried by the electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.

The effect of data aggregation on dispersion estimates in count data models

For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can be aggregated without loss of information on the Poisson parameter, which remains true if the Poisson assumption is relaxed towards quasi-Poisson. However, in biodosimetry in particular, but also beyond, the question of how the dispersion estimates for quasi-Poisson models behave under data aggregation have received little attention. Indeed, for real data sets featuring unexplained heterogeneities, dispersion estimates can increase strongly after aggregation, an effect which we will demonstrate and quantify explicitly for some scenarios. The increase in dispersion estimates implies an inflation of the parameter standard errors, which, however, by comparison with random effect models, can be shown to serve a corrective purpose. The phenomena are illustrated by γ-H2AX foci data as used for instance in radiation biodosimetry for the calibration of dose-response curves.

Modeling and prediction of dengue occurrences in Kolkata, India, based on climate factors

Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.

Laparoscopic transperitoneal adrenalectomy: a comparative study of different techniques for vessel sealing

BACKGROUND

Laparoscopic adrenalectomy is the standard surgical approach to adrenal lesions. Adrenal vessel sealing is the critical surgical phase of laparoscopic adrenalectomy. This study aimed at comparing perioperative outcomes of laparoscopic transperitoneal adrenalectomy by means of radiofrequency energy-based device (LARFD) to those performed with traditional clipping device (LACD), while focusing on the different adrenal vessel control techniques.

METHODS

Patients who underwent adrenalectomy for adrenal disease between January 1994 and April 2019 at the Surgical Clinic, Polytechnic University of Marche were included in the study. Overall, 414 patients met inclusion criteria for study eligibility: 211 and 203 patients underwent LARFD and LACD, respectively. Multiple models of quantile regression, logistic regression and Poisson finite mixture regression were used to assess the relationship between operative time, conversion to open procedure, length of stay (LoS), surgical procedure and patient characteristics, respectively.

RESULTS

LARFD reduced operative time of about 12 min compared to LACD. Additional operative time-related factors were surgery side, surgery approach, conversion to open procedure and trocar number. The probability of conversion to open procedure decreased by about 76% for each added trocar, whereas it increased by about 49% for each added centimeter of adrenal lesion and by about 25% for each added year of surgery. Two patient clusters were identified based on the LoS: long-stay and short-stay. In the long-stay cluster, LoS decreased of about 30% in LARFD group and it was significantly associated with conversion to open procedure and postoperative complications, whereas in short-stay cluster only postoperative complications had a significant effect on LoS.

CONCLUSION

Laparoscopic transperitoneal adrenalectomy performed by means of radiofrequency energy-based device for the sealing of adrenal vessels is an effective procedure reducing operative time with potentially improved postoperative outcomes.

Modelling RNA-Seq data with a zero-inflated mixture Poisson linear model

RNA sequencing (RNA-Seq) has been frequently used in genomic studies and has generated a vast amount of data. The RNA-Seq data are composed of two parts: (a) a sequence of nucleotides of the genome; and (b) a corresponding sequence of counts, standing for the number of short reads whose mapped positions start at each position of the genome. One common feature of these count data is that they are typically nonuniform; recent studies have revealed that the nonuniformity is partially owing to a systematic bias resulted from the sequencing preference. Existing works in the literature model the nonuniformity with a single component Poisson linear model that incorporates the effects of the sequencing preference. However, we observe consistently that the short reads mapped to a gene may have a mixture structure and can be zero-inflated. A single component model may not suffice to model the complexity of such data. In this paper, we propose a zero-inflated mixture Poisson linear model for the RNA-Seq count data and derive a fast expectation-maximisation-based algorithm for estimating the unknown parameters. Numerical studies are conducted to illustrate the effectiveness of our method.

Marginalized mixture models for count data from multiple source populations

Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models. In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome. The models are examined using simulations and applied to two datasets, one from a double-blind dental caries incidence trial, and the other from a horticultural experiment. The finite sample performance of the proposed models are compared with each other and with marginalized zero-inflated count models, as well as ordinary Poisson and negative binomial regression.

A general EM approach for maximum likelihood estimation in mixed Poisson regression models

An EM type algorithm for maximum likelihood estimation is proposed for the case of mixed Poisson regression models. The algorithm makes use of the mixture representation of such models. Two members of this family are examined in depth, the negative binomial regression model and the Poisson-inverse Gaussian regression model. Closed form expressions are derived for both models leading to easily programmable algorithms. Especially for the case of the Poisson-inverse Gaussian model no special numerical techniques are needed. The algorithms are applied to a real data set concerning crime data from Greece.

Joint modelling of recurrence and progression of adenomas: a latent variable approach

In this paper, we treat the number of recurrent adenomatous polyps as a latent variable and then use a mixture distribution to model the number of observed recurrent adenomatous polyps. This approach is equivalent to zero-inflated Poisson regression, which is a method used to analyse count data with excess zeros. In a zero-inflated Poisson model, a count response variable is assumed to be distributed as a mixture of a Poisson distribution and a distribution with point mass of one at zero. In many cancer studies, patients often have variable follow-up. When the disease of interest is subject to late onset, ignoring the length of follow-up will underestimate the recurrence rate. In this paper, we modify zero-inflated Poisson regression through a weight function to incorporate the length of follow-up into analysis. We motivate, develop, and illustrate the methods described here with an example from a colon cancer study.

Impact of Firm Performance on Board Characteristics: Empirical Evidence from India

This study attempts to examine the impact of prior and current firm performance on board composition as it is the least explored issue in the corporate governance area. For this purpose, our analysis covers a large sample of the Indian manufacturing firms for the period 2001–2010. We utilize a range of measures of firm performance such as return on assets, return on equity, net profit margin, adjusted Tobin’s q and stock returns in the analysis. We also use a range of alternative measures of board characteristics like board size, independence and meetings in the estimation process. The results of the study show that firm performance has a negative impact on board characteristics. Findings of the study also indicate that the larger board, outside membership and more meetings are considered as expensive affairs in the firm. Our findings in this study are expected to generate further debate on the related issue and sensitize the scholars to reason further research in this area especially in context of developing countries.

Human Capital Redistribution in the USA: The Migration of the College-bound

Almost all the contributions on human capital and migration have focused on individuals who recently completed a tertiary education degree. Not much has been done with regard to high-school leavers. However, studying the migration of high-school leavers (college-bound individuals), is at least as important as studying college graduates' migration. We present an analysis of college-bound individuals' migration patterns for the USA. We argue that understanding the main determinants of these migration patterns is fundamental for policy makers in their 'quest for human capital retention'.

Improved random-starting method for the EM algorithm for finite mixtures of regressions

Two methods for generating random starting values for the expectation maximization (EM) algorithm are compared in terms of yielding maximum likelihood parameter estimates in finite mixtures of regressions. One of these methods is ubiquitous in applications of finite mixture regression, whereas the other method is an alternative that appears not to have been used so far. The two methods are compared in two simulation studies and on an illustrative data set. The results show that the alternative method yields solutions with likelihood values at least as high as, and often higher than, those returned by the standard method. Moreover, analyses of the illustrative data set show that the results obtained by the two methods may differ considerably with regard to some of the substantive conclusions. The results reported in this article indicate that in applications of finite mixture regression, consideration should be given to the type of mechanism chosen to generate random starting values for the EM algorithm. In order to facilitate the use of the proposed alternative method, an R function implementing the approach is provided in the Appendix of the article.

Evidence for overdispersion in the distribution of malaria parasites and leukocytes in thick blood smears

Background

Microscopic examination of stained thick blood smears (TBS) is the gold standard for routine malaria diagnosis. Parasites and leukocytes are counted in a predetermined number of high power fields (HPFs). Data on parasite and leukocyte counts per HPF are of broad scientific value. However, in published studies, most of the information on parasite density (PD) is presented as summary statistics (e.g. PD per microlitre, prevalence, absolute/assumed white blood cell counts), but original data sets are not readily available. Besides, the number of parasites and the number of leukocytes per HPF are assumed to be Poisson-distributed. However, count data rarely fit the restrictive assumptions of the Poisson distribution. The violation of these assumptions commonly results in overdispersion. The objectives of this paper are to investigate and handle overdispersion in field-collected data.

Methods

The data comprise the records of three TBSs of 12-month-old children from a field study of Plasmodium falciparum malaria in Tori Bossito, Benin. All HPFs were examined systemically by visually scanning the film horizontally from edge to edge. The numbers of parasites and leukocytes per HPF were recorded and formed the first dataset on parasite and leukocyte counts per HPF. The full dataset is published in this study. Two sources of overdispersion in data are investigated: latent heterogeneity and spatial dependence. Unobserved heterogeneity in data is accounted for by considering more flexible models that allow for overdispersion. Of particular interest were the negative binomial model (NB) and mixture models. The dependent structure in data was modelled with hidden Markov models (HMMs).

Results

The Poisson assumptions are inconsistent with parasite and leukocyte distributions per HPF. Among simple parametric models, the NB model is the closest to the unknown distribution that generates the data. On the basis of model selection criteria AIC and BIC, HMMs provided a better fit to data than mixtures. Ordinary pseudo-residuals confirmed the validity of HMMs.

Conclusion

Failure to take overdispersion into account in parasite and leukocyte counts may entail important misleading inferences when these data are related to other explanatory variables (malariometric or environmental). Its detection is therefore essential. In addition, an alternative PD estimation method that accounts for heterogeneity and spatial dependence should be seriously considered in epidemiological studies with field-collected parasite and leukocyte data.

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach

BACKGROUND

Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.

METHODS AND FINDINGS

We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.

CONCLUSIONS

The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.

Nonparametric Mixture of Regression Models

Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.