Analysis Of Longitudinal Data Research Articles

The Relationship Between Access to Abortion and Mental Health in Women of Childbearing Age: Analyses of Data From the Global Burden of Disease Studies.

Background It has often been suggested that restricting access to legal abortion could have a negative impact on the mental health of women seeking this procedure. The recent judgment of the United States Supreme Court in the Dobbs case has brought the question of the psychological impactof changes in abortion policy into focus. The results of studies on the link between induced abortion and mental health are contradictory, and interpreting the results of these studies is complex due to a multitude of potential confounding factors. However, there is little data on the relationship between the availability of legal abortion and its effects onthe mental health of women of childbearing age in the general population. Objective The objective of the current study was to examine cross-sectional and longitudinal associations between access to legal abortion and the prevalence of common mental disorders in women of childbearing age at a cross-national level while correcting for potential confounding factors. Methods The current study employs both cross-sectional and longitudinal analyses of nation-level data for 197 countries and regions. As data on global abortion policy were compiled in 2009 and 2017 by the Guttmacher Institute, data on access to legal abortion for these yearswere examined in relation to the prevalence of anxiety disorders and depression in women of childbearing age for each country, obtained from the Global Burden of Disease Studies for the most recent subsequent year (2010 and 2019). The relationship between changes in abortion policy and changes in the prevalence of these disorders in each country was examined for the aforementioned period. All analyses were adjusted for the potential confounding effects of gender development, gender inequality, and intimate partner violence. Cross-sectional associations were examined using Pearson's bivariate and partial correlations, while longitudinal associations were examined using a general linear model and analyses of covariance. Results At the cross-sectional level, broad legal access to abortion was associated with a lower prevalence of depression in women aged 25-49 years, however, this association was not significant after correcting for measures of gender development, gender inequality, and intimate partner violence. At the longitudinal level, a slight but significant decrease in the prevalence of anxiety disorders in women aged 25-49 years was observed in countries where access to legal abortion was broadened in the period 2009-2017. This association remained marginally significant after adjustment for the above confounders. Conclusions The current study suggests that there is a modest relationship between access to legal abortion and its effects on themental health of women aged 25-49 years. However, this relationship appears to be largely indirect in nature and influenced by factors, such as gender development, gender inequality, and intimate partner violence. These results may lead to further exploration of the links between reproductive rights and mental health of women in the general populationand drawattention to the influence of gender inequality and intimate partner violence on mental health of women of childbearing age.

Unravelling the spatial properties of individual mobility patterns using longitudinal travel data

The analysis of longitudinal travel data enables investigating how mobility patterns vary across the population and identify the spatial properties thereof. The objective of this study is to identify the extent to which users explore different parts of the network as well as identify distinctive user groups in terms of the spatial extent of their mobility patterns. To this end, we propose two means for representing spatial mobility profiles and clustering travellers accordingly. We represent users patterns in terms of zonal visiting frequency profiles and grid-cells spatial extent heatmaps. We apply the proposed analysis to a large-scale multi-modal mobility dataset from the public transport system in Stockholm, Sweden. We unravel three clusters - Locals, Commuters and Explorers - that best describe the zonal visiting frequency and show that their composition varies considerably across users’ place of residence. We also identify 15 clusters of visiting spatial extent based on the intensity and direction in which they are oriented. A cross-analysis of the results of the two clustering methods reveals that user segmentation based on exploration patterns and spatial extent are largely independent, indicating that the two different clustering approaches provide fundamentally different insights into the underlying spatial properties of individuals’ mobility patterns. The approach proposed and demonstrated in this study could be applied for any longitudinal individual travel demand data.

The directionality of anxiety and gaming disorder: An exploratory analysis of longitudinal data from an Australian youth population.

Gaming activities among adolescents have increased during the COVID-19 pandemic, bringing with it a growing concern for the potential harms of excessive gaming and its risk factors. Anxiety is frequently linked with gaming disorder, but studies investigating this association were mostly cross-sectional in design. Longitudinal studies that explore risk factors associated with gaming disorder are sparse and the trajectories of gaming disorder remain unclear. To address this paucity, the present study analyzed a large longitudinal dataset with a 12-month follow-up of 4,968 Australian adolescents (ages 13-14) during the pandemic. Logistic regression and multiple regression analyses were conducted to investigate the temporal relationships between anxiety, gaming frequency, the amount of money spent within video games, and gaming disorder. Prevalence rates for gaming disorder in adolescents aged 13 and 14 were 15 and 16%, respectively. The regression models indicated a bidirectional relationship between anxiety and gaming disorder symptoms, where higher levels of anxiety were associated with higher levels of gaming disorder 12 months later and vice versa. The study also found that the interaction between anxiety and higher gaming frequency could predict gaming disorder symptoms. Overall, the findings suggest that young adolescents may be more vulnerable to developing gaming disorder and highlight the importance of addressing the interactions between risk factors and gaming disorder in treatment approaches.

The influence of neighbourhood-level socioeconomic deprivation on developing type 2 diabetes in older men: a longitudinal analysis of the British Regional Heart Study cohort data

BackgroundEvidence from the British longitudinal studies on the influence of neighbourhood-level socioeconomic deprivation in older men on developing type 2 diabetes mellitus is scarce. This study investigates the prospective associations of neighbourhood-level deprivation and individual socioeconomic position with type 2 diabetes mellitus incidence in older men. MethodsThe British Regional Heart Study comprises a cohort of 4252 men aged 60–79 years in 1998–2000 from 24 towns across the UK. Neighbourhood-level deprivation was based on the country specific 2004–05 Index of Multiple Deprivation (IMD) quintiles for participants' postcode of residence at baseline. Individual socioeconomic position was defined as occupational social class based on longest-held occupation at recruitment. Participants with prevalent diabetes were excluded. The incidence of type 2 diabetes mellitus was ascertained from biannual reviews of primary care records over 18 years of follow-up. Cox proportional hazard models were undertaken to examine associations. FindingsThe incidence of type 2 diabetes mellitus cases observed was 368 (9·9%), amongst the 3706 male participants. Age-adjusted rates of type 2 diabetes mellitus increased from IMD quintile 1 (least deprived) to quintile 4 (second most deprived), before decreasing in quintile 5. Compared with quintile 1, the age and individual social class adjusted hazard ratio (aHR) in quintile 4 was 1·52 (95% CI 1·09–2·13), remaining significant after further adjustments for obesity and family history of type 2 diabetes mellitus. Participants in the lowest social class had the highest rates of type 2 diabetes mellitus. The aHR by age and IMD scores for type 2 diabetes mellitus in the lowest social class compared with the highest social class was 2·12 (95% CI 1·15–3·89). InterpretationNeighbourhood-level and individual-level socioeconomic factors remain important predictors of developing type 2 diabetes mellitus in older men, demonstrating that inequalities persist in later life. Inequalities in diabetes and its underlying modifiable risk factors need to be addressed to reduce the burden of this disease in socioeconomically deprived older-age populations. FundingNone.

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort

Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients with idiopathic pulmonary fibrosis using machine learning techniques. We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the imputation performance of conventional and machine learning techniques to impute missing data and then analysed the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent dataset, obtained from the Chicago Consortium. 415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising maps identified four distinct clusters (1-4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%) participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2 comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29-3·40]) and cluster 3 (2·23 years [1·75-3·84]), followed by cluster 2 (4·74 years [3·96-5·73]), and was longest in cluster 4 (5·56 years [5·18-6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified in the replication cohort. Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might optimise evaluation of intervention efficacy during clinical trials and patient management. National Institute for Health and Care Research, Medical Research Council, and GlaxoSmithKline.

Longitudinal modeling of fasting blood sugar with diabetes: A case study of Adama Hospital Medical College, Ethiopia.

The aim of this study was to evaluate the change in fasting blood sugar (FBS) over time and its determinants in diabetic patients. A longitudinal data analysis retrospective-based study was considered with a sample of 312 patients, and the linear mixed effect model was applied. Based on the linear mixed model, the 3-month change in time decreases the average FBSlevel by 0.0111. An increase of one unit of body mass index (BMI) increases the FBS level by 0.0434. Similarly, an increase in blood pressure (DBP) per unit increased the average log FBS level by 0.0005. Secondary and higher education levels lower log FBS levels by 99.41%and 99.45%, respectively, compared with noneducated individuals. The study showed that hypertension history, type of diet, age, status of education, type of drug, body mass index, diastolic blood pressure, and time were statistically significant factors. According to the study, eating a healthy diet, maintaining a healthy body weight, and a low blood sugar level are essential to controlling blood sugar and preventing long-term complications. The government should build an educational institution proportional to the population and open programs to increase awareness about the prevention mechanism of diabetes in communities.

A test of differential susceptibility in behavior trajectories of preterm infants from preschool to adulthood.

Preterm birth predisposes children to internalizing and externalizing behaviors that may persist into adolescence resulting in adult mental health conditions. Social and caregiving contexts, particularly for vulnerable infants born preterm, influence long-term outcomes, but mechanisms are not clearly understood. Healthcare teams caring for those born preterm face difficulty predicting who will be most affected by risk, who will most benefit, and the optimal timing of intervention. Differential susceptibility theory offers an alternative to the traditional risk-only assessments and theories by positing that individuals may be more, or less, susceptible to environmental influences. A sample of preterm- and term-born infants were followed from birth to 23 years of age. Mixed model repeated measures analyses of internalizing and externalizing behaviors were utilized for the comparison groups (N = 214; observations = 1070). Environmental contexts were indexed as proximal protection (low, moderate, high) and medical risk (low, moderate, high). Personal characteristic covariates of sex, race, socioeconomic status, and cognition were modeled. Internalizing behavior trajectories varied significantly over time. Early proximal protective environments conferred a sustained positive influence on behaviors. There is partial support for differential susceptibility theory suggesting that prematurity, as a malleability characteristic enables absorption of both the positive and negative influences of the environment, with greater intensity that those without malleability. The current analyses suggest lasting effects of the preschool age proximal environment on internalizing and externalizing behaviors in young adulthood for those born preterm. Understanding these nuances may aid healthcare professionals in the promotion and timing of interventions to support the child and family.The current manuscript reflects ongoing analyses of longitudinal data. No patient or public contribution to the analyses were required for testing the differential susceptibility theory. The authors would solicit patient or public contribution when implementing practice or policy changes based on the results.

Integrating computational thinking into a longitudinal data analysis course for public health students

After completing an introductory biostatistics course, public health students often need to take one or more follow-on courses focusing on specialized areas of biostatistics. While there exists decades’ worth of pedagogical research on teaching introductory statistics to non-statistics majors, few systematic attempts have been made to explore innovative ways to teaching follow-on courses to non-statistics majors such as public health students. Extending previous research on teaching categorical data analysis to public health students, this paper explores ways to harness the power of computational thinking in teaching conceptual knowledge in a follow-on course on longitudinal data analysis. The proposed approach aims to keep students in their zone of proximal development by using computational experiments as a tool for developing understanding of conceptual knowledge. Learning activities center on experiments that explore the likelihood function. Illustrative examples of actual student work are used to demonstrate a practical way of integrating computational thinking into biostatistics course content.

ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods.

The increasing availability of multivariate data within biomedical research calls for appropriate statistical methods that can describe and model complex relationships between variables. The extended ANOVA simultaneous component analysis (ASCA+) framework combines general linear models and principal component analysis (PCA) to decompose and visualize the separate effects of experimental factors. It has recently been demonstrated how linear mixed models can be included in the framework to analyze data from longitudinal experimental designs with repeated measurements (RM-ASCA+). The ALASCA package for R makes the ASCA+ framework accessible for general use and includes multiple methods for validation and visualization. The package is especially useful for longitudinal data and the ability to easily adjust for covariates is an important strength. This paper demonstrates how the ALASCA package can be applied to gain insights into multivariate data from interventional as well as observational designs. Publicly available data sets from four studies are used to demonstrate the methods available (proteomics, metabolomics, and transcriptomics).

Mental Distress and Use of Stimulants: Analysis of a Longitudinal Cohort of Transgender Women.

Purpose: The purpose of our study was to examine the effects of mental distress (depression, anxiety, and post-traumatic stress disorder [PTSD]), incarceration, and hate crime on stimulant use (methamphetamine, crack, and cocaine) among transgender women. Methods: We conducted a secondary analysis of longitudinal data collected from 2016 to 2018 with 429 transgender women in the San Francisco Bay Area. Generalized estimating equation log-binomial regressions were used to calculate relative risks of stimulant use associated with mental distress, incarceration, and hate crime. Results: At baseline, transgender women experienced transphobic hate crime (46.4%), incarceration (53.0%), mental distress (69.2%), and stimulant use (28.4%). Transgender women who used stimulants reported lower education (45.1%, χ2 = 14.3, p = 0.001) and significantly more had been incarcerated (62.3%, χ2 = 5.9, p = 0.015), and reported diagnoses of depression (67.8%, χ2 = 6.1, p = 0.014), anxiety (62.8%, χ2 = 4.3, p = 0.039), and PTSD (43.8%, χ2 = 6.7, p = 0.010). Longitudinal multivariate analysis found that depression (adjusted relative risk [aRR] = 1.46, 95% confidence interval [CI] 1.09-1.95), anxiety (aRR = 1.42, 95% CI = 1.05-1.93), and PTSD (aRR = 1.38, 95% CI = 1.02-1.87) were associated with methamphetamine use but not with crack or cocaine use. Incarceration was associated with methamphetamine use and crack use, whereas experiencing hate crime was associated with crack use. Conclusions: Mental distress, incarceration, and hate crime were key exposures of stimulant use among transgender women. Intervention targets for reducing stimulant use should consider working upstream by addressing underlying stressors impacting mental health for transgender women, including laws to protect transgender women from hate crime and to reduce their disproportionate representation in the criminal justice system.

Sociodemographic variables associated with risk for diabetic retinopathy

Background:Several systemic and sociodemographic factors have been associated with the development and progression of diabetic retinopathy (DR). However, there is limited investigation of the potential role sociodemographic factors may play in augmenting systemic risk factors of DR. We hypothesize that age, sex, race, ethnicity, income, and insurance payor have an impact on hemoglobin A1c (HbA1c), body mass index, and systolic blood pressure, and therefore an upstream effect on the development of DR and vision-threatening forms of DR (VTDR).Methods:Multivariable analysis of longitudinal electronic health record data at a large academic retina clinic was performed. Sociodemographic factors included race, ethnicity, income, and insurance payor. Systemic risk factors for DR included hemoglobin A1c (HbA1c), systolic blood pressure (SBP), and body mass index (BMI). VTDR was identified from encounter diagnostic codes indicating proliferative retinopathy or diabetic macular edema. Patient-reported primary address zip codes were used to approximate income level, stratified into quartiles.Results:From 2016 to 2018, 3,470 patients with diabetes totaled 11,437 visits were identified. Black patients had higher HbA1c and SBP compared to White patients. White patients had higher BMI and SBP compared to patients of unknown/other race and greater odds of VTDR than the latter. Patients of Hispanic ethnicity had significantly higher SBP than non-Hispanic patients. Low-income patients had higher BMI and SBP than high-income patients and greater odds of VTDR than the latter. Medicaid recipients had greater odds of VTDR than those with Blue Care Network (BCN) and Blue Cross Blue Shield (BCBS) insurance. Medicaid and Medicare recipients had higher SBP compared to BCBS recipients. Finally, both higher HbA1c and SBP had greater odds of VTDR. There were no differences in odds of VTDR between White and Black patients or between Hispanic and non-Hispanic patients.Conclusion:Significant associations exist between certain sociodemographic factors and well-known risk factors for DR. Income and payor were associated with increased severity of systemic risk factors and presence of VTDR. These results warrant further investigation of how risk factor optimization and disease prevention may be further improved by targeted intervention of these modifiable sociodemographic factors.

Are emotions contagious? A conceptual review of studies in language education.

Considering the great role those affective variables play in language learning, it seems wise to hypothesize whether teacher and learner emotions are contagious in the language classroom or not. The existing literature on emotion contagion in other fields of study accepts this hypothesis and reveals significant points about the verbal and non-verbal cues for emotion contagion in class. The present paper introduces and defines the topic and goes on with summarizing the procedure of transmitting the emotion. Then, it reviews the existing research on emotion contagion in different areas and creates a link between them and the L2 studies. It draws attention to the dearth of research on emotion contagion in the second language acquisition (SLA) domain and emphasizes the need for innovative dynamic approaches to research to explore emotion contagion in the ecology of the English as a Foreign Language (EFL) classroom. It also reviews the limited body of research in this regard in the SLA domain and shows how the significant findings can be useful for L2 educators. The findings of these studies show that more relevant studies in the future can be hoped to provide insightful remarks about how different emotions are transmitted between the teacher and students or among students in L2 classes. These studies are supposed to adopt a dynamic approach as well as a longitudinal data collection and analysis procedure. These can have implications for teacher education programs in the English language teaching (ELT) domain.

Diversity and Ecology of Caudoviricetes Phages with Genome Terminal Repeats in Fecal Metagenomes from Four Dutch Cohorts.

The human gut harbors numerous viruses infecting the human host, microbes, and other inhabitants of the gastrointestinal tract. Most of these viruses remain undiscovered, and their influence on human health is unknown. Here, we characterize viral genomes in gut metagenomic data from 1950 individuals from four population and patient cohorts. We focus on a subset of viruses that is highly abundant in the gut, remains largely uncharacterized, and allows confident complete genome identification—phages that belong to the class Caudoviricetes and possess genome terminal repeats. We detect 1899 species-level units belonging to this subset, 19% of which do not have complete representative genomes in major public gut virome databases. These units display diverse genomic features, are predicted to infect a wide range of microbial hosts, and on average account for <1% of metagenomic reads. Analysis of longitudinal data from 338 individuals shows that the composition of this fraction of the virome remained relatively stable over a period of 4 years. We also demonstrate that 54 species-level units are highly prevalent (detected in >5% of individuals in a cohort). Finally, we find 34 associations between highly prevalent phages and human phenotypes, 24 of which can be explained by the relative abundance of potential hosts.

Trends in the full blood count blood test and colorectal cancer detection: a longitudinal, case-control study of UK primary care patient data [version 2; peer review: 2 approved, 1 not approved

The full blood count (FBC) is a common blood test performed in general practice. It consists of many individual parameters that may change over time due to colorectal cancer. Such changes are likely missed in practice. We identified trends in these FBC parameters to facilitate early detection of colorectal cancer. We performed a retrospective, case-control, longitudinal analysis of UK primary care patient data. LOWESS smoothing and mixed effects models were derived to compare trends in each FBC parameter between patients diagnosed and not diagnosed over a prior 10-year period. There were 399,405 males (2.3%, n = 9,255 diagnosed) and 540,544 females (1.5%, n = 8,153 diagnosed) in the study. There was no difference between cases and controls in FBC trends between 10 and four years before diagnosis. Within four years of diagnosis, trends in many FBC levels statistically significantly differed between cases and controls, including red blood cell count, haemoglobin, white blood cell count, and platelets (interaction between time and colorectal cancer presence: p <0.05). FBC trends were similar between Duke's Stage A and D colorectal tumours, but started around one year earlier in Stage D diagnoses. Trends in FBC parameters are different between patients with and without colorectal cancer for up to four years prior to diagnosis. Such trends could help earlier identification.

Longitudinal effects of school policies on children's eating behaviors and weight status: findings from the childhood obesity study in China megacities.

To examine longitudinal effects of school policies on children's weight status and eating behaviors, and study how these effects may vary by children's age, sex, and baseline weight status in China. Data were collected in 2015, 2016, and 2017 in an open cohort of 3298 children aged 6-17, their parents, and schools in five large cities across China (Beijing, Shanghai, Xi'an, Nanjing, and Chengdu). Children's weight, height, and waist circumference were measured, and their eating behaviors were self-reported. The 1691 children with such repeated measures in ≥ two waves were included in longitudinal data analysis with mixed effects models, testing the associations. Having school vicinity food stall policy was associated with less frequent consumption of fast food (β = -0.14, p < 0.01) and snack (β = -0.84, p < 0.01). More significant associations were found between school policies and unhealthy eating behaviors for girls and children aged 6-11 than their counterparts. Among children without overweight or central obesity at baseline, having school cafeteria food policy was associated with lower risks for overweight and obesity (OR = 0.33, 95% CI: 0.17-0.63) and central obesity (OR = 0.47, 95% CI: 0.26-0.85). This existed for girls, but not for boys. School vicinity food stall policy was associated with lower BMI (β = -0.20, 95% CI: -0.37, -0.03) among all children and in girls (β = -0.28, 95% CI: -0.50, -0.05) without overweight at baseline. School policies could reduce children's unhealthy eating behaviors and obesity risk in megacities in China. Children's age, sex, and baseline weight status modify the effects. School policies are important to fight the growing childhood obesity epidemic.

Do Appetite Traits Mediate the Link Between Birth Weight and Later Child Weight in Low-Income Hispanic Families?

Background: Birth weight and appetite traits (ATs) are important early life determinants of child weight and obesity. Objectives: The aim of this study is to examine whether (1) birth weight-for-gestational age z-scores (BWGAzs) were associated with ATs at child age 2 years and (2) ATs mediated the link between BWGAzs and weight-for-age z-scores (WFAzs) at child ages 3 and 4 years among Hispanic children. Methods: We conducted a secondary longitudinal analysis of data from the Starting Early Program of low-income, Hispanic mother-child pairs. ATs were assessed using the Child Eating Behavior Questionnaire at age 2 years. Child birth weight was obtained from medical records. Birth weight, sex, and gestational age were used to generate BWGAzs with Fenton growth curves. WFAz was calculated based on the CDC 2000 growth charts. Regression and mediation analyses were used to explore associations between BWGAzs, ATs, and WFAzs. Results: Infants with higher BWGAzs had significantly lower Satiety Responsiveness (B = -0.10) and Food Fussiness (B = -0.13) scores at age 2 years and higher WFAzs at ages 3 (B = 0.44) and 4 (B = 0.34) years. Lower Satiety Responsiveness at age 2 years was associated with higher WFAzs at ages 3 (B = -0.11) and 4 (B = -0.34; all p < 0.01) years. Lower Satiety Responsiveness partially mediated the positive relationship between birth weight and child WFAzs at ages 3 and 4 years. Conclusions: Children with higher birth weight and lower Satiety Responsiveness scores may be at higher risk of developing obesity in childhood. Further research is needed to understand the mechanisms through which birth weight influences child appetite. Clinical Trial Registration: ClinicalTrials.gov: NCT01541761.

Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study

Numerous methods have been developed for longitudinal binomial data in the literature. These traditional methods are reasonable for longitudinal binomial data with a negative association between the number of successes and the number of failures over time; however, a positive association may occur between the number of successes and the number of failures over time in some behaviour, economic, disease aggregation and toxicological studies as the numbers of trials are often random. In this paper, we propose a joint Poisson mixed modelling approach to longitudinal binomial data with a positive association between longitudinal counts of successes and longitudinal counts of failures. This approach can accommodate both a random and zero number of trials. It can also accommodate overdispersion and zero inflation in the number of successes and the number of failures. An optimal estimation method for our model has been developed using the orthodox best linear unbiased predictors. Our approach not only provides robust inference against misspecified random effects distributions, but also consolidates the subject-specific and population-averaged inferences. The usefulness of our approach is illustrated with an analysis of quarterly bivariate count data of stock daily limit-ups and limit-downs.

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis–Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Malaria outbreak facilitated by increased mosquito breeding sites near houses and cessation of indoor residual spraying, Kole district, Uganda, January-June 2019

BackgroundIn June 2019, surveillance data from the Uganda’s District Health Information System revealed an outbreak of malaria in Kole District. Analysis revealed that cases had exceeded the outbreak threshold from January 2019. The Ministry of Health deployed our team to investigate the areas and people affected, identify risk factors for disease transmission, and recommend control and prevention measures.MethodsWe conducted an outbreak investigation involving a matched case-control study. We defined a confirmed case as a positive malaria test in a resident of Aboke, Akalo, Alito, and Bala sub-counties of Kole District January–June 2019. We identified cases by reviewing outpatient health records. Exposures were assessed in a 1:1 matched case-control study (n = 282) in Aboke sub-county. We selected cases systematically from 10 villages using probability proportionate to size and identified age- and village-matched controls. We conducted entomological and environmental assessments to identify mosquito breeding sites. We plotted epidemic curves and overlaid rainfall, and indoor residual spraying (IRS). Case-control exposures were combined into: breeding site near house, proximity to swamp and breeding site, and proximity to swamp; these were compared to no exposure in a logistic regression analysis.ResultsOf 18,737 confirmed case-patients (AR = 68/1,000), Aboke sub-county residents (AR = 180/1,000), children < 5 years (AR = 94/1,000), and females (AR = 90/1,000) were most affected. Longitudinal analysis of surveillance data showed decline in cases after an IRS campaign in 2017 but an increase after IRS cessation in 2018–2019. Overlay of rainfall and case data showed two malaria upsurges during 2019, occurring 35–42 days after rainfall increases. Among 141 case-patients and 141 controls, the combination of having mosquito breeding sites near the house and proximity to swamps increased the odds of malaria 6-fold (OR = 6.6, 95% CI = 2.24–19.7) compared to no exposures. Among 84 abandoned containers found near case-patients’ and controls’ houses, 14 (17%) had mosquito larvae. Adult Anopheles mosquitoes, larvae, pupae, and pupal exuviae were identified near affected houses.ConclusionStagnant water formed by increased rainfall likely provided increased breeding sites that drove this outbreak. Cessation of IRS preceded the malaria upsurges. We recommend re-introduction of IRS and removal of mosquito breeding sites in Kole District.