Sensitivity and specificity of information criteria

High-Density Lipoprotein Anti-Inflammatory Capacity and Incident Cardiovascular Events

BACKGROUND

The role of high-density lipoprotein (HDL) function in cardiovascular disease represents an important emerging concept. The present study investigated whether HDL anti-inflammatory capacity is prospectively associated with first cardiovascular events in the general population.

METHODS

HDL anti-inflammatory capacity was determined as its ability to suppress TNFα (tumor necrosis factor α)-induced VCAM-1 (vascular cell adhesion molecule-1) mRNA expression in endothelial cells in vitro (results expressed as achieved percent reduction by individual HDL related to the maximum TNFα effect with no HDL present). In a nested case-control design of the PREVEND (Prevention of Renal and Vascular End Stage Disease) study, 369 cases experiencing a first cardiovascular event (combined end point of death from cardiovascular causes, ischemic heart disease, nonfatal myocardial infarction, and coronary revascularization) during a median of 10.5 years of follow-up were identified and individually matched to 369 controls with respect to age, sex, smoking status, and HDL cholesterol. Baseline samples were available in 340 cases and 340 matched controls.

RESULTS

HDL anti-inflammatory capacity was not correlated with HDL cholesterol or hsCRP (high-sensitivity C-reactive protein). HDL anti-inflammatory capacity was significantly lower in cases compared with controls (31.6% [15.7-44.2] versus 27.0% [7.4-36.1]; P<0.001) and was inversely associated with incident CVD in a fully adjusted model (odds ratio [OR] per 1 SD, 0.74 [CI, 0.61-0.90]; P=0.002). Furthermore, this association was approximately similar with all individual components of the cardiovascular disease end point. The HDL anti-inflammatory was not correlated with cholesterol efflux capacity (r=-0.02; P>0.05). When combining these 2 HDL function metrics in 1 model, both were significantly and independently associated with incident cardiovascular disease in a fully adjusted model (efflux: OR per 1 SD, 0.74; P=0.002; anti-inflammatory capacity: OR per 1 SD, 0.66; P<0.001). Adding HDL anti-inflammatory capacity improved risk prediction by the Framingham risk score, with a model likelihood-ratio statistic increase from 10.50 to 20.40 (P=0.002).

CONCLUSIONS

The HDL anti-inflammatory capacity, reflecting vascular protection against key steps in atherogenesis, was inversely associated with incident cardiovascular events in a general population cohort, independent of HDL cholesterol and HDL cholesterol efflux capacity. Adding HDL anti-inflammatory capacity to the Framingham risk score improves risk prediction.

Derivation and Validation of a Total Fruit and Vegetable Intake Prediction Model to Identify Targets for Biomarker Discovery Using the UK National Diet and Nutrition Survey

BACKGROUND

Dietary assessments in research and clinical settings are largely reliant on self-reported questionnaires. It is acknowledged that these are subject to measurement error and biases and that objective approaches would be beneficial. Dietary biomarkers have been purported as a complementary approach to improve the accuracy of dietary assessments. Tentative biomarkers have been identified for many individual fruits and vegetables (FVs), but an objective total FV intake assessment tool has not been established.

OBJECTIVES

To derive and validate a prediction model of total FV intake (TFVpred) to inform future biomarker studies.

METHODS

Data from the National Diet and Nutrition Survey (NDNS) were used for this analysis. A modeling group (MG) consisting of participants aged >11 years from the NDNS years 5-6 was created (n = 1746). Intake data for 96 FVs were analyzed by stepwise regression to derive a model that satisfied 3 selection criteria: SEE ≤80, R2 >0.7, and ≤10 predictors. The TFVpred model was validated using comparative data from a validation group (VG) created from the NDNS years 7-8 (n = 1865). Pearson's correlation coefficients were assessed between observed and predicted values in the MG and VG. Bland-Altman plots were used to assess agreement between TFVpred estimates and total FV intake.

RESULTS

A TFVpred model, comprised of tomatoes, apples, carrots, bananas, pears, strawberries, and onions, satisfied the selection criteria (R2 = 0.761; SEE = 78.81). Observed and predicted total FV intake values were positively correlated in the MG (r = 0.872; P < 0.001; R2 = 0.761) and the VG (r = 0.838; P < 0.001; R2 = 0.702). In the MG and VG, 95.0% and 94.9%, respectively, of TFVpred model residuals were within the limits of agreement.

CONCLUSIONS

Intakes of a concise FV list can be used to predict total FV intakes in a UK population. The individual FVs included in the TFVpred model present targets for biomarker discovery aimed at objectively assessing total FV intake.

Bifactor model of cognition in schizophrenia: Evidence for general and specific abilities

BACKGROUND

Despite extensive study of cognition in schizophrenia, it remains unclear as to whether cognitive deficits and their latent structure are best characterized as reflecting a generalized deficit, specific deficits, or some combination of general and specific constructs.

METHOD

To clarify latent structure of cognitive abilities, confirmatory factor analysis was used to examine the latent structure of cognitive data collected for the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for Schizophrenia study. Baseline assessment data (n = 813) were randomly divided into calibration (n = 413) and cross-validation samples (n = 400). To examine whether generalized or specific deficit models provided better explanation of the data, we estimated first-order, hierarchical, and bifactor models.

RESULTS

A bifactor model with seven specific factors and one general factor provided the best fit to the data for both the calibration and cross-validation samples.

CONCLUSIONS

These findings lend support for a replicable bifactor model of cognition in schizophrenia, characterized by both a general cognitive factor and specific domains. This suggests that cognitive deficits in schizophrenia might be best understood by separate general and specific contributions.

Association of pre- and early-pregnancy factors with the risk for gestational diabetes mellitus in a large Chinese population

Gestational diabetes mellitus (GDM) has aroused wide public concern, as it affects approximately 1.8-25.1% of pregnancies worldwide. This study aimed to examine the association of pre-pregnancy demographic parameters and early-pregnancy laboratory biomarkers with later GDM risk, and further to establish a nomogram prediction model. This study is based on the big obstetric data from 10 "AAA" hospitals in Xiamen. GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria. Data are analyzed using Stata (v14.1) and R (v3.5.2). Total 187,432 gestational women free of pre-pregnancy diabetes mellitus were eligible for analysis, including 49,611 women with GDM and 137,821 women without GDM. Irrespective of confounding adjustment, eight independent factors were consistently and significantly associated with GDM, including pre-pregnancy body mass index (BMI), pre-pregnancy intake of folic acid, white cell count, platelet count, alanine transaminase, albumin, direct bilirubin, and creatinine (p < 0.001). Notably, per 3 kg/m² increment in pre-pregnancy BMI was associated with 22% increased risk [adjusted odds ratio (OR) 1.22, 95% confidence interval (CI) 1.21-1.24, p < 0.001], and pre-pregnancy intake of folic acid can reduce GDM risk by 27% (adjusted OR 0.73, 95% CI 0.69-0.79, p < 0.001). The eight significant factors exhibited decent prediction performance as reflected by calibration and discrimination statistics and decision curve analysis. To enhance clinical application, a nomogram model was established by incorporating age and above eight factors, and importantly this model had a prediction accuracy of 87%. Taken together, eight independent pre-/early-pregnancy predictors were identified in significant association with later GDM risk, and importantly a nomogram modeling these predictors has over 85% accuracy in early detecting pregnant women who will progress to GDM later.

Individual motion perception parameters and motion sickness frequency sensitivity in fore-aft motion

Previous literature suggests a relationship between individual characteristics of motion perception and the peak frequency of motion sickness sensitivity. Here, we used well-established paradigms to relate motion perception and motion sickness on an individual level. We recruited 23 participants to complete a two-part experiment. In the first part, we determined individual velocity storage time constants from perceived rotation in response to Earth Vertical Axis Rotation (EVAR) and subjective vertical time constants from perceived tilt in response to centrifugation. The cross-over frequency for resolution of the gravito-inertial ambiguity was derived from our data using the Multi Sensory Observer Model (MSOM). In the second part of the experiment, we determined individual motion sickness frequency responses. Participants were exposed to 30-minute sinusoidal fore-aft motions at frequencies of 0.15, 0.2, 0.3, 0.4 and 0.5 Hz, with a peak amplitude of 2 m/s² in five separate sessions, approximately 1 week apart. Sickness responses were recorded using both the MIsery SCale (MISC) with 30 s intervals, and the Motion Sickness Assessment Questionnaire (MSAQ) at the end of the motion exposure. The average velocity storage and subjective vertical time constants were 17.2 s (STD = 6.8 s) and 9.2 s (STD = 7.17 s). The average cross-over frequency was 0.21 Hz (STD = 0.10 Hz). At the group level, there was no significant effect of frequency on motion sickness. However, considerable individual variability was observed in frequency sensitivities, with some participants being particularly sensitive to the lowest frequencies, whereas others were most sensitive to intermediate or higher frequencies. The frequency of peak sensitivity did not correlate with the velocity storage time constant (r = 0.32, p = 0.26) or the subjective vertical time constant (r = − 0.37, p = 0.29). Our prediction of a significant correlation between cross-over frequency and frequency sensitivity was not confirmed (r = 0.26, p = 0.44). However, we did observe a strong positive correlation between the subjective vertical time constant and general motion sickness sensitivity (r = 0.74, p = 0.0006). We conclude that frequency sensitivity is best considered a property unique to the individual. This has important consequences for existing models of motion sickness, which were fitted to group averaged sensitivities. The correlation between the subjective vertical time constant and motion sickness sensitivity supports the importance of verticality perception during exposure to translational sickness stimuli.

Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding

Resolving the relationships between the major lineages in the animal tree of life is necessary to understand the origin and evolution of key animal traits. Sponges, characterized by their simple body plan, were traditionally considered the sister group of all other animal lineages, implying a gradual increase in animal complexity from unicellularity to complex multicellularity. However, the availability of genomic data has sparked tremendous controversy as some phylogenomic studies support comb jellies taking this position, requiring secondary loss or independent origins of complex traits. Here we show that incorporating site-heterogeneous mixture models and recoding into partitioned phylogenomics alleviates systematic errors that hamper commonly-applied phylogenetic models. Testing on real datasets, we show a great improvement in model-fit that attenuates branching artefacts induced by systematic error. We reanalyse key datasets and show that partitioned phylogenomics does not support comb jellies as sister to other animals at either the supermatrix or partition-specific level.

Exploring the Trade-Off Between Economic and Health Outcomes During a Pandemic: A Discrete Choice Experiment of Lockdown Policies in Australia

Background

All countries experienced social and economic disruption and threats to health security from the COVID-19 pandemic in 2020, but the responses in terms of control measures varied considerably. While control measures, such as quarantine, lockdown and social distancing, reduce infections and infection-related deaths, they have severe negative economic and social consequences.

Objectives

The objective of this study was to explore the acceptability of different infectious disease control measures, and examine how respondents trade off between economic and health outcomes.

Methods

A discrete choice experiment was developed, with attributes covering: control restrictions, duration of restrictions, tracking, number of infections and of deaths, unemployment, government expenditure and additional personal tax. A representative sample of Australians (n = 1046) completed the survey, which included eight choice tasks. Data were analysed using mixed logit regression to identify heterogeneity and latent class models to examine heterogeneity.

Results

In general, respondents had strong preferences for policies that avoided high infection-related deaths, although lower unemployment and government expenditure were also considered important. Respondents preferred a shorter duration for restrictions, but their preferences did not vary significantly for the differing levels of control measures. In terms of tracking, respondents preferred mobile phone tracking or bracelets when compared to no tracking. Significant differences in preferences was identified, with two distinct classes: Class 1 (57%) preferred the economy to remain open with some control measures, whereas Class 2 (43%), had stronger preferences for policies that reduced avoidable deaths.

Conclusions

This study found that the Australian population is willing to relinquish some freedom, in the short term, and trade off the negative social and economic impacts of the pandemic, to avoid the negative health consequences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40271-021-00503-5.

A novel model to label delirium in an intensive care unit from clinician actions

BACKGROUND

In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance.

METHODS

EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation.

RESULTS

Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes.

CONCLUSIONS

Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.

Life expectancy, family constellation and stress in giant mole-rats (Fukomys mechowii)

Giant mole-rats (Fukomys mechowii) are remarkably long-lived subterranean rodents (maximum recorded lifespan as reported here greater than 26 years) that live in families with one reproductive pair (breeders) and their non-reproductive offspring (non-breeders). Previous studies have shown that breeders live on average approximately twice as long as non-breeders, a finding contradicting the classic trade-off between reproduction and lifespan. Because recent evidence points to the hypothalamic-pituitary-adrenal axis as playing an important role in shaping the pace of ageing in mole-rats, we analysed the influence of the social environment of giant mole-rats on intrafamilial aggression levels, indicators of long-term stress, and, ultimately, mortality. Behavioural data indicated that family constellation, especially the presence or the absence of parents, influences agonistic behaviour. As a measure of long-term stress, we established a non-invasive method of extracting and measuring cortisol from hair of giant mole-rats. Interestingly, orphaned non-breeders exhibited significantly lower levels of cortisol and lower mortality rates than did non-breeders living with both parents. Because hypercortisolism is harmful in the long-term, intrafamilial stress could help explain the earlier onset of senescence in non-breeders, resulting in a shorter lifespan. Our findings suggest that the social environment should be considered as a further factor in ageing studies involving group-living animals. This article is part of the theme issue 'Ageing and sociality: why, when and how does sociality change ageing patterns?'

Public Preferences for Allocating Ventilators in an Intensive Care Unit: A Discrete Choice Experiment

Background and Objective

During the COVID-19 pandemic, resources in intensive care units (ICUs) have the potential to be inadequate to treat all those who might benefit. Therefore, it is paramount to identify the views of the community regarding how to allocate such resources. This study aims to quantify Australian community preferences for ventilation allocation.

Methods

A discrete choice experiment was designed and administrated to an adult Australian online panel. Each survey respondent answered 12 choice sets from a total design of 120. Each choice set placed the respondent in the role of hypothetical decision maker, prioritising care between two patients. Conditional logit, mixed logit regression and latent class analysis were used to analyse the data. Additionally, we asked a series of attitudinal questions about different methods of making such decisions in practice, focusing on who should be responsible.

Results

A total of 1050 community members completed the survey and responded to each choice. Dimensions considered most important were age, likely effectiveness, smoking status, whether the person has dependents, whether they are a healthcare worker, and whether they have a disability or not. Estimating marginal rates of substitution between patient characteristics and chance of survival if ventilated yielded values of up to 30 percentage points if the patient was 70 years old relative to being 30. However, respondents typically said they would prefer such decisions to be made by medical professionals.

Conclusion

This study demonstrated the preferences of the community to allocation of ventilators during the COVID-19 pandemic. The use of such information should be treated with some caution as the underlying reason for such preferences are unclear, and respondents themselves preferred the decision to be made by others.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40271-021-00498-z.

Can you be a follower even when you do not follow the leader? Yes, you can

In the ongoing debate in the area of critical leadership studies, the nature of leader–follower relationships is a thorny issue. The nature of followership has been questioned, especially whether followers can display resistance behaviours while maintaining their follower position. Addressing this issue requires a dialectical approach in which followers and leaders alike are primary elements in leadership co-production. Followers who face destructive leaders are of special interest when leadership is studied as a co-creational process. This context favours the emergence of a full range of behavioural profiles in which passives and colluders will illustrate the destructive leadership co-production process, and those who resist demonstrate that followers may not follow the leader and still keep a followership purpose. A two-step data analysis procedure was conducted based on the behaviour descriptions of 123 followers having a destructive leader. A qualitative analysis (i.e. content analysis) showed a set of behaviours and their antecedents that suggest three main groups of followers: resisters, obedient and mixed behaviour. Treating these data quantitatively (i.e. latent class analysis), six followers’ profiles emerged: active resistance, passive resistance, passive obedience, conflict avoidance, support and mixed. Our findings provide evidence that followers who resist may do it for the sake of the organisation. We discuss our findings in light of followership theory, whereby joining role-based and constructionist approaches allows us to argue that followers may still be followers even when they do not invariably follow their leader.

A differentiated Ca2+ signalling phenotype has minimal impact on myocardin expression in an automated differentiation assay using A7r5 cells

Vascular smooth muscle cells are unusual in that differentiated, contractile cells possess the capacity to "de-differentiate" into a synthetic phenotype that is characterized by being replicative, secretory, and migratory. One aspect of this phenotypic modulation is a shift from voltage-gated Ca²⁺ signalling in electrically coupled, differentiated cells to increased dependence on store-operated Ca²⁺ entry and sarcoplasmic reticulum Ca²⁺ release in synthetic cells. Conversely, an increased voltage-gated Ca²⁺ entry is seen when proliferating A7r5 smooth muscle cells quiesce. We asked whether this change in Ca²⁺ signalling was linked to changes in the expression of the phenotype-regulating transcriptional co-activator myocardin or α-smooth muscle actin, using correlative epifluorescence Ca²⁺ imaging and immunocytochemistry. Cells were cultured in growth media (DMEM, 10% serum, 25 mM glucose) or differentiation media (DMEM, 1% serum, 5 mM glucose). Coinciding with growth arrest, A7r5 cells became electrically coupled, and spontaneous Ca²⁺ signalling showed increasing dependence on L-type voltage-gated Ca²⁺ channels that were blocked with nifedipine (5 μM). These synchronized oscillations were modulated by ryanodine receptors, based on their sensitivity to dantrolene (5 μM). Actively growing cultures had spontaneous Ca²⁺ transients that were insensitive to nifedipine and dantrolene but were blocked by inhibition of the sarco-endoplasmic reticulum ATPase with cyclopiazonic acid (10 μM). In cells treated with differentiation media, myocardin and αSMA immunoreactivity increased prior to changes in the Ca²⁺ signalling phenotype, while chronic inhibition of voltage-gated Ca²⁺ entry modestly increased immunoreactivity of myocardin. Stepwise regression analyses suggested that changes in myocardin expression had a weak relationship with Ca²⁺ signalling synchronicity, but not frequency or amplitude. In conclusion, we report a 96-well assay and analytical pipeline to study the link between Ca²⁺ signalling and smooth muscle differentiation. This assay showed that changes in the expression of two molecular differentiation markers (myocardin and αSMA) tended to precede changes in the Ca²⁺ signalling phenotype.

Tense Marking in the Kindergarten Population: Testing the Bimodal Distribution Hypothesis

Purpose The purpose of this study was to explore whether evidence for a bimodal distribution of tense marking, previously documented in clinically referred samples, exists in a population-based sample of kindergarten children from a rural county in Tennessee. Method A measure of tense marking, the Test of Early Grammatical Impairment (TEGI) Screening Test, was individually administered to consented kindergarten students (N = 153) across three elementary schools in a single school district. The consented children constituted 73% of kindergartners in the district. Cluster analysis was used to evaluate the number and composition of latent classes that best fit the distribution of the TEGI Screening Test scores. Results Analysis of the scores revealed a distribution that deviated significantly from normality. Cluster analyses (Ward's, k-means, single linkage) revealed a two-cluster solution as the best fitting model. The very large effect-size difference in mean TEGI Screening Test score between the two clusters (d = 4.77) provides validation of an identifiable boundary delineating typical from atypical tense marking in this sample of kindergartners. The difference in tense marking across the two clusters was not attributable to child chronological age. The percentage of the sample comprising the low-performing cluster aligns with specific language impairment and developmental language disorder prevalence estimates. Conclusion Additional demonstrations of a bimodal distribution of tense marking in future studies with carefully defined samples could strengthen the clinical marker evidence and utility of this linguistic feature.

Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.

Early-Life Circumstances, Health Behavior Profiles, and Later-Life Health in Great Britain

Objectives: Drawing on UK Household Longitudinal Study data, this study assessed a pathway from early-life disadvantage to suboptimal later-life health via health behavior. Methods: Latent class analysis was used to identify distinct smoking, nutrition, alcohol, and physical activity health behavior profiles. Mediation analyses were performed to assess indirect effects of early-life disadvantage via health behavior on allostatic load, an objective measure of physiological wear and tear. Results: Four health behavior profiles were identified: (1) broadly healthy and high alcohol consumption, (2) low smoking and alcohol consumption, healthy nutrition, and physically inactive, (3) broadly unhealthy and low alcohol consumption, and (4) broadly moderately unhealthy and high alcohol consumption. Having grown up in a higher socioeconomic position family was associated with lower later-life allostatic load. This was partly attributable to health behavioral differences. Discussion: Growing up under disadvantageous socioeconomic circumstances may initiate a chain of risk by predisposing people to health behavior profiles associated with poorer later-life health.

The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran

Background

The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease.

Methods

Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R² was used to select the best model in GAM/GAMM.

Results

During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)₁₂ model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF.

Conclusions

Meteorological variables can affect CCHF occurrence.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Latent class analysis to identify clinical profiles among indigenous infants with bronchiolitis

OBJECTIVE

Better phenotyping of the heterogenous bronchiolitis syndrome may lead to targeted future interventions. This study aims to identify severe bronchiolitis profiles among hospitalized Australian Indigenous infants, a population at risk of bronchiectasis, using latent class analysis (LCA).

METHODS

We included prospectively collected clinical, viral, and nasopharyngeal bacteria data from 164 Indigenous infants hospitalized with bronchiolitis from our previous studies. We undertook multiple correspondence analysis (MCA) followed by LCA. The best-fitting model for LCA was based on adjusted Bayesian information criteria and entropy R² .

RESULTS

We identified five clinical profiles. Profile-A's (23.8% of cohort) phenotype was previous preterm (90.7%), low birth-weight (89.2%) and weight-for-length z-score <-1 (82.7% from combining those with z-score between -1 and -2 and those in the z-score of <-2 group) previous respiratory hospitalization (39.6%) and bronchiectasis on chest high-resolution computed tomography scan (35.4%). Profile-B (25.3%) was characterized by the oxygen requirement (100%) and marked accessory muscle use (45.5%). Infants in profile-C (7.0%) had the most severe disease, with oxygen requirement and bronchiectasis in 100%, moderate accessory muscle use (85% vs 0%-51.4%) and bacteria detected (93.1% vs 56.7%-72.0%). Profile-D (11.6%) was dominated by rhinovirus (49.4%), mild accessory muscle use (73.8%), and weight-for-length z-score <-2 (36.0%). Profile-E (32.2%) included bronchiectasis (13.8%), RSV (44.0%), rhinovirus (26.3%) and any bacteria (72%).

CONCLUSION

Using LCA in Indigenous infants with severe bronchiolitis, we identified five clinical profiles with one distinct profile for bronchiectasis. LCA can characterize distinct phenotypes for severe bronchiolitis and infants at risk for future bronchiectasis, which may inform future targeted interventions.

Ebola Virus Dose Response Model for Aerosolized Exposures: Insights from Primate Data

This study develops dose-response models for Ebolavirus using previously published data sets from the open literature. Two such articles were identified in which three different species of nonhuman primates were challenged by aerosolized Ebolavirus in order to study pathology and clinical disease progression. Dose groups were combined and pooled across each study in order to facilitate modeling. The endpoint of each experiment was death. The exponential and exact beta-Poisson models were fit to the data using maximum likelihood estimation. The exact beta-Poisson was deemed the recommended model because it more closely approximated the probability of response at low doses though both models provided a good fit. Although transmission is generally considered to be dominated by person-to-person contact, aerosolization is a possible route of exposure. If possible, this route of exposure could be particularly concerning for persons in occupational roles managing contaminated liquid wastes from patients being treated for Ebola infection and the wastewater community responsible for disinfection. Therefore, this study produces a necessary mathematical relationship between exposure dose and risk of death for the inhalation route of exposure that can support quantitative microbial risk assessment aimed at informing risk mitigation strategies including personal protection policies against occupational exposures.

Consumer Reactions to E. Coli and Antibiotic Residue Recalls: Utility Maximization vs. Regret Minimization

Food safety remains a major issue to many consumers. Previous studies examining the economic impact of food safety recalls have focused on Class I recalls. Antibiotic residue in meat products, a Class II recall, has increased in consumer importance yet little is known about how much research and development expenditure should be allocated to reduce antibiotic residue pre- and post-harvest. This study compares demand elasticities and the decrease in willingness to pay in response to either an E. coli (Class I) or antibiotic residue (Class II) recall. We compare and contrast two competing behavioral frameworks, Random Utility and Regret Minimizing. Modeling behavior using the random regret framework is found to be more powerful for assessing consumer responses. In addition, we explore if different groups of consumers exist that either maximize utility or minimize regret. Consumer devaluations of E. coli (Class I) are 40-65% larger than antibiotic residue (Class II). Approximately 60% of consumers are identified as regret minimizers and 40% were identified as utility maximizers. While industry response and government policy recommendations differed conditional on modeling framework, the regret minimizing framework required smaller price discounts than regret minimizing to maintain the same level of market share.

mPartition: A Model-Based Method for Partitioning Alignments

Maximum likelihood (ML) analysis of nucleotide or amino-acid alignments is widely used to infer evolutionary relationships among species. Computing the likelihood of a phylogenetic tree from such alignments is a complicated task because the evolutionary processes typically vary across sites. A number of studies have shown that partitioning alignments into sub-alignments of sites, where each sub-alignment is analyzed using a different model of evolution (e.g., GTR + I + G), is a sensible strategy. Current partitioning methods group sites into subsets based on the inferred rates of evolution at the sites. However, these do not provide sufficient information to adequately reflect the substitution processes of characters at the sites. Moreover, the site rate-based methods group all invariant sites into one subset, potentially resulting in wrong phylogenetic trees. In this study, we propose a partitioning method, called mPartition, that combines not only the evolutionary rates but also substitution models at sites to partition alignments. Analyses of different partitioning methods on both real and simulated datasets showed that mPartition was better than the other partitioning methods tested. Notably, mPartition overcame the pitfall of grouping all invariant sites into one subset. Using mPartition may lead to increased accuracy of ML-based phylogenetic inference, especially for multiple loci or whole genome datasets.

A structural equation model of factors associated with HIV risk behaviors and mental health among men who have sex with men in Malawi

BACKGROUND

Men who have sex with men (MSM) bear a disproportionate burden of HIV in Malawi. Early prevention efforts in Malawi have been largely focused on preventing heterosexual and vertical transmission of HIV, and MSM have rarely been the specific benefactors of these efforts, despite facing both higher prevalence of HIV coupled with multiple barriers to prevention and care. To better facilitate the design of culturally relevant HIV prevention programs and prioritize resources among MSM in resource limited settings, the objective of this analysis was to estimate the relationship between social factors and HIV related risk behaviors and mental health.

METHODS

338 MSM were recruited using respondent-driven sampling in Blantyre, Malawi from April 2011 to March 2012. Structural equation models were built to test the association between six latent factors: participation in social activities, social support, stigma and human rights violations, depression symptomatology, condom use, and sexual risk behaviors, including concurrent sexual partnerships and total number of partners.

RESULTS

The mean age of participants was 25 years old. Almost 50% (158/338) of the participants were unemployed and 11% (37/338) were married or cohabiting with women. More than 30% (120/338) of the participants reported sexual behavior stigma and 30% (102/338) reported depression symptomatology. Almost 50% (153/338) of the participants reported any kind of HIV-related risk behaviors and 30% (110/338) participated in one of the recorded social activities. Significant associations were identified between stigma and risk behaviors (β = 0.14, p = 0.03); stigma and depression symptomatology (β = 0.62, p = 0.01); participation in social activities and depression symptomatology (β = 0.17, p = 0.01).

CONCLUSION

Results suggest MSM reporting stigma are more likely to report sexual risk practices associated with HIV/STI transmission and depressive symptoms, while those reporting participation in social activities related to HIV education are less likely to be depressed. Furthermore, interventions at the community level to support group empowerment and engagement may further reduce risks of HIV transmission and improve mental health outcomes. Taken together, these results suggest the potential additive benefits of mental health services integrated within comprehensive HIV prevention packages to optimize both HIV-related outcomes and general quality of life among MSM in Malawi.

Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics

BACKGROUND

Single-cell RNA sequencing is a powerful tool for characterizing cellular heterogeneity in gene expression. However, high variability and a large number of zero counts present challenges for analysis and interpretation. There is substantial controversy over the origins and proper treatment of zeros and no consensus on whether zero-inflated count distributions are necessary or even useful. While some studies assume the existence of zero inflation due to technical artifacts and attempt to impute the missing information, other recent studies argue that there is no zero inflation in scRNA-seq data.

RESULTS

We apply a Bayesian model selection approach to unambiguously demonstrate zero inflation in multiple biologically realistic scRNA-seq datasets. We show that the primary causes of zero inflation are not technical but rather biological in nature. We also demonstrate that parameter estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zero inflation.

CONCLUSIONS

Despite the existence of zero inflation in scRNA-seq counts, we recommend the generalized linear model with negative binomial count distribution, not zero-inflated, as a suitable reference model for scRNA-seq analysis.

Prevalence and patterns of multimorbidity in the Jamaican population: A comparative analysis of latent variable models

Background

Evidence suggests that the single-disease paradigm does not accurately reflect the individual experience, with increasing prevalence of chronic disease multimorbidity, and subtle yet important differences in types of co-occurring diseases. Knowledge of multimorbidity patterns can aid clarification of individual-level burden and needs, to inform prevention and treatment strategies. This study aimed to estimate the prevalence of multimorbidity in Jamaica, identify population subgroups with similar and distinct disease profiles, and examine consistency in patterns identified across statistical techniques.

Methods

Latent class analysis (LCA) was used to examine multimorbidity patterns in a sample of 2,551 respondents aged 15–74 years, based on data from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008 and self-reported presence/absence of 11 chronic conditions. Secondary analyses compared results with patterns identified using exploratory factor analysis (EFA).

Results

Nearly one-quarter of the sample (24.1%) were multimorbid (i.e. had ≥2 diseases), with significantly higher burden in females compared to males (31.6% vs. 16.1%; p<0.001). LCA revealed four distinct classes, including a predominant Relatively Healthy class, comprising 52.7% of the sample, with little to no morbidity. The remaining three classes were characterized by varying degrees and patterns of multimorbidity and labelled Metabolic (30.9%), Vascular-Inflammatory (12.2%), and Respiratory (4.2%). Four diseases determined using physical assessments (obesity, hypertension, diabetes, hypercholesterolemia) were primary contributors to multimorbidity patterns overall. EFA identified three patterns described as “Vascular” (hypertension, obesity, hypercholesterolemia, diabetes, stroke); “Respiratory” (asthma, COPD); and “Cardio-Mental-Articular” (cardiovascular disease, arthritis, mental disorders).

Conclusion

This first study of multimorbidity in the Caribbean has revealed a high burden of co-existing conditions in the Jamaican population, that is predominantly borne by females. Consistency across methods supports the validity of patterns identified. Future research into the causes and consequences of multimorbidity patterns can guide development of clinical and public health strategies that allow for targeted prevention and intervention.

A cluster analysis of serious adverse event reports after human papillomavirus (HPV) vaccination in Danish girls and young women, September 2009 to August 2017

BackgroundSuspected adverse events (AE) after human papillomavirus (HPV) vaccines include postural orthostatic tachycardia syndrome (POTS), chronic fatigue syndrome (CFS), complex regional pain syndrome (CRPS) and symptoms including headache and orthostatic intolerance.AimWe aimed to identify phenotypes of AEs after HPV vaccination, defined as patterns of AE terms (signs, symptoms, diagnoses), and to evaluate if identified phenotypes reflected previously suspected symptomatology or heightened public concerns over HPV vaccine safety since 1 January 2015.MethodsWe conducted a retrospective observational study using latent class cluster analysis of all serious AE reports (n = 963) reported by females residing in Denmark between September 2009 and August 2017. Resulting clusters were characterised according to AE terms associated with POTS, CFS and CRPS before (September 2009-December 2014) and during (January 2015-August 2017) a time of heightened media activity regarding HPV vaccines.ResultsFour clusters of AE reports were distinguished. The most common symptoms were fatigue, dizziness and headache but their frequency varied among clusters. The majority of reports in one cluster were submitted during a period of heightened media activity, including an anomalous spike in submissions in December 2015; a high proportion of these reports included the symptoms cognitive disorder (78%), abdominal pain (77%), dysuria (60%) and sleep disorder (60%).ConclusionsNon-specific symptoms including headache, fatigue and dizziness feature prominently in serious AE reports from females in Denmark. Our analysis identified a cluster of reports, likely media stimulated, with a focus on symptoms of CFS and POTS.

Closable Valves and Channels for Polymeric Microfluidic Devices

This study explores three unique approaches for closing valves and channels within microfluidic systems, specifically multilayer, centrifugally driven polymeric devices. Precise control over the cessation of liquid movement is achieved through either the introduction of expanding polyurethane foam, the application of direct contact heating, or the redeposition of xerographic toner via chloroform solvation and evaporation. Each of these techniques modifies the substrate of the microdevice in a different way. All three are effective at closing a previously open fluidic pathway after a desired unit operation has taken place, i.e., sample metering, chemical reaction, or analytical measurement. Closing previously open valves and channels imparts stringent fluidic control-preventing backflow, maintaining pressurized chambers within the microdevice, and facilitating sample fractionation without cross-contamination. As such, a variety of microfluidic bioanalytical systems would benefit from the integration of these valving approaches.

A new phylogenetic protocol: dealing with model misspecification and confirmation bias in molecular phylogenetics

Molecular phylogenetics plays a key role in comparative genomics and has increasingly significant impacts on science, industry, government, public health and society. In this paper, we posit that the current phylogenetic protocol is missing two critical steps, and that their absence allows model misspecification and confirmation bias to unduly influence phylogenetic estimates. Based on the potential offered by well-established but under-used procedures, such as assessment of phylogenetic assumptions and tests of goodness of fit, we introduce a new phylogenetic protocol that will reduce confirmation bias and increase the accuracy of phylogenetic estimates.

Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces

An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.

Using Serum Amino Acids to Predict Traumatic Brain Injury: A Systematic Approach to Utilize Multiple Biomarkers

Traumatic brain injury (TBI) can cause biochemical and metabolomic alterations in the brain tissue and serum. These alterations can be used for diagnosis and prognosis of TBI. Here, the serum concentrations of seventeen amino acids (AA) were studied for their potential utility as biomarkers of TBI. Twenty-five female, 4-week-old piglets received diffuse (n = 13) or focal (n = 12) TBI. Blood samples were obtained both pre-injury and at either 24-h or 4-days post-TBI. To find a robust panel of biomarkers, the results of focal and diffuse TBIs were combined and multivariate logistic regression analysis, coupled with the best subset selection technique and repeated k-fold cross-validation method, was used to perform a thorough search of all possible subsets of AAs. The combination of serum glycine, taurine, and ornithine was optimal for TBI diagnosis, with 80% sensitivity and 86% overall prediction rate, and showed excellent TBI diagnostic performance, with 100% sensitivity and 78% overall prediction rate, on a separate validation dataset including four uninjured and five injured animals. We found that combinations of biomarkers outperformed any single biomarker. We propose this 3-AA serum biomarker panel to diagnose mild-to-moderate focal/diffuse TBI. The systematic approaches implemented herein can be used for combining parameters from various TBI assessments to develop/evaluate optimal multi-factorial diagnostic/prognostic TBI metrics.

What Influences Educators' Design Preferences for Bullying Prevention Programs? Multi-level Latent Class Analysis of a Discrete Choice Experiment

We used a discrete choice conjoint experiment to model the anti-bullying (AB) program preferences of 1080 junior kindergarten to Grade 8 educators. Participants chose between hypothetical AB programs that varied combinations of 12 design attributes. Multi-level latent class analysis yielded three classes: All-in Supervisors (21.5%) preferred that all teaching staff supervise playgrounds and hallways; Facilitators (61.6%) preferred that students take ownership of AB activities with 25% of educators supervising playgrounds and hallways; and Reluctant Delegators (16.9%) preferred delegating the supervision of playgrounds and hallways to non-teaching staff. This class reported higher dispositional reactance, more implementation barriers, and more psychological reactance to these initiatives. They were less sensitive to social influences and less intent on participating in AB activities. Multi-level analysis showed a greater proportion of Reluctant Delegators clustered in one of the two groups of schools. The program choices of all classes were sensitive to the support of principals, colleagues, students, and, to a lesser extent, parents. All classes preferred programs conducted from kindergarten through Grade 12 that addressed the problems underlying bullying while valuing firm and consistent consequences for all students. Educators preferred AB programs selected by individual schools, rather than governments.

Trajectories of self-rated health in an older general population and their determinants: the Lifelines Cohort Study

OBJECTIVES

Poor self-rated health (SRH) is a strong predictor of premature mortality in older adults. Trajectories of poor SRH are associated with multimorbidity and unhealthy behaviours. Whether trajectories of SRH are associated with deviating physiological markers is unclear. This study identified trajectories of SRH and investigated the associations of trajectory membership with chronic diseases, health risk behaviours and physiological markers in community-dwelling older adults.

STUDY DESIGN AND SETTING

Prospective general population cohort.

PARTICIPANTS

Trajectories of SRH over 5 years were identified using data of 11 600 participants aged 65 years and older of the Lifelines Cohort Study.

OUTCOME MEASURES

Trajectories of SRH were the main outcome. Covariates included demographics (age, gender, education), chronic diseases, health-risk behaviour (physical activity, smoking, drinking) and physiological markers (body mass index, cardiovascular function, lung function, glucose metabolism, haematological condition, endocrine function, renal function, liver function and cognitive function).

RESULTS

Four stable trajectories were identified, including excellent (n=607, 6%), good (n=2111, 19%), moderate (n=7677, 65%) and poor SRH (n=1205, 10%). Being women (OR: 1.4; 95% CI: 1.0 to 1.9), low education (OR: 2.1; 95% CI: 1.5 to 3.0), one (OR: 10.4; 95% CI: 7.4 to 14.7) or multiple chronic diseases (OR: 37.8; 95% CI: 22.4 to 71.8), smoking (OR: 1.8; 95% CI: 1.0 to 3.2), physical inactivity (OR: 3.1; 95% CI: 1.8 to 5.2), alcohol abstinence (OR: 2.2; 95% CI: 1.4 to 3.2) and deviating physiological markers (OR: 1.5; 95% CI: 1.1 to 2.0) increase the odds for a higher probability of poor SRH trajectory membership compared with excellent SRH trajectory membership.

CONCLUSION

SRH of community-dwelling older adults is stable over time with the majority (65%) having moderate SRH. Older adults with higher probabilities of poor SRH often have unfavourable health status.

Parental and life-course influences on symptomatic airflow obstruction

Few studies have examined the contribution of life-course factors in explaining familial aggregation of chronic lung conditions. Using data from the 1958 British Birth Cohort, a life-course approach was used to examine whether, and how, exposure to risk factors through one's life explained the association between parental respiratory disease history and symptomatic airflow obstruction (AO). Cohort participants (n=6212) were characterised in terms of parental respiratory disease history and symptomatic AO at 45 years. Life-course factors (e.g. smoking, asthma and early-life factors) were operationalised as life period-specific and cumulative measures. Logistic regression and path analytic models predicting symptomatic AO adjusted for parental respiratory disease history were used to test different life-course models (critical period, accumulation- and chain-of-risks models). While some life-course factors (e.g. childhood passive smoking and occupational exposure) were individually associated with parental respiratory disease history and symptomatic AO, asthma (OR 6.44, 95% CI 5.01-8.27) and persistent smoking in adulthood (OR 5.42, 95% CI 4.19-7.01) had greater impact on the association between parental respiratory disease history and symptomatic AO. A critical period model provided a better model fit compared with an accumulation-of-risk model and explained 57% of the effect of parental respiratory disease history on symptomatic AO. Adulthood asthma and smoking status explained around half of the effect of parental respiratory disease history on chronic obstructive pulmonary disease. Beyond smoking history, the combination of parental respiratory disease history and adulthood asthma may provide an opportunity for early diagnosis and intervention.

A Statistical Approach to Determine Optimal Models for IUPAC-Classified Adsorption Isotherms

Adsorption heat transformation (AHT) systems can play a major role in protecting our environment by decreasing the usage of fossil fuels and utilizing natural and alternative working fluids. The adsorption isotherm is the most important feature in characterizing an AHT system. There are eight types of International Union of Pure and Applied Chemistry (IUPAC) classified adsorption isotherms for different “adsorbent-adsorbate” pairs with numerous empirical or semi-empirical mathematical models to fit them. Researchers face difficulties in choosing the best isotherm model to describe their experimental findings as there are several models for a single type of adsorption isotherm. This study presents the optimal models for all eight types of isotherms employing several useful statistical approaches such as average error; confidence interval (CI), information criterion (ICs), and proportion tests using bootstrap sampling. Isotherm data of 13 working pairs (which include all eight types of IUPAC isotherms) for AHT applications are extracted from literature and fitted with appropriate models using two error functions. It was found that modified Brunauer–Emmet–Teller (BET) for Type-I(a) and Type-II; Tóth for Type-I(b); GAB for Type-III; Ng et al. model for Type-IV(a) and Type-IV(b); Sun and Chakraborty model for Type-V; and Yahia et al. model for Type-VI are the most appropriate as they ensure less information loss compared to other models. Moreover; the findings are affirmed using selection probability; overall; and pairwise proportion tests. The present findings are important in the rigorous analysis of isotherm data.

Sociodemographic characteristics associated with alcohol consumption and alcohol-related consequences, a latent class analysis of The Norwegian WIRUS screening study

BACKGROUND

For alcohol, the association with socioeconomic status (SES) is different than for other public health challenges - the associations are complex, and heterogeneous between socioeconomic groups. Specifically, the relationship between alcohol consumption per se and adverse health consequences seems to vary across SES. This observation is called the 'alcohol harm paradox'. This study aims to describe different patterns of alcohol use and potential problems. Next, the associations between sub-groups characterized by different patterns of alcohol use and potential problems, and age, gender, educational level, full-time employment, occupational level and income is analysed.

METHODS

Employing data from the ongoing cross-sectional WIRUS-study, N = 4311 participants were included in the present study. Individual response patterns of the ten-item Alcohol Use Disorders Identification Test (AUDIT) were analysed and latent class analysis (LCA) was used to identify latent groups. Next, the associations between the classes identified in the best fitting LCA-model and sociodemographic factors were analysed and presented.

RESULTS

We identified three classes based on the response patterns on AUDIT. Class 1 was characterised by low-level alcohol consumption and very low probability of negative alcohol-related consequences related to their alcohol consumption. Class 2 was characterised by a higher level of consumption, but despite this, class 2 also had a relatively low probability of reporting negative alcohol-related consequences. Class 3, however, was characterised by high levels of alcohol consumption, and a high probability of reporting negative consequences of their consumption. The classes identified were systematically differentially associated with the included measures of SES, with class 3 characterised by younger age, more males and lower educational attainment.

CONCLUSIONS

Our findings highlight the interconnectedness of alcohol consumption and alcohol-related consequences. Furthermore, the identified classes and SES yields further insights into to intricate relationship between various socioeconomic factors, alcohol use patterns and related negative consequences.

Comparing the potential causal influence of two indicators of early alcohol use on later alcohol use disorder symptoms

Age of first drink (AFD) has repeatedly been found to be associated with alcohol use disorder (AUD); however, some studies suggest this is a noncausal effect that may be due to childhood risk factors or familial influences. In contrast to indicators of any early alcohol use, such as AFD, indicators of a pattern of repeated drinking may be more likely to be causally associated with later problematic alcohol use. The current study examined AFD and age of onset of regular drinking (ARD; defined as drinking at least once a month for 6 or more months) as quasicausal predictors of lifetime AUD symptoms. Participants were 3,005 adult Australian twins who reported having been regular drinkers in their lifetime. Semistructured interviews were conducted to assess AFD, ARD, AUD, externalizing symptomatology, and other substance use. Personality traits were assessed via questionnaire. Unadjusted and adjusted multilevel discordant twin models were conducted using data from 1,041 complete twin pairs; adjusted models included socioeconomic status, personality, conduct disorder, and early initiation of regular smoking and marijuana use as covariates. Results from fully adjusted models controlling for familial confounds provided evidence for a causal influence of ARD on AUD symptoms, whereby twins with an earlier age of regular drinking than their cotwin had more lifetime AUD symptoms. However, AFD did not significantly predict AUD symptoms after adjusting for confounds. These results suggest that early regular drinking may serve as a causal risk factor for future problems, while early initiation of any alcohol use may indicate genetic liability. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Delay effect and burden of weather-related tuberculosis cases in Rajshahi province, Bangladesh, 2007-2012

Tuberculosis (TB) is a potentially fatal infectious disease that continues to be a public health problem in Bangladesh. Each year in Bangladesh an estimated 70,000 people die of TB and 300,000 new cases are projected. It is important to understand the association between TB incidence and weather factors in Bangladesh in order to develop proper intervention programs. In this study, we examine the delayed effect of weather variables on TB occurrence and estimate the burden of the disease that can be attributed to weather factors. We used generalized linear Poisson regression models to investigate the association between weather factors and TB cases reported to the Bangladesh National TB control program between 2007 and 2012 in three known endemic districts of North-East Bangladesh. The associated risk of TB in the three districts increases with prolonged exposure to temperature and rainfall, and persisted at lag periods beyond 6 quarters. The association between humidity and TB is strong and immediate at low humidity, but the risk decreases with increasing lag. Using the optimum weather values corresponding to the lowest risk of infection, the risk of TB is highest at low temperature, low humidity and low rainfall. Measures of the risk attributable to weather variables revealed that weather-TB cases attributed to humidity is higher than that of temperature and rainfall in each of the three districts. Our results highlight the high linearity of temporal lagged effects and magnitudes of the burden attributable to temperature, humidity, and rainfall on TB endemics. The results can hopefully advise the Bangladesh National TB control program and act as a practical reference for the early warning of TB cases.

Development and validation of a model that includes two ultrasound parameters and the plasma D-dimer level for predicting malignancy in adnexal masses: an observational study

Background

Pre-operative discrimination of malignant from benign adnexal masses is crucial for planning additional imaging, preparation, surgery and postoperative care. This study aimed to define key ultrasound and clinical variables and develop a predictive model for calculating preoperative ovarian tumor malignancy risk in a gynecologic oncology referral center. We compared our model to a subjective ultrasound assessment (SUA) method and previously described models.

Methods

This prospective, single-center observational study included consecutive patients. We collected systematic ultrasound and clinical data, including cancer antigen 125, D-dimer (DD) levels and platelet count. Histological examinations served as the reference standard. We performed univariate and multivariate regressions, and Bayesian information criterion (BIC) to assess the optimal model. Data were split into 2 subsets: training, for model development (190 observations) and testing, for model validation (n = 100).

Results

Among 290 patients, 52% had malignant disease, including epithelial ovarian cancer (72.8%), metastatic disease (14.5%), borderline tumors (6.6%), and non-epithelial malignancies (4.6%). Significant variables were included into a multivariate analysis. The optimal model, included three independent factors: solid areas, the color score, and the DD level. Malignant and benign lesions had mean DD values of 2.837 and 0.354 μg/ml, respectively. We transformed established formulae into a web-based calculator (http://gin-onc-calculators.com/gynonc.php) for calculating the adnexal mass malignancy risk. The areas under the curve (AUCs) for models compared in the testing set were: our model (0.977), Simple Rules risk calculation (0.976), Assessment of Different NEoplasias in the adneXa (ADNEX) (0.972), Logistic Regression 2 (LR2) (0.969), Risk of Malignancy Index (RMI) 4 (0.932), SUA (0.930), and RMI3 (0.912).

Conclusions

Two simple ultrasound predictors and the DD level (also included in a mathematical model), when used by gynecologist oncologist, discriminated malignant from benign ovarian lesions as well or better than other more complex models and the SUA method. These parameters (and the model) may be clinically useful for planning adequate management in the cancer center. The model needs substantial validation.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5629-x) contains supplementary material, which is available to authorized users.

Subtypes of stuttering determined by latent class analysis in two Swiss epidemiological surveys

Aims

Associations between stuttering in childhood and a broad spectrum of risk factors, associated factors and comorbidities were examined in two large epidemiological studies. Subtypes of stuttering were then identified based on latent class analysis (LCA).

Methods

Data were from two representative Swiss population samples: PsyCoLaus (N = 4,874, age 35–82 years) and the ZInEP Epidemiology Survey (N = 1,500, age 20–41 years). Associations between stuttering and sociodemographic characteristics, familial aggregation, comorbidity and psychosocial risk / associated factors were investigated in both samples. LCAs were conducted on selected items from people in both samples who reported having stuttered in childhood.

Results

Initial analyses linked early anxiety disorders, such as separation anxiety disorder and overanxious disorder, to stuttering (PsyCoLaus). ADHD was associated with stuttering in both datasets. In the analyses of risk / associated factors, dysfunctional parental relationships, inter-parental violence and further childhood adversities were mutual predictors of stuttering. Moreover, comorbidities were seen with hay fever, asthma, eczema and psoriasis (PsyCoLaus). Subsequent LCA identified an unspecific group of persons who self-reported that they stuttered and a group defined by associations with psychosocial adversities (ZINEP, PsyCoLaus) and atopic diseases (PsyCoLaus).

Conclusions

The two subtypes of developmental stuttering have different risk / associated factors and comorbidity patterns. Most of the factors are associated with vulnerability mechanisms that occur early in life and that have also been linked with other neurodevelopmental disorders. Both psychosocial and biological factors appear to be involved in the etiopathogenesis of stuttering.

Identifying heavy health care users among primary care patients with chronic non-cancer pain

Objective: The objective of this study was to identify biopsychosocial factors predicting primary care chronic non-cancer pain (CNCP) patients' risk of being heavy health care users. Methods: Patients reporting moderate to severe CNCP for at least 6 months with an active analgesic prescription from a primary care physician were recruited in community pharmacies. Recruited patients completed questionnaires documenting biopsychosocial characteristics. Using administrative databases, direct costs were estimated for health care services used by each patient in the year preceding and following the recruitment. Heavy health care users were defined as patients in the highest annual direct health care costs quartile. Logistic multivariate regression models using the Akaike information criterion were developed to identify predictors of heavy health care use. Results: The median annual direct health care cost incurred by heavy health care users (n = 63) was CAD (Canadian dollars) 7627, versus CAD 1554 for standard health care users (n = 188). The final predictive model of the risks of being a heavy health care user included pain located in the lower body (odds ratio [OR] = 3.03; 95% confidence interval [CI], 1.20-7.65), pain-related disability (OR = 1.24; 95% CI, 1.03-1.48), and health care costs incurred in the year prior to recruitment (OR = 17.67; 95% CI, 7.90-39.48). Variables in the model also included sex, comorbidity, patients' depression level, and attitudes toward medical pain cure. Conclusion: Patients suffering from CNCP in the lower body and showing greater disability were more likely to be heavy health care users, even after adjusting for previous-year direct health care costs. Improving pain management for these patients could have positive impacts on health care use and costs.

Contemporary alcohol use patterns among a national sample of U.S. adult drinkers

The aim of the current article is to identify subgroups of adult drinkers characterized by typical drinking patterns. Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III were used to classify drinkers based on several indicators of drinking. Past-year drinkers aged 18-64 were included (n = 22,776). Latent class analysis revealed a 5-class model: Occasional, Light Drinkers (28%), Frequent Drinkers (25%), Infrequent Drinkers with Occasional Binging (5%), Frequent Drinkers with Occasional Binging (22%), and High-Intensity Drinkers (20%). Although most were Light Drinkers, many engaged in excessive drinking. Given the potential risk for harm, prevention efforts are warranted particularly for High-Intensity Drinkers.