Associations between perceived discrimination over the life course, subjective social status, and health literacy: A racial/ethnic stratification analysis

Objective

To analyze the relationship between perceived discrimination over the life course, social status, and limited health literacy (HL).

Methods

5040 adults who participated in the 2023 Survey of Racism and Public Health. We applied stratified multilevel models adjusted for sociodemographic characteristics.

Results

The average age was 47 years, 48% identified as White, 20% as Latinx, and 17% as Black. In the overall sample, we observed associations of perceived discrimination (b = 0.05, 95% CI: 0.01, 0.09), subjective social status (b = -0.16, 95% CI: -0.23, -0.10), and their interaction (b = 0.02, 95% CI: 0.01, 0.03). More perceived discrimination was associated with lower HL in the White and Multiracial participants. Higher subjective social status was associated with higher HL in the White and Latinx participants. There was a statistically significant interaction between perceived discrimination and subjective social status on HL among the White, Latinx, and Multiracial participants.

Conclusion

This analysis has implications for public health practice, indicating that multi-level interventions are needed to address limited HL.

Innovation

Our findings provide novel insights for identifying key SDOH indicators to assess in clinical settings to provide health literate care.

How firearm legislation impacts firearm mortality internationally: A scoping review

Background

The literature on gun violence is broad and variable, describing multiple legislation types and outcomes in observational studies. Our objective was to document the extent and nature of evidence on the impact of firearm legislation on mortality from firearm violence.

Methods

A scoping review was conducted under PRISMA-ScR guidance. A comprehensive peer-reviewed search strategy was executed in several electronic databases from inception to March 2024. Grey literature was searched for unpublished sources. Data were extracted on study design, country, population, type of legislation, and overall study conclusions on legislation impact on mortality from suicide, homicide, femicide, and domestic violence. Critical appraisal for a sample of articles with the same study design (ecological studies) was conducted for quality assessment.

Findings

5057 titles and abstracts and 651 full-text articles were reviewed. Following full-text review and grey literature search, 202 articles satisfied our eligibility criteria. Federal legislation was identified from all included countries, while state-specific laws were only reported in studies from the U.S. Numerous legislative approaches were identified including preventative, prohibitive, and more tailored strategies focused on identifying high risk individuals. Law types had various effects on rates of firearm homicide, suicide, and femicide. Lack of robust design, uneven implementation, and poor evaluation of legislation may contribute to these differences.

Interpretation

We found that national, restrictive laws reduce population-level firearm mortality. These findings can inform policy makers, public health researchers, and governments when designing and implementing legislation to reduce injury and death from firearms.

Funding

Funding is provided by the Strategy for Patient-Oriented Research (SPOR) Evidence Alliance and in part by St. Michael's Hospital, University of Toronto.

Scoping review registration

Open Science Framework (OSF): https://osf.io/sf38n.

Observational methods for human studies of transgenerational effects

There are substantial challenges in studying human transgenerational epigenetic outcomes resulting from environmental conditions. The task requires specialized methods and tools that incorporate specific knowledge of multigenerational relationship combinations of probands and their ancestors, phenotype data for individuals, environmental information of ancestors and their descendants, which can span historical to present datasets, and informative environmental data that chronologically aligns with ancestors and descendants over space and time. As a result, there are few epidemiologic studies of potential transgenerational effects in human populations, thus limiting the knowledge of ancestral environmental conditions and the potential impacts we face with modern human health outcomes. In an effort to overcome some of the challenges in studying human transgenerational effects, we present two transgenerational study designs: transgenerational space-time cluster detection and transgenerational case-control study design. Like other epidemiological methods, these methods determine whether there are statistical associations between phenotypic outcomes (e.g., adverse health outcomes) among probands and the shared environments and environmental factors facing their ancestors. When the ancestor is a paternal grandparent, a statistically significant association provides some evidence that a transgenerational inheritable factor may be involved. Such results may generate useful hypotheses that can be explored using epigenomic data to establish conclusive evidence of transgenerational heritable effects. Both methods are proband-centric: They are designed around the phenotype of interest in the proband generation for case selection and family pedigree creation. In the examples provided, we incorporate at least three generations of paternal lineage in both methods to observe a potential transgenerational effect.

A dual-gene panel of two fragments of methylated IRF4 and one of ZEB2 in plasma cell-free DNA for gastric cancer detection

Early detection is crucial for increasing the survival rate of gastric cancer (GC). We aimed to identify a methylated cell-free DNA (cfDNA) marker panel for detecting GC. The differentially methylated CpGs (DMCs) were selected from datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The selected DMCs were validated and further selected in tissue samples (40 gastric cancer and 36 healthy white blood cell samples) and in a quarter sample volume of plasma samples (37 gastric cancer, 12 benign gastric disease, and 43 healthy individuals). The marker combination selected was then evaluated in a normal sample volume of plasma samples (35 gastric cancer, 39 control diseases, and 40 healthy individuals) using real-time methylation-specific PCR (MSP). The analysis of the results compared methods based on 2-ΔΔCt values and Ct values. In the results, 30 DMCs were selected through bioinformatics methods, and then 5 were selected for biological validation. The marker combination of two fragments of IRF4 (IRF4-1 and IRF4-2) and one of ZEB2 was selected due to its good performance. The Ct-based method was selected for its good results and practical advantages. The assay, IRF4-1 and IRF4-2 in one fluorescence channel and ZEB2 in another, obtained 74.3% sensitivity for the GC group at any stage, at 92.4% specificity. In conclusion, the panel of IRF4 and ZEB2 in plasma cfDNA demonstrates good diagnostic performance and application potential in clinical settings.

Novel DNA methylation changes in mouse lungs associated with chronic smoking

Smoking is a potent cause of asthma exacerbations, chronic obstructive pulmonary disease (COPD) and many other health defects, and changes in DNA methylation (DNAm) have been identified as a potential link between smoking and these health outcomes. However, most studies of smoking and DNAm have been done using blood and other easily accessible tissues in humans, while evidence from more directly affected tissues such as the lungs is lacking. Here, we identified DNAm patterns in the lungs that are altered by smoking. We used an established mouse model to measure the effects of chronic smoke exposure first on lung phenotype immediately after smoking and then after a period of smoking cessation. Next, we determined whether our mouse model recapitulates previous DNAm patterns observed in smoking humans, specifically measuring DNAm at a candidate gene responsive to cigarette smoke, Cyp1a1. Finally, we carried out epigenome-wide DNAm analyses using the newly released Illumina mouse methylation microarrays. Our results recapitulate some of the phenotypes and DNAm patterns observed in human studies but reveal 32 differentially methylated genes specific to the lungs which have not been previously associated with smoking. The affected genes are associated with nicotine dependency, tumorigenesis and metastasis, immune cell dysfunction, lung function decline, and COPD. This research emphasizes the need to study CS-mediated DNAm signatures in directly affected tissues like the lungs, to fully understand mechanisms underlying CS-mediated health outcomes.

Assessing the prevalence of obesity in a Russian adult population by six indices and their associations with hypertension, diabetes mellitus and hypercholesterolaemia

The anthropometric index that best predicts cardiometabolic risk remains inconclusive. This study therefore assessed the prevalence of obesity using six indices and compared their associations with obesity-related cardiometabolic disorders. We determined obesity prevalence according to body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio (WHtR), body fat percentage and fat mass index (FMI) using data from the Know Your Heart study (n = 4495, 35-69 years). The areas under the receiver operating characteristic curves (AUCs) provided predictive values of each index for detecting the presence of hypertension, hypercholesterolaemia and diabetes. Age-standardised obesity prevalence significantly varied according to anthropometric index: from 17.2% (FMI) to 75.8% (WHtR) among men and from 23.6% (FMI) to 65.0% (WHtR) among women. WHtR had the strongest association with hypertension (AUC = 0.784; p < 0.001) and with a combination of disorders (AUC = 0.779; p < 0.001) in women. In women, WHtR also had the largest AUCs for hypercholesterolaemia, in men - for hypertension, diabetes and a combination of disorders, although not all the differences from other obesity indices were significant. WHtR exhibited the closest association between hypertension and a combination of disorders in women and was non-inferior compared to other indices in men.

Contribution of body mass index, waist circumference, and 25-OH-D3 on the risk of pre-diabetes mellitus in the Chinese population

This study aimed to investigate the associations between body mass index (BMI), waist circumference (WC), 25-hydroxy-vitamin D3 (25-OH-D3), and the risk of pre-diabetes mellitus (PDM), as well as their predictive values in identifying PDM. A total of 1688 participants were included in this cross-sectional investigation. Spearman's correlation analysis was used to assess the relationships between candidate indicators and PDM. The impact of indicators on PDM risk was determined by multivariate logistic regression. The receiver operating characteristic (ROC) analysis was performed to evaluate the prognostic value of indicators. Our study indicated a positive correlation between WC, BMI, and 25-OH-D3 and PDM. WC (OR = 1.05, 95% CI = 1.04-1.06, p < 0.001), BMI (OR = 1.11, 95% CI = 1.08-1.15, p < 0.001), and 25-OH-D3 (OR = 1.01, 95% CI = 1.00-1.02, p = 0.037) and an increased risk of PDM. Additionally, the ROC analysis demonstrated that WC (AUC = 0.651, Specificity = 55.00%, Sensitivity = 67.900%) had a higher diagnostic value for predicting PDM compared to the other variables (BMI, 25-OH-D3, TG, TC, LDL-C, HDL-C, and UA). A cut-off value of WC > 80.5 cm predicted PDM with both good sensitivity and specificity. Additionally, the cut-off value of waist circumference (WC) for men with prediabetes was 86.500, while for women with prediabetes, it was 76.500.

Development and validation of risk prediction model for adverse outcomes in trauma patients

BACKGROUND

The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis.

OBJECTIVE

To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China.

METHODS

This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA).

RESULTS

Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well.

CONCLUSIONS

This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.

Elements in trace amount with a significant role in human physiology: a tumor pathophysiological and diagnostic aspects

Cancer has a devastating impact globally regardless of gender, age, and community, which continues its severity to the population due to the lack of efficient strategy for the cancer diagnosis and treatment. According to the World Health Organisation report, one out of six people dies due to this deadly cancer and we need effective strategies to regulate it. In this context, trace element has a very hidden and unexplored role and require more attention from investigators. The variation in concentration of trace elements was observed during comparative studies on a cancer patient and a healthy person making them an effective target for cancer regulation. The percentage of trace elements present in the human body depends on environmental exposure, food habits, and habitats and could be instrumental in the early diagnosis of cancer. In this review, we have conducted inclusive analytics on trace elements associated with the various types of cancers and explored the several methods involved in their analysis. Further, intricacies in the correlation of trace elements with prominent cancers like prostate cancer, breast cancer, and leukaemia are represented in this review. This comprehensive information on trace elements proposes their role during cancer and as biomarkers in cancer diagnosis.

Shortening of telomere length may be associated with inflammatory cytokine levels in patients with bipolar disorder

BACKGROUND

Bipolar disorder (BD) is hypothesized to be associated with accelerated biological aging. Telomere length (TL) is a biomarker of aging, and although TL decreases with each cell division, the rate of telomere shortening may be affected by inflammation. We aimed to investigate whether TL is decreased in BD patients and to determine the association between TL and inflammatory markers in such patients.

METHODS

137 BD patients and 118 healthy controls (HCs) were recruited. Leukocyte TL and plasma levels of cytokines [tumor necrosis factor (TNF)-α, interleukin (IL)-8, IL-6, IL-10, transforming growth factor (TGF)-β1], C-reactive protein (CRP), and brain-derived neurotrophic factor (BDNF) were assessed.

RESULTS

TL did not differ significantly between the BD patients and HCs after adjustment for potential confounding factors (P = 0.79). TL was significantly negatively associated with age (β = -0.007, P < 0.001). In addition, log TNF-α levels were significantly negatively associated with TL (P = 0.009), in both the BD patients (P = 0.02) and HCs (P = 0.05).

CONCLUSION

We found a significant association between TNF-α levels and TL shortening in both BD patients and HCs. However, BD patients did not display increased TL shortening relative to HCs. Studies that involve larger sample sizes and control for the heterogeneity of BD participants will be needed.

Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data

BACKGROUND AND OBJECTIVE

Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance.

METHODS

We integrated multi-task and cost-sensitive learning, developing an enhanced deep neural network architecture that extends multi-task learning to predict mortality risk across multiple chronic diseases. Each patient cohort with a chronic disease was assigned to a separate task, with shared lower-level parameters capturing inter-disease complexities through distinct top-level networks. Cost-sensitive functions were incorporated to ensure learning of positive class characteristics for each task and achieve accurate prediction of the risk of death from multiple chronic diseases.

RESULTS

Our study covers 15 prevalent chronic diseases and is experimented with real-world data from 482,145 patients (including 9,516 deaths) in Shenzhen, China. The proposed model is compared with six models including three machine learning models: logistic regression, XGBoost, and CatBoost, and three state-of-the-art deep learning models: 1D-CNN, TabNet, and Saint. The experimental results show that, compared with the other compared algorithms, MTL-CSDNN has better prediction results on the test set (ACC=0.99, REC=0.99, PRAUC=0.97, MCC=0.98, G-means = 0.98).

CONCLUSIONS

Our method provides valuable insights into leveraging real-world data for precise multi-disease mortality risk prediction, offering potential applications in optimizing chronic disease management, enhancing well-being, and reducing healthcare costs for the elderly population.

A systematic review of interpersonal processes and their measurement within experience sampling studies of self-injurious thoughts and behaviours

Self-injurious thoughts and behaviours (SITBs) are a leading cause of death, and interpersonal processes (IPs) appear to play a role in SITBs. This systematic review synthesises the literature on IPs and SITBs in daily life and addresses four critical questions: (1) Which IPs have been assessed and how, (2) How are differences in IPs between individuals associated with SITBs?, (3) How are differences in IPs within individuals associated with SITBs? and (4) Do IPs relate differently to self-injurious thoughts than behaviours? Our review followed PRISMA guidelines and eligible literature was screened until 25 April 2024. We identified 58 Experience Sampling studies (32.76% daily-diary studies) of which most focused on IPs from major SITBs theories (e.g., thwarted belongingness) but largely used inconsistent operationalizations. Results from 39 studies investigating within-person associations were mixed. Based on 26 studies, whether differences in IPs between individuals relate to SITBs remains unclear. Three studies have investigated whether IPs relate to the transition from thoughts to behaviours, but temporal models are needed to draw firm conclusions. Studies investigating IPs and S