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Background Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics of CVD in these populations and develop a prediction model by using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods We used follow-up data from the CHARLS to analyse CVD incidence in the Chinese middle-aged and elderly population over a time span of 9 years. Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. The application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Model performance was evaluated via analyses including the area under the ROC curve (AUC), precision, recall, F1 score, and SHAP plots for interpretability. Results In accordance with the exclusion criteria, 12,580, 12,061, 11,545, and 11,619 participants were enrolled in four follow-up rounds. The cumulative incidence (CI) of CVD at 2, 4, 7, and 9 years was 2.846%, 8.971%, 17.869% and 20.518%,, respectively. Significant differences in CVD incidence were observed across gender, age, ethnicity, and region, with higher rates observed in females and in the northeast region. Ultimately, 8,080 participants and 24 features were analysed for CVD risk prediction. Five ML models were built based on these features. Although the LGB model achieves an AUC of 0.818, indicating strong overall performance, its F1 score and recall rate are relatively low, at 0.509 and 43.1%, respectively. Shapley additive explanations (SHAP) analyses revealed the importance of key features, such as night sleep duration, TG levels, and waist circumference, in predicting outcomes, and highlighted the nonlinear relationships between these features and CVD risk. Conclusions Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients.

Background The prevalence of depression symptoms, the third most disabling disease worldwide, is as high as 11.5%-21.1% in China’s middle-aged and elderly population and increases significantly with age. It is crucial to identify high-risk groups efficiently and implement appropriate early interventions to improve the performance of depression risk prediction models. Methods We used data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020) to track depression the onset characteristics of depression symptoms in adults aged over 45 without depressive symptoms at baseline. This tracking was conducted over 9 years, involving four follow-ups. Eight machine-learning models, with pre-sampling and three types of resampled data, were employed. Their hyperparameters were optimized through a grid search strategy and tenfold cross-validation. Model performance was evaluated, including the area under the ROC curve (AUC), precision, recall, and F1 score. Additionally, Shapley Additive Properties (SHAP) plots for interpretability. Results The cumulative incidence of depression symptoms at different follow-up time points was 19.043%, 22.554%, 27.416%, and 29.416%, respectively, with higher incidence rates in females, rural areas, those with low education, and the western regions. The RandomUnder-Sampler-extreme gradient boosting(XGB) model performed optimally in predicting the 9-year risk of depression symptoms (recall = 70.36%, F1 = 0.5605, AUC = 0.750). SHAP analysis showed that education level, cognitive ability, and satisfaction with life were the core factors affecting the prediction of depression symptoms. Conclusions The prevalence of depressive symptoms in China’s middle-aged and elderly population is high, and the influencing factors are complex. When predicting depressive symptoms, the model should be selected based on the prediction needs, and random undersampling with XGB is suitable for long-term risk prediction in large-scale populations. For high-risk groups, accurate prediction strategies can be used to reduce the risk of depressive symptoms.

As of March 2021, coronavirus disease (COVID-19) had led to >500,000 deaths in the United States, and the state of Tennessee had the fifth highest number of cases per capita. We reviewed the Tennessee Department of Health COVID-19 surveillance and chart-abstraction data during March 15‒August 15, 2020. Patients who died from COVID-19 were more likely to be older, male, and Black and to have underlying conditions (hereafter comorbidities) than case-patients who survived. We found 30.4% of surviving case-patients and 20.3% of deceased patients had no comorbidity information recorded. Chart-abstraction captured a higher proportion of deceased case-patients with >1 comorbidity (96.3%) compared with standard surveillance deaths (79.0%). Chart-abstraction detected higher rates of each comorbidity except for diabetes, which had similar rates among standard surveillance and chart-abstraction. Investing in public health data collection infrastructure will be beneficial for the COVID-19 pandemic and future disease outbreaks.

Objective: Within oncological diseases, stomach cancer is ranked fourth in Kazakhstan, following breast cancer, cancer of the trachea, bronchi and lungs, and skin cancer. In males, stomach cancer is ranked second, following cancer of the trachea, bronchi and lungs, and amounting to 11.8% from all the localizations. Methods: Descriptive methods of modern oncological epidemiology were used in the present study, which was performed on the total population of Kazakhstan. The calculation of the unadjusted and adjusted rates, and the morbidity structure rates was performed according to all age-gender groups over a 10-year period (between 2004 and 2013). Results: 2013, the stomach cancer morbidity frequency in Kazakhstan was 16.4/100,000 of the population (21.8 in males and 11.6 in females). In the structure of malignant growths, the proportion of stomach cancer was 8.5%. There was a tendency in morbidity reduction between 2004 and 2013 (growth rate, -18.0%). A significant predominance of the prevalence rate of this localization was observed among males. The morbidity peak was indicated in the 75-79 years old group (225.8 and 90.3/100,000 of the relevant population in males and females, respectively). Conclusion: In spite of the progress made in oncology worldwide, stomach cancer in Kazakhstan remains one of the most prevailing malignant growths. Identification of the features of stomach cancer morbidity in Kazakhstan may reduce its prevalence in the future by tailoring research into preventing the incidence and improving treatment.

PurposeThe social vulnerability index (SVI), developed by the Centers for Disease Control and Prevention, is a novel composite measure encompassing multiple variables that correspond to key social determinants of health. The objective of this review was to investigate innovative applications of the SVI to oncology research and to employ the framework of the cancer care continuum to elucidate further research opportunities.MethodsA systematic search for relevant articles was performed in five databases from inception to 13 May 2022. Included studies applied the SVI to analyze outcomes in cancer patients. Study characteristics, patent populations, data sources, and outcomes were extracted from each article. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.ResultsIn total, 31 studies were included. Along the cancer care continuum, five applied the SVI to examine geographic disparities in potentially cancer-causing exposures; seven in cancer diagnosis; fourteen in cancer treatment; nine in treatment recovery; one in survivorship care; and two in end-of-life care. Fifteen examined disparities in mortality.ConclusionIn highlighting place-based disparities in patient outcomes, the SVI represents a promising tool for future oncology research. As a reliable geocoded dataset, the SVI may inform the development and implementation of targeted interventions to prevent cancer morbidity and mortality at the neighborhood level.

Clinicians can encounter sex and gender disparities in diagnostic and therapeutic responses. These disparities are noted in epidemiology, pathophysiology, clinical manifestations, disease progression, and response to treatment. This Review discusses the fundamental influences of sex and gender as modifiers of the major causes of death and morbidity. We articulate how the genetic, epigenetic, and hormonal influences of biological sex influence physiology and disease, and how the social constructs of gender affect the behaviour of the community, clinicians, and patients in the health-care system and interact with pathobiology. We aim to guide clinicians and researchers to consider sex and gender in their approach to diagnosis, prevention, and treatment of diseases as a necessary and fundamental step towards precision medicine, which will benefit men's and women's health.

Observational studies have suggested that accelerated surgery is associated with improved outcomes in patients with a hip fracture. The HIP ATTACK trial assessed whether accelerated surgery could reduce mortality and major complications. HIP ATTACK was an international, randomised, controlled trial done at 69 hospitals in 17 countries. Patients with a hip fracture that required surgery and were aged 45 years or older were eligible. Research personnel randomly assigned patients (1:1) through a central computerised randomisation system using randomly varying block sizes to either accelerated surgery (goal of surgery within 6 h of diagnosis) or standard care. The coprimary outcomes were mortality and a composite of major complications (ie, mortality and non-fatal myocardial infarction, stroke, venous thromboembolism, sepsis, pneumonia, life-threatening bleeding, and major bleeding) at 90 days after randomisation. Patients, health-care providers, and study staff were aware of treatment assignment, but outcome adjudicators were masked to treatment allocation. Patients were analysed according to the intention-to-treat principle. This study is registered at ClinicalTrials.gov (NCT02027896). Between March 14, 2014, and May 24, 2019, 27 701 patients were screened, of whom 7780 were eligible. 2970 of these were enrolled and randomly assigned to receive accelerated surgery (n=1487) or standard care (n=1483). The median time from hip fracture diagnosis to surgery was 6 h (IQR 4–9) in the accelerated-surgery group and 24 h (10–42) in the standard-care group (p<0·0001). 140 (9%) patients assigned to accelerated surgery and 154 (10%) assigned to standard care died, with a hazard ratio (HR) of 0·91 (95% CI 0·72 to 1·14) and absolute risk reduction (ARR) of 1% (−1 to 3; p=0·40). Major complications occurred in 321 (22%) patients assigned to accelerated surgery and 331 (22%) assigned to standard care, with an HR of 0·97 (0·83 to 1·13) and an ARR of 1% (−2 to 4; p=0·71). Among patients with a hip fracture, accelerated surgery did not significantly lower the risk of mortality or a composite of major complications compared with standard care. Canadian Institutes of Health Research.

The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories. In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466–469, 470.0, 480–482.8, 483.0–483.9, 484.1–484.2, 484.6–484.7, and 487–489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4–B97.6, J09–J15.8, J16–J16.9, J20–J21.9, J91.0, P23.0–P23.4, and U04–U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age–sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age–sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors. Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240–275) LRI incident episodes in males and 232 million (217–248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18–1·42) male deaths and 1·20 million (1·07–1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16–1·18) and 1·31 times (95% UI 1·23–1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4–131·1]) and deaths (100·0% [83·4–115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (–70·7% [–77·2 to –61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53·0% [95% UI 37·7–61·8] in males and 56·4% [40·7–65·1] in females), and more than a quarter of LRI deaths among those aged 5–14 years were attributable to household air pollution (PAF 26·0% [95% UI 16·6–35·5] for males and PAF 25·8% [16·3–35·4] for females). PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4–25·2) in those aged 15–49 years, 30·5% (24·1–36·9) in those aged 50–69 years, and 21·9% (16·8–27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5–27·9) in those aged 15–49 years and 18·2% (12·5–24·5) in those aged 50–69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2–15·8) of LRI deaths. The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities. Bill & Melinda Gates Foundation.

Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated ( r  = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset ( n  = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry. Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven approach to show that the symptom dimensions of mood, psychosis, fear and externalizing behavior exhibit unique patterns of functional dysconnectivity.

Jails and prisons are exceptionally susceptible to viral outbreaks, such as severe acute respiratory syndrome coronavirus 2. The USA has extremely high rates of incarceration and COVID-19 is causing an urgent health crisis in correctional facilities and detention centres. Epidemics happening in prisons are compounding the elevated risks that COVID-19 poses to people of colour, older people, and those with comorbidities. Intersectoral community re-entry efforts in the USA and other countries have shown that releasing people from correctional facilities as a pandemic-era public health intervention is safe and can support both public safety and community rebuilding. Therefore, substantial decarceration in the USA should be initiated. A point of focus for such efforts is that many people in prison are serving excessively long sentences and pose acceptable safety risks for release. Properly managed, correctional depopulation will prevent considerable COVID-19 morbidity and mortality and reduce prevailing socioeconomic and health inequities.