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Background: There is a growing interest in the linkage of cardiovascular health (CVH) with depression/anxiety but the evidence of the latest “Life’s Essential 8” is scarce. We aimed to evaluate prospective associations of “Life’s Essential 8” CVH score redefined in 2022 with risk of incident depression and anxiety. Methods: Based on the latest definition of CVH score (“Life’s Essential 8”), we calculated the total score (0-800 points) based on eight components (0-100 points/component) including diet, physical activity, smoking, sleep duration, body mass index, blood lipids, blood glucose, and blood pressure. Hospital records and mental health questionnaires were employed to yield the baseline prevalence and incidence of depression and anxiety. To address potential genetic confounding and gene-CVH interactions, we quantified genetic risk for both disorders using polygenic scores derived from genome-wide association studies. Results: Participants with a lower CVH score more often experienced depression, anxiety, and either disorder. During the follow-up, 17,554 incident events with symptoms of either disorder were recorded. Per 100-point decrease in CVH score was significantly associated with an increased risk of incident either disorder (Hazard ratio [HR]=1.149, 95% confidence interval [CI]:1.128-1.170), depression (HR=1.232, 95% CI:1.205-1.260), and anxiety (HR=1.045, 95% CI:1.018-1.072). Sleep duration was the key component related to the risk of depression/anxiety by providing much better estimates of the risks of depression/anxiety. A higher genetic risk could further significantly enhance the impact of worsened CVH on the incidences of either disorder and depression. Conclusions: Poor CVH is a risk factor for the incident late-life depression/anxiety symptoms of middle-aged and older adults and healthy behaviors especially sleep quality could be targeted for the risk assessment and intervention of depression/anxiety.

ObjectiveTo explore the correlation between the ideal cardiovascular health metrics (ICVHMs) and the incidence of colorectal cancer (CRC) among people aged 50 years or older.DesignProspective cohort study.SettingThe UK Biobank, a prospective cohort of middle-aged participants recruited between 2006 and 2010.ParticipantsThe study included 342 226 participants from the UK Biobank aged 50 years or older without prevalent cancer.ExposureThe ICVHMs consist of four behavioural factors (abstinence from smoking, ideal body mass index (BMI), physical activity at goal and consumption of healthy diet) and three cardiometabolic factors (untreated total cholesterol Main outcomesThe outcome was ascertained by linkage to cancer and death registries using the International Classification of Diseases, Tenth codes C18–C20.ResultsDuring a median follow-up time of 8.72 years, 3060 CRC cases were identified. Compared with the reference (participants with ICVHMs ≤2), the multivariable-adjusted HRs for subgroups with 3, 4, 5 and ≥6 ICVHM factors were 0.98 (95% CI 0.85 to 1.12), 0.90 (95% CI 0.77 to 1.02), 0.85 (95% CI 0.71 to 0.98) and 0.69 (95% CI 0.48 to 0.90), respectively. Among the seven ICVHM factors, lower BMI, healthier diet and ideal fasting plasma glucose were significantly associated with lower risk of CRC (HR: 0.86, 95% CI 0.78 to 0.95; HR: 0.92, 95% CI 0.84 to 0.99; HR: 0.90, 95% CI 0.80 to 0.99).ConclusionsAdherence to the ICVHMs was associated with a lower risk of CRC among people aged 50 years or older. Among the seven ICVHM factors, BMI, diet and fasting plasma glucose played a more critical role in the prevention of CRC. These findings imply that adherence to ICVHMs should be encouraged to reduce the burden of cardiovascular disease as well as CRC.

Background: Dyslipidemia is an important risk factor for coronary artery disease and stroke. Early detection and prevention of dyslipidemia can markedly alter cardiovascular morbidity and mortality. Cox proportional hazard model has been commonly employed for survival datasets to construct the prediction model. Recently, the data-driven learning algorithm began to be used to analyze right-censored survival data. However, there is no attempt to use deep neural networks in dyslipidemia prediction. The objective of this study is to predict the risk of dyslipidemia via deep neural networks for survival data.Methods: The study cohort was based on the routine health check-up data from 6,328 participants aged 19 to 90 years and free of dyslipidemia at baseline. A deep neural network (DNN) was used to develop risk models for predicting dyslipidemia. Cox Proportional Hazards (Cox) and Random Survival Forests (RSF) were applied in comparison with the DNN model. As our metric of performance, we use the time-dependent concordance index (Ctd-index).Results: The Ctd-index of the prediction models by using DNN was 0.802. The DNN model performed significantly better than Cox and RSF model (Ctd-index: 0.735 and 0.770, respectively). The improvement of DNN over the competing methods was statistically significant. Moreover, DNN provides performance gain on time intervals compared to conventional survival models.Conclusions: DNN is a promising method in learning the estimated distribution of survival time and event while capturing the right-censored nature inherent in survival data. DNN achieves large and statistically significant performance improvements over previous intuitive regression model and state-of-the-art data-mining methods.

Previous studies have shown certain exposure factors (such as lifestyle and metabolism) are associated with colorectal cancer (CRC) events. However, the application of the exposome theoretical frame and the extent to which the exposome domain can modulate the risk of CRC remain unknown. Our study aimed to construct valid exposome measurements and examine the relationship between exposome counts and the risk of CRC. This study included 273,342 individuals in the UK Biobank. We used exploratory factor analysis to identify a valid construct of exposome factors. We then summed the exposome counts within each domain. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of CRC risk related to the exposome factors and counts. During an 8.69 year median follow-up, 8642 CRC cases were identified. Seven domains were extracted from 18 variables, including air pollution, noise pollution, ecosystem, lifestyle, tobacco and alcohol use, social economics, and social support. The Cox model results showed that the ecosystem was positively related to the reduced CRC risk (HR=0.97; 95% CI: 0.949–0.991). Similar results were also found among the domains of healthy lifestyles (HR=0. 873; 95% CI: 0.854–0.893), and no tobacco and alcohol use (HR=0.880; 95% CI: 0.862–0.899). The disadvantageous social economic (HR=1.068; 95% CI: 1.046–1.090) and insufficient social support domains (HR=1.067; 95% CI: 1.042–1.094) were associated with an increased risk of CRC. Similar risk trends were also observed across the exposome count groups with CRC incidence. Our findings suggest that certain exposure domains are related to the incidence of CRC. Ecosystem, lifestyle, and social factors can be incorporated into prediction models to identify individuals at high risk of CRC.

Objective Prevention and early detection of colorectal cancer (CRC) can increase the chances of successful treatment and reduce burden. Various data mining technologies have been utilized to strengthen the early detection of CRC in primary care. Evidence synthesis on the model’s effectiveness is scant. This systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of CRC. Methods The PRISMA framework guided the conduct of this study. We obtained papers via PubMed, Cochrane Library, EMBASE and Google Scholar. Quality appraisal was performed using Downs and Black’s quality checklist. To evaluate the performance of included models, the values of specificity and sensitivity were comparted, the values of area under the curve (AUC) were plotted, and the median of overall AUC of included studies was computed. Results A total of 316 studies were reviewed for full text. Seven articles were included. Included studies implement techniques including artificial neural networks, Bayesian networks and decision trees. Six articles reported the overall model accuracy. Overall, the median AUC is 0.8243 [interquartile range (IQR): 0.8050−0.8886]. In the two articles that reported comparison results with traditional models, the data mining method performed better than the traditional models, with the best AUC improvement of 10.7%. Conclusions The adoption of data mining technologies for CRC detection is at an early stage. Limited numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques’ effects.

BACKGROUND Dyslipidemia is an important risk factor for coronary artery disease and stroke. Early detection and prevention of dyslipidemia can markedly alter cardiovascular morbidity and mortality. Cox proportional hazard model has been commonly employed for survival datasets to construct the prediction model. Recently, the data-driven learning algorithm began to be used to analyze right-censored survival data. However, there is no attempt to use deep neural networks in dyslipidemia prediction. OBJECTIVE The objective of this study is to predict the risk of dyslipidemia via deep neural networks for survival data. METHODS The study cohort was based on the routine health check-up data from 6,328 participants aged 19 to 90 years and free of dyslipidemia at baseline. A deep neural network (DNN) was used to develop risk models for predicting dyslipidemia. Cox Proportional Hazards (Cox) and Random Survival Forests (RSF) were applied in comparison with the DNN model. As our metric of performance, we use the time-dependent concordance index (Ctd-index). RESULTS The Ctd-index of the prediction models by using DNN was 0.802. The DNN model performed significantly better than Cox and RSF model (Ctd-index: 0.735 and 0.770, respectively). The improvement of DNN over the competing methods was statistically significant. Moreover, DNN provides performance gain on time intervals compared to conventional survival models. CONCLUSIONS DNN is a promising method in learning the estimated distribution of survival time and event while capturing the right-censored nature inherent in survival data. DNN achieves large and statistically significant performance improvements over previous intuitive regression model and state-of-the-art data-mining methods.

Background Chronic systemic inflammation has been positively associated with structural and functional brain changes representing early markers of Alzheimer’s Disease (AD) and cognitive decline. The current study examined associations between systemic inflammation and cognitive performance among African-Americans and Whites urban adults. Methods Participants were selected from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study (2004–2013, baseline age: 30–64 y, mean ± SD follow-up time of 4.64 ± 0.93 y, N = 189–222, k = 1.5–1.7 observations/participant). Cytokines known to be positively linked to AD incidence among others were tested against cross-sectional and longitudinal cognitive function, stratifying by age group (≤50 y vs. >50 y), sex and race. A series of mixed-effects regression models were conducted, adjusting for key confounders. Results Among key findings, IL1β was positively associated with a faster rate of decline on a test of executive functioning, among older adults (age >50 y, γ11 = +2.49 ± 0.89, p = 0.005), while in the total population, IL-6 was linked to a faster decline on a test of verbal memory (γ11 = −0.011 ± 0.004, p = 0.009). Among younger participants, IL-18 was linked to a poorer performance on a test of attention at baseline (age ≤50 y, γ01 = −0.007 ± 0.0025, p = 0.004) though a slower rate of decline with higher IL-18 was detected for a test of psychomotor speed in older adults (age >50 y, γ11 = +0.0010 ± 0.0004, p = 0.008). Finally, among Whites, unlike among African-Americans, IL-6 was associated with a better baseline performance on two tests of verbal and working memory. Conclusions Cytokines were shown to be associated with age-related cognitive decline among middle-aged and older urban adults in an age group and race-specific manner. Further longitudinal studies are needed to replicate our findings and mediation through relevant biological and psychosocial factors need to be studied as well.

Introduction Understanding the role of Medicaid for health centre patients with chronic diseases can aid policymakers in designing and improving programmes to effectively meet the needs of this vulnerable segment of the population. This study compares the number of chronic conditions, health services utilization and access to care between patients with Medicaid coverage and uninsured patients seen at US health centres. Methods We used data from the 2014 Health Center Patient Survey. Measures included number of chronic conditions, number of physician visits, number of prescription drugs used, access to a usual source of care, the receipt of a routine checkup, unmet need for care and unmet need for prescription drugs. We conducted bivariate analyses to determine differences of outcome measures between the Medicaid and uninsured patient groups. Multiple linear and logistic regression models were conducted to test associations of Medicaid and other health-related factors with outcome measures. Results Bivariate results indicated there were no differences between Medicaid and uninsured patients in number of chronic diseases, having a usual source of care and receipt of a routine checkup. Significant differences existed in health services utilization measures and unmet health needs measures. After controlling for confounding factors, the differences in these measures were still apparent. Uninsured status was linked to a reduced physician visit frequency by −3.03 (95% CI: −4.05, −2.00) as compared with Medicaid patients (p Conclusion Despite having comparable needs to Medicaid patients, uninsured patients with chronic conditions continue to have substantial unmet needs for health care services and limited access to the health care system. Health centres serve an important role in eliminating such disparity regardless of insurance status. In addition, Medicaid eligibility may also have a substantial and positive impact on improving health services utilization and access to care among these low-income patients.

Chronic disease burden and its related health risk factors are especially concentrated among the poor. Community health centres reach the nation’s most vulnerable population. This study explored the prevalence, racial/ethnic, and gender disparities of five modifiable health risk factors and the receipt of related counselling and treatment among patients in U.S. federally qualified health centres. The 2014 Health Center Patient Survey was used for this study. We performed a cross-sectional study. Measures included tobacco use, excessive alcohol consumption, obese/overweight, high blood pressure, high blood cholesterol, and five related counselling/treatment measures. Logistic regression models were conducted to examine the multivariable-adjusted putative associations of several sociodemographic and health-related factors with modifiable health risk and counselling/treatment measures. Preliminary findings revealed differences in modifiable health risk factors across different racial/ethnic groups, while few racial/ethnic differences existed in related counselling/treatment measures. In the multivariable-adjusted logistic regression models, these differences in health risk factors were still apparent, while associations between race/ethnicity status and counselling/treatment measures were not detected. Gender was also a predictor for four types of health risk measures and two types of counselling/treatment measures. Health centre patients had high rates of modifiable health risk factors. The rates were different across race/ethnicity groups, and the differences found did not always favour non-Hispanic Whites. However, the findings reveal equitable access to related counselling/treatment service among patients across race/ethnicity groups.

Housing is one of the social determinants of health and the most basic survival needs of human beings. Many studies have preliminarily confirmed that housing factors can influence residents&rsquo, health. The aims of this study were: to evaluate the housing factors associated with self-rated health and mental health among Chinese residents, to explore the regional heterogeneity of the impact of housing on health, and to assess the effects of housing on health among different age groups. Data was derived from the Chinese General Social Survey (CGSS). Housing factors were analyzed along six dimensions: housing property, living space, number of living people, number of houses, living region and housing price. Self-rated health and mental health were used to measure health outcomes. Multivariate ordered logistic regression was performed to assess the relationship between housing and health. The living space, living region and housing price was significantly associated with self-rated health. The number of living people living region and housing price were related to mental health. The influence of housing factors on health were more pronounced among residents living in eastern and central area and among the middle-aged group (41&ndash, 65). Present findings support the notion that housing factors were related to health outcomes. Future studies may focus on the impact of interventions that target on these factors, and the impact of housing on health among special groups such as migrant population and low-and-middle income families.

Identifying molecular biomarkers promises to significantly improve the accuracy in cancer diagnosis at its early stage. DNA nanomachines, which are designable and switchable nanostructures made of DNA, show broad potential to detect tumor biomarkers with noninvasive, inexpensive, highly sensitive, and highly specific advantages. This Feature summarizes the recent DNA nanomachine-based platforms for the early detection of cancer biomarkers, both from body fluids and in cells.

Objective This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. Data source The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. Study design Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. Data collection/extraction methods We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findings While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health. Conclusion Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

At present, there is a lack of research examining the relationships between the built environment and health status from a social epidemiological perspective. With this in mind, the present study aimed to explore the construct validity of housing/neighborhood conditions and evaluate the associations between the built environment and self-rated health among Chinese residents. To conduct the analysis, data from 4906 participants were derived from the 2016 China Labor-force Dynamics Survey (CLDS). Specifically, we used exploratory factor analysis to identify construct of housing/neighborhood factors and performed principal component regression (PCR) to assess the relationship between the built environment and both self-rated physical health and mental health. This process identified five common factors that corresponded to the built environment, including housing affordability, housing quality, neighborhood services, neighborhood physical environment, and perceived environment. The regression results suggested that housing affordability was negatively related to health status. Meanwhile, the services, physical environment, and perceived environment of neighborhoods were related to positive health outcomes. The influence of housing on health exhibits group heterogeneity: respondents in the 41 to 65 age group were most vulnerable to poor built environments. Whilst the results indicated that housing factors and neighborhood conditions were related to health outcomes, their influence varied across different age groups. Future interventions should be intentionally designed to target housing affordability and neighborhood factors, which may include the provision of housing assistance programs and planning layouts.

Background: Healthcare providers’ health and safety can influence the quality of patient care. The attitude towards work, either satisfaction or dissatisfaction, could influence the way in which people perceive their health status. To check this hypothesis, some studies have shown an association between healthcare providers’ health status and job satisfaction. The present study examined the associations between self-perceived health status and job satisfaction among primary care providers in China, and the moderating effect of job type.Methods: Data were collected by using questionnaire survey among Chinese primary care providers, who work for general practice teams (GPTs). The GPT members’ self-perceived health, job satisfaction, and job type were evaluated, where job satisfaction comprised work, promotion and training, and work environment. Multivariate logistic regression analysis was performed.Findings: Of the 536 participants, 48.51% reported good physical health, and 55.78% reported good mental health. Participants reported being moderately satisfied with their job. Self-perceived physical and mental health were significantly and positively associated with job satisfaction in similar magnitudes. Promotion and training satisfaction were more strongly associated with physical health, whereas work environment had the strongest association with mental health. Job type moderated this association, wherein the association was significant only for non-general practitioners (non-GPs).Conclusion: Primary care providers’ job satisfaction is significantly associated with their self-perceived health. Actions are needed to increase their job satisfaction and therefore to improve their health. Meanwhile, further research into GPs’ health determinants is needed.

Background Optimal cancer care entails coordination among multiple providers and continued follow-up and surveillance over time. The patient-centered care brings opportunities to improve the delivery of cancer care. The adoption of patient-centered oncology care (PCOC) is in its infancy. Evidence synthesis on the model's effectiveness is scant. Purposes This is the first systemic review and meta-analysis on associations of PCOC with cancer patients' adverse health care utilization, cost, patient satisfaction, and quality of care. Methods Our study was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) framework. Quality appraisal was performed using Downs and Black's quality checklist. Study-level effect sizes of adverse health care utilization were computed using Cohen's d and summarized using forest plots. Funnel plots were constructed to examine publication bias. Results Of 334 studies that were reviewed, 10 met eligibility criteria and were included into the final analysis. Many included studies implemented almost all six of patient-centered care core attributes, plus three additional attributes that specifically addressed cancer patients' needs, including triage pathways, standardized and evidence-based symptom management, as well as support patient navigation. PCOC patients had lower utilization of inpatient care (standardized means difference [SMD] = -0.027, p = .049). Overall positive effect of PCOC on emergency department use was small and not significant (SMD = -0.023, p = .103). With regard to cost and quality of care, our narrative summaries showed an overall positive direction, though we found limitations in individual study quality that precluded a meta-analysis. Public implication The results showed that it is possible to utilize patient-centered model to support best practice of cancer care. Early evidence shows that the PCOC model has potential to improve health care utilization, cost, and quality of care, but limited numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the model's effects.

Objectives (1) To evaluate the relationship between Patient-centered Medical Home (PCMH) recognition and quality of clinical care among health centers, and (2) to determine whether the duration of recognition is positively associated with cumulative quality improvement over time. Methods Data came from the 2012 to 2015 Uniform Data System, health centers' PCMH recognition status, and the Area Resource File. Health center was the unit of observation. The outcome variables included 11 measures of clinical quality. We pooled all years of data and modeled longitudinal data with generalized estimating equations to examine the degree of improvement in health care quality in health centers with and without PCMH recognition over the years 2012-2015. Results Health centers with PCMH recognition generally performed better on clinical quality measures than health centers that did not have PCMH recognition for all years studied. After accounting for health center and county-level potential confounders, health centers with longer periods of PCMH recognition were more likely to have improved their clinical quality on 9 of 11 measures, than health centers with fewer years of PCMH recognition. Conclusions Health centers' length of time with PCMH recognition was positively associated with additive quality improvement. Adoption of the PCMH model of care may serve as a strategy to enhance quality of primary care services.