Relationship between socioeconomic status and cognitive ability among Chinese older adults: the moderating role of social support.

BACKGROUND: Understanding the causes and pathways of cognitive decline among older populations is of great importance in China. This study aims to examine whether the discrepancy in socioeconomic status (SES) makes a difference to the cognitive ability among Chinese older adults, and to disentangle the moderating role of different types of social support in the process in which SES influences cognition. METHODS: We utilized a nationally representative sample from the 2018 Chinese Longitudinal Healthy Longevity Survey. A cumulative SES score was constructed to measure the combined effect of different socioeconomic statuses on the cognitive ability of the elderly. We further examined the moderating role of two types of social support, including emotional support, and financial support. Hierarchical regression analysis was applied to test the direct effect of SES on cognitive ability, and to investigate the moderating role of social support on the association of the SES with the dependent variables. RESULTS: The results showed that the higher SES of older adults was significantly associated with better cognitive ability (ß = 0.52, p < 0.001) after controlling for age, sex, marital status, living region, Hukou, health insurance, lifestyle factors, and physical health status. Emotional support and financial support were moderated the relationship between SES score and cognitive ability. CONCLUSION: Our results reveal the importance of considering social support in buffering the effects of SES and the associated cognitive ability for aging populations. It highlights the importance of narrowing the socioeconomic gap among the elderly. Policymakers should consider promoting social support to improve the cognitive ability among older adults.

Genetic modification effects of physical frailty on the morbidity of mental disorders in the UK Biobank.

Objectives: Depression and anxiety are two major categories of mental disorders that are highly prevalent in the general population. This study aims to explore the genetic modification effects of physical frailty on the morbidity of mental disorders.Methods: Using data from UK Biobank, we calculate genetic risk scores for depression, anxiety and mental disorders based on 37/44 single-nucleotide polymorphisms (SNPs) of Major Depressive Disorder (MDD) and 9/10 SNPs of anxiety. Frailty status was defined by a modified version of the frailty phenotype based on five individual components. Cox proportional hazard models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of depression and anxiety risk among groups with different frailty status.Results: Of 267,755 participants in UK Biobank, 4,905 (2%) were considered frail, 98,907 (37%) pre-frail and 163,943 (61%) not frail. Compared with the non-frail group, the pre-frail group (HR = 1.53; [95% CI:1.47-1.61]), and frail group (HR = 2.75; [95% CI:2.46-3.07]) were significantly associated with increased risk of depression. Per 1-number increment in frailty component counts were significantly associated with increased risk of mental disorders. In each genetic risk score (GRS) strata, people with pre-frailty and frailty suffered higher risks of mental disorders than the non-frail group.Conclusion: Our results indicate that physical frailty plays an important role in the incidence of mental disorders, even after adjustments for covariates, and patients with genetic individual differences are also affected. Therefore, it is crucial that while diagnosing mental disorders, professionals pay closer attention to those patients who present symptoms of frailty.

Cancer screening among racial/ethnic groups in health centers.

BACKGROUND: Underserved and low-income population are placed at a disadvantage for receiving necessary cancer screenings. This study aims to measure the rates of receiving three types of cancer screening services, Pap test, mammogram and colorectal cancer screening, among patients seen at U.S. health centers (HCs) to investigate if cancer screening among patients varies by race/ethnicity. METHODS: We analyzed data from the 2014 U.S. Health Center Patient Survey, and included samples age 21 and above. We examined three cancer screening indicators as our dependent variables including cervical, breast, and colorectal cancer screening. Logistic regressions were used to assess the racial/ethnic disparities on cancer screening, while controlling for potentially confounding factors. RESULTS: The rates of receiving three types of cancer screening were comparable and even higher among HC patients than those for the U.S. general population. Both bivariate and multivariate results showed there were racial/ethnic differences in the likelihood of receiving cancer screening services. However, the differences did not favor non-Hispanic Whites. African Americans had higher odds than Whites (OR: 1.92, 95% CI: 1.44-2.55, p < 0.001) of receiving Pap tests. Similar results were also found in measures of the receipt of mammogram (OR = 1.96, 95% CI: 1.46-2.64, P < 0.001) and colorectal cancer screening (OR = 1.28, 95% CI: 1.02-1.60, p < 0.05). CONCLUSION: The current study presents U.S. nationally representative estimates and imply that HCs are helping fulfill an important role as a health care safety-net in reducing racial/ethnic disparities in the delivery of cancer screening services.

Exposome approach for identifying modifiable factors for the prevention of colorectal cancer.

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 335,370 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 and 95% confidence intervals of CRC risk related to the exposome factors and counts. During an 8.69 year median follow-up, 10,702 CRC cases were identified. Five domains were extracted from 12 variables, including 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 = 0.970; 95% CI 0.952-0.989). Similar results were also found among the domains of healthy lifestyles (HR = 0. 889; 95% CI 0.871-0.907), and no tobacco and alcohol use (HR = 0.892; 95% CI 0.876-0.909). The disadvantageous social economic (HR = 1.081; 95% CI 1.058-1.105) and insufficient social support domains (HR = 1.036; 95% CI 1.017-1.056) 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.

The Effect of Built Environment on Physical Health and Mental Health of Adults: A Nationwide Cross-Sectional Study in China.

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.

Sleep pattern, healthy lifestyle and colorectal cancer incidence.

Researchers have identified an association between lifestyle factors and colorectal cancer (CRC) risk. This study examined the relationship between sleep patterns and CRC events. 392,252 individuals were sampled from the UK Biobank. Chronotype, sleep duration, insomnia, snoring, and excessive daytime sleepiness were combined to measure a healthy sleep score. A number of healthy sleep factors were defined, along with factors for healthy lifestyle scores. Using Cox proportional hazards regression, computed hazard ratios (HRs) were used to examine the associations between sleep patterns, healthy lifestyles, and the incidence of CRC. Healthy sleep scores were inversely associated with CRC events. The HRs for CRC were 0.90 (95% CI, 0.88-0.92) and 0.95 (95% CI, 0.92-0.98) for a 1-point healthy sleep score increase among males and females. When analyzing sleep components, sleeping 7-8 h/day, no frequent insomnia, no snoring, and no frequent daytime sleepiness were independently associated with a 9%, 14%, 8%, and 14% lower risk of CRC, respectively, whilst healthy lifestyle scores were inversely associated with CRC incidence across all models. Sleep pattern and lifestyle are significantly correlated with CRC risk. The healthier the subject's lifestyle and sleep pattern, the lower their CRC risk.

Housing and Adult Health: Evidence from Chinese General Social Survey (CGSS).

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' 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 (41-65). 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.