Associations of neighborhood deprivation and household income during pregnancy on child externalizing and internalizing problems.

There is increasing recognition that socioeconomic inequalities contribute to disparities in brain and cognitive health in older adults. However, whether neighborhood socioeconomic status (SES) buffers individuals with low individual SES against neurodegeneration, cerebrovascular disease, and poorercognitive function is not well understood. Here, we evaluated whether neighborhood deprivation (Townsend deprivation index) interacted with individual SES (composite household income and education levels) on hippocampus volume, regional cortical thickness, white matter hyperintensities, and cognition in 19,638 individuals (mean age = 54.8) from the UK Biobank. We found that individuals with low individual SES had the smallest hippocampal volumes, greatest white matter hyperintensity burden, and poorest cognition if they were living in high deprivation neighborhoods but that these deleterious effects on brain and cognitive function were attenuated if they were living in low deprivation neighborhoods (p for interactions < .05). While neighborhood deprivation did not interact with individual SES to influence regional cortical thickness, higher neighborhood deprivation was independently associated with lower cortical thickness in 16 regions (false discovery rate q < .05). Across multiple brain indices and cognitive function analyses, we found converging evidence suggesting that low neighborhood deprivation may have a neuroprotective effect against neurodegeneration, cerebrovascular pathology, and cognitive impairment, particularly in vulnerable individuals with low household income and education levels.

BackgroundActive travel for utilitarian purposes contributes to total physical activity and may help counter the obesity epidemic. However, the evidence linking active travel and individual-level body weight is equivocal. Statistical modeling that accounts for spatial autocorrelation and unmeasured spatial predictors has not yet used to explore whether the health benefits of active travel are shared equally across socioeconomic groups.MethodsBayesian hierarchical models with spatial random effects were developed using travel survey data from Saskatoon, Canada (N = 4625). Differences in log-transformed body mass index (BMI) were estimated for levels of active travel use (vehicular travel only, mixed vehicular/active travel, and active travel only), household income, and neighbourhood deprivation after controlling for sociodemographic and physical activity variables. The modifying effect of household income on the association between active travel and BMI was also evaluated.ResultsSignificant and meaningful decreases in BMI were observed for mixed (β = − 0.02, CrI − 0.036 to − 0.004) and active only (β = − 0.043, CrI − 0.06 to − 0.025) compared to vehicular only travelers. BMI was significantly associated with levels of household income and neighbourhood deprivation. Accounting for the interaction between travel mode and household income, decreases in BMI were observed for active only compared to vehicular only travellers in the highest income category (β = − 0.061, CrI − 0.115 to − 0.007).ConclusionStrategies to increase active travel use can support healthy weight loss and maintenance, but the opportunity to benefit from active travel use may be limited by low income. Considerations should be given to how interventions to increase active transportation might exacerbate social inequalities in BMI. Spatial statistical models are needed to account for unmeasured but spatially structured neighbourhood factors.

BackgroundIncome inequality has dramatically increased worldwide, and there is a need to re-evaluate the association between socio-economic status (SES) and depression. Relative contributions of household income and education to depression, as well as their interactions, have not been fully evaluated. This study aimed to examine the association between SES and depressive symptoms in Japanese adults, focusing on interactions between education and household income levels.MethodsThis cross-sectional study used data from baseline surveys of two cohort studies. Participants were 38,499 community-dwelling people aged 40–74 years who participated in baseline surveys of the Murakami cohort study (2011–2012) and Uonuma cohort study (2012–2015) conducted in Niigata Prefecture, Japan. Information regarding marital status, education level, household income, occupation, activities of daily living (ADL), and history of cancer, myocardial infarction, stroke, and diabetes was obtained using a self-administered questionnaire. Depressive symptoms were examined using the Center for Epidemiologic Studies Depression Scale (CES-D). Logistic regression analysis was used to obtain odds ratios (ORs). Covariates included age, sex, marital status, education, household income, occupation, ADL, and disease history.ResultsIndividuals with higher education levels had lower ORs (adjusted P for trend = 0.0007) for depressive symptoms, independently of household income level. The OR of the university-or-higher group was significantly lower than that of the junior high school group (adjusted OR = 0.79). Individuals with lower household income levels had higher ORs (adjusted P for trend< 0.0001) for depressive symptoms, independently of education level. The type of occupation was not associated with depressive symptoms. In subgroup analyses according to household income level, individuals with higher education levels had significantly lower ORs in the lowest- and lower-income groups (adjusted P for trend = 0.0275 and 0.0123, respectively), but not in higher- and highest-income groups (0.5214 and 0.0915, respectively).ConclusionsBoth education and household income levels are independently associated with the prevalence of depressive symptoms, with household income levels showing a more robust association with depressive symptoms than education levels. This suggests that a high household income level may offset the risk of depressive symptoms from having a low education level.

Objective: To assess relations between parental socioeconomic status (SES) and impaired fasting glucose (IFG) and obesity of children by sex in Korea population. Method: This study was based on the data from the Korean National Health and Nutrition Examination Survey, conducted by the Korean Ministry of Health and Welfare from 2007 to 2017. Data of 28747 participants (6605 children, 4969 fathers, and 6387 mothers) were analyzed. The SES was assessed with household income level and education level. The household income level was categorized into three groups: high, middle, and low. The education level was categorized into two groups: graduated post-secondary school or not. The prevalence of IFG was defined as &amp;gt;=100 fasting blood sugar. The prevalence of obesity was defined with the Korean Pediatrics Growth Chart from 2017. The relationship of SES to the risk of IFG and obesity was assessed with multivariate logistic regression analysis. Result: In female children participants, a higher risk of IFG was associated with 1) low household income level (OR: 2.874, 95% CI:1.315, 6.279) and 2) low education level of mother (OR: 1.923, 95% CI: 1.062, 3.744), while the risk was not associated with low education level of father (OR: 1.395, 95% CI: 0.893, 2.18). Risk of obesity of female children was also associated with these two factors: low household income level (OR: 2.262, 95% CI: 1.182, 4.328) and education level of mother (OR: 1.484, 95% CI: 1.081, 2.036). Unlike female, in male children participants, risk of IFG and obesity was not associated with either factors. Conclusions: For girls, low household income and low mothers’ education level may increase the incidence of IFG and obesity while boys are not affected by either factors. These differences should be considered when raising children and managing risk of diabetes and obesity of children to ultimately prevent risk of diabetes. Disclosure S. Lee: None. K. Han: None.

Association between recommended levels of physical activity and depressive symptoms among Japanese adults: A cross-sectional study

In wrongful death damages, personal consumption dollars represent the portion of total household expenditures that are exclusive to the decedent. Most of the personal consumption estimates in the forensic economic literature are computed from Bureau of Labor Statistics (BLS) published tables of Consumer Expenditure Survey (CEX) data. Those tables delineate, by household size and income, the average total dollars spent on approximately 20 summary expenditure groupings.1 Ireland and Depperschmidt (1999) presented a compilation of personal consumption articles that rely on such CEX summary table data with recent like-articles being written by Ajwa, Martin and Vavoulis (2000) and Ruble, Patton, and Nelson (2004). Trout and Foster (1993) and Scoggins (2001) both used the CEX Interview microdata to compute personal consumption estimates specific to husbands and wives. Unfortunately, those two studies ignored the Diary part of the CEX which includes significant household expenditures relevant to personal consumption and they continued to use the BLS’s summary group tabulations of each household’s expenditures. This paper uses CEX microdata to provide new personal consumption estimates for husbands and wives by their work status and by the number of children living in the home. Instead of relying on summary expenditures, all 700 micro-expenditure items within the CEX Interview and Diary surveys are incorporated into the analysis. Like Trout and Foster and Scoggins, the expenditure data is specific to husbands and wives, but this study goes further by using all of the CEX Interview and Diary microdata instead of just summary Interview data. Husband and wife specific data, by work status and number of children living at home, are important delineations in reliably computing personal consumption applicable to the loss of wage earnings of a married person. Assume the case of a wrongful death of a working married male living only with his wife. If a traditional two-person household personal consumption table were used to offset the husband’s earnings loss, such an analysis would mix the expenditure data of working and retired husbands and wives along with mixing in the data of households consisting of two single persons or one single person living with a child, etc. Another problem with studies based on summary tables is that total expenditures are usually shown to exceed income at the lower income levels. That result is not necessarily a fault of the CEX, but its presenta-

Parenting stress reflects a discrepancy between a parent's perception of their resources, the demands of their child's needs, and the caregiving relationship and contexts (Abidin, 1992). Parenting stress can increase the risk of issues in the parent-child relationship, as well as child behavioral and emotional outcomes (Neece et al., 2012; Spinelli et al., 2021). Chronic stressors, such as living through the COVID-19 pandemic, have the potential to increase the demands of parenting and thus parenting stress. Using latent growth curve modeling, we examined parenting stress trajectories of 298 American parents with young children (Mage = 15.02 months, range = 1-34 months) over the first year of the COVID-19 pandemic. We also examined the effects of parental mental health on parenting stress, and the effects of parental mental health and parenting stress on child problem behaviors using data gathered through the Prolific survey platform. Parental mental health, measured by depressive symptoms Centre for Epidemiological Studies Depression Scale-10, anxiety symptoms Generalized Anxiety Disorder Scale (GAD-7), and overall stress levels 10-item Perceived Stress Scale, was related to higher initial parenting stress index-short form. Changes in parenting stress over time were linked with higher levels of children's problem behaviors (CBCL). Child temperament was also related to initial parenting stress. Lower levels of household income were linked with higher levels of parental mental health symptoms and higher rates of parenting stress increases over time. These results highlight the importance of considering the well-being of all family members in child outcomes, and the ways in which different experiences and resources during the COVID-19 pandemic affect parental and child well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reports an error in "Parenting young children during COVID-19: Parenting stress trajectories, parental mental health, and child problem behaviors" by Ashleigh I. Aviles, Sophia K. Betar, Sarah M. Cline, Ziyu Tian, Deborah B. Jacobvitz and Jody S. Nicholson (Journal of Family Psychology, 2024[Mar], Vol 38[2], 296-308). In the original article, there were some errors. Corrections have been made in the Abstract sentence, first paragraph of the main text, the beginning of the first sentence of the Parenting Stress subsection in the Method section, and the start of the first sentence of the Depressive Symptoms subsection. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2024-45266-001). Parenting stress reflects a discrepancy between a parent's perception of their resources, the demands of their child's needs, and the caregiving relationship and contexts (Abidin, 1992). Parenting stress can increase the risk of issues in the parent-child relationship, as well as child behavioral and emotional outcomes (Neece et al., 2012; Spinelli et al., 2021). Chronic stressors, such as living through the COVID-19 pandemic, have the potential to increase the demands of parenting and thus parenting stress. Using latent growth curve modeling, we examined parenting stress trajectories of 298 American parents with young children (Mage = 15.02 months, range = 1-34 months) over the first year of the COVID-19 pandemic. We also examined the effects of parental mental health on parenting stress, and the effects of parental mental health and parenting stress on child problem behaviors using data gathered through the Prolific survey platform. Parental mental health, measured by depressive symptoms Centre for Epidemiological Studies Depression Scale-10, anxiety symptoms Generalized Anxiety Disorder Scale (GAD-7), and overall stress levels 10-item Perceived Stress Scale, was related to higher initial parenting stress index-short form. Changes in parenting stress over time were linked with higher levels of children's problem behaviors (CBCL). Child temperament was also related to initial parenting stress. Lower levels of household income were linked with higher levels of parental mental health symptoms and higher rates of parenting stress increases over time. These results highlight the importance of considering the well-being of all family members in child outcomes, and the ways in which different experiences and resources during the COVID-19 pandemic affect parental and child well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

BackgroundDiet is an important risk factor for cardiovascular diseases and shows well-established socioeconomic patterning among adults. However, less clear is how socioeconomic inequalities in diet develop across the life course....

BackgroundHealth-related quality of life (HRQoL), which can be influenced by various aspects, especially socioeconomic status and lifestyle, has been identified as an important predictor of the prognosis of older adults. Dietary habit, a major part of lifestyle, can affect the nutritional status, which is closely correlated with the development of geriatric syndromes in the elderly.AimsThe aim of the study was to examine the association of HRQoL, socioeconomic status, and lifestyle with the risk and severity of sarcopenia, a geriatric syndrome characterized by progressive loss of skeletal muscle mass, strength and function.MethodsA cross-sectional retrospective study with 2877 participants aged ≥65 years was performed. HRQoL was assessed using EuroQoL Five Dimensions questionnaire. Socioeconomic status was assessed by the educational attainment, occupation, and household income. Lifestyle was assessed using 12 items closely related to Chinese living habits. The information of daily dietary habits including tea, alcohol, type of diet, and volume of drinking water were collected. The associations of HRQoL, socioeconomic status, and lifestyle with the risk of sarcopenia were examined by multivariate regression logistical analysis. The potential causal role of age, body mass index, and waist circumference in the effect of HRQoL on sarcopenia risk was analyzed by causal mediation analysis.ResultsHigh HRQoL [adjusted odds ratio (OR) =0.85, 95% confidence interval (CI) =0.69–0.95, P=0.034] and household income levels (adjusted OR =0.74, 95% CI =0.57–0.95, P=0.019) were inversely associated with the risk of sarcopenia. Meanwhile, more consumption of spicy food (adjusted OR =1.34, 95% CI =1.09–1.81, P =0.037) and occasionally drinking (adjusted OR =1.46, 95% CI =1.07–2.00, P =0.016, as compared to those never drinking) were associated with higher risk of sarcopenia, while skipping breakfast occasionally (adjusted OR =0.37, 95% CI =0.21–0.64, P <0.001, as compared to those eating breakfast every day) and less consumption of salt (adjusted OR =0.71, 95% CI =0.52–0.96, P =0.026, as compared to those consuming high amount of salt) were associated with lower risk of sarcopenia. Further causal mediation analysis aimed to explore how much age, body mass index, and waist circumference might explain the effect of HRQoL on the risk of sarcopenia showed that the estimated proportion that mediated the effect of HRQoL on the risk of sarcopenia by age was 28.0%.ConclusionsIn summary, our findings demonstrate that low levels of HRQoL and household income, more intake of salt and spicy food, and occasional intake of alcohol are correlated with higher risk of sarcopenia, while skipping breakfast occasionally is associated with lower risk of sarcopenia in a Chinese population of older adults.

BackgroundPersonal social capital, which refers to the scope and quality of an individual’s social networks within a community, has received increasing attention as a potential sociological factor associated with better individual health; yet, the mechanism relating social capital to health is still not fully understood. This study examined the associations between social capital and self-rated health while exploring the roles of leisure-time physical activity (LTPA) and socioeconomic status (SES) among middle-aged and older adults.MethodsCross-sectional data were collected from 662 middle-aged and older adults (Mean age: 58.11 ± 10.59 years old) using the Qualtrics survey panel. Personal Social Capital Scale was used to measure bonding and bridging social capital and the International Physical Activity Questionnaire was used to assess LTPA levels. SES was assessed by education and household income levels. Self-rated health was assessed using a single item, by which the participants were categorized into the two groups, having ‘good’ vs. ‘not good’ self-rated health. A series of univariate and multivariate logistic regression models were established to examine the independent and adjusted associations of social capital with self-rated health and to test mediating and moderating roles of LTPA and SES, respectively.ResultsBonding and bridging social capital were positively associated with self-rated health (Odds ratios = 1.11 and 1.09; P’s < .05, respectively), independent of LTPA that was also significantly associated with greater self-rated health (P-for-linear trends = .007). After adjusting SES, the associations of social capital were significantly attenuated and there was a significant interaction effect by household income (P-for-interaction = .012). Follow-up analyses stratified by household income showed that beneficial associations of social capital with self-rated health were more apparent among the people with low and high levels of household income; yet, LTPA was the stronger predictor of self-rated health among those in the middle class of household income.ConclusionsFindings suggest that both social capital and LTPA are associated with better self-rated health; yet, these associations vary by SES. The health policymakers should address both social capital and LTPA for enhancing perceived health among aging populations but may need to consider varying SES backgrounds.

A subset of normal-weight individuals appears predisposed to obesity-related cardiometabolic abnormalities. Studies of this metabolically obese, normal weight (MONW) phenotype in youth are scarce. We aimed to identify early environmental and genetic factors associated with MONW in children. Overall, 1475 normal-weight Chinese children aged 6-18 were recruited from the Beijing Children and Adolescents Metabolic Syndrome study cohort. Birthweight, childhood lifestyle, socio-economic factors, and 20 genetic variants previously shown to be associated with BMI or glucose metabolism in East Asian adults were examined for their association with the MONW phenotype. MONW was defined by exhibiting any metabolic syndrome component. After adjusting for covariates including BMI, low birthweight and low levels of physical activity, fruit consumption, parental education and household income, as well as CDKAL1 rs2206734 genotype were independent predictors of the MONW phenotype (all P < 0.05). Moreover, rs2206734 interacted with birthweight to predict the MONW phenotype (Pinteraction = 0.0008). Among high (>75th percentile) birthweight individuals, each C allele at this locus was associated with a 62% reduced risk of MONW (OR = 0.38; 95% CI = 0.26-0.58; P = 5.71 × 10-6), while no such genetic associations were found in intermediate or low birthweight individuals (P > 0.1). This CDKAL1-MONW relationship in high birthweight individuals was especially strong in the presence of favorable childhood environmental factors (high levels of physical activity, fruit consumption, parental education and household income) (Pinteraction = 0.013). Our findings provided the novel evidence that early environment (especially birthweight) and genetics, along with their interaction with one another, play important roles in predicting the MONW phenotype among children.

BackgroundA lack of evidence exists on the association between area-level income inequality and oral health within Australia. This study examined associations between area-level income inequality and oral health outcomes (inadequate dentition (<21 teeth) and poor self-rated oral health) among Australian adults. Variations in the association between area-level income inequality and oral health outcomes according to area-level mean income were also assessed. Finally, household-income gradients in oral health outcomes according to area-level income inequality were compared.MethodsFor the analyses, data on Australian dentate adults (n = 5,165 nested in 435 Local Government Areas (LGAs)) was obtained from the National Dental Telephone Interview Survey-2013. Multilevel multivariable logistic regression models with random intercept and fixed slopes were fitted to test associations between area-level income inequality and oral health outcomes, examine variations in associations according to area-level mean income, and examine variations in household-income gradients in outcomes according to area-level income inequality. Covariates included age, sex, LGA-level mean weekly household income, geographic remoteness and household income.ResultsLGA-level income inequality was not associated with poor self-rated oral health and inversely associated with inadequate dentition (OR: 0.64; 95% CI: 0.48, 0.87) after adjusting for covariates. Inverse association between income inequality and inadequate dentition at the individual level was limited to LGAs within the highest tertile of mean weekly household income. Household income gradients in both outcomes showed poorer oral health at lower levels of household income. The household income gradients for inadequate dentition varied according to the LGA-level income inequality.ConclusionFindings suggest that income inequality at the LGA-level in Australia is not positively associated with poorer oral health outcomes. Inverse association between income inequality and inadequate dentition is likely due to the contextual differences between Australia and other high-income countries.

Using UK household expenditure data spanning over four decades (1960–2000), this paper employs Engel’s needs-based approach to analyzing household expenditure patterns and finds evidence for the existence of a stable hierarchy of expenditure patterns at low levels of household income. Second, we investigate how rising household income influences the manner in which total expenditure is distributed across Engel’s expenditure categories. Our results suggest that i) total household expenditure is distributed across Engel’s expenditure categories in an increasingly even manner as household income increases and ii) over time, there has been an acceleration in the rate at which household expenditure patterns become diversified as household income rises. Finally, we consider how the shape of Engel Curves may help shed light on the relationship between goods and the underlying needs they serve.

Using UK household expenditure data spanning over four decades (1960–2000), this paper employs Engel’s needs-based approach to analyzing household expenditure patterns and finds evidence for the existence of a stable hierarchy of expenditure patterns at low levels of household income. Second, we investigate how rising household income influences the manner in which total expenditure is distributed across Engel’s expenditure categories. Our results suggest that i) total household expenditure is distributed across Engel’s expenditure categories in an increasingly even manner as household income increases and ii) over time, there has been an acceleration in the rate at which household expenditure patterns become diversified as household income rises. Finally, we consider how the shape of Engel Curves may help shed light on the relationship between goods and the underlying needs they serve.KeywordsGini CoefficientHousehold ExpenditureConsumption ExpenditureBudget ShareExpenditure CategoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.