Search Results

Methods: Using 11 years of data from the National Longitudinal Survey of Youth 1997 (1997–2007) and 2000 Census data, the authors performed three-level hierarchical linear regression to examine the associations between hypersegregation, race and a sexual risk behaviour index among black and white non-Hispanic adolescents as they transition to adulthood.

Results: Through most of the teenage years, African–Americans are at higher sexual risk than Caucasians. However, by age 19, Caucasians are at higher risk. Hypersegregation was not associated with increased sexual risk index score on average and did not impact the trajectory of the race–sexual risk association.

Conclusions: The authors did not find any evidence that hypersegregation was associated with the sex risk index or that it modified the race–sex risk association as individuals got older. Future studies should examine whether segregation is associated with other causes of STI/HIV acquisition risk, such as sexual network patterns.

Methods: Young adults born between 1980 and 1990 who participated in the Young Adult file of the 1979 National Longitudinal Study of Youth were examined (N=3901). The accumulation of childhood poverty is captured via poverty exposure from each survey year from the prenatal year through age 18 years. Body mass index was calculated and categorised into the reference criteria for adults outlined by the Center for Disease Control. Logistic regression models were stratified by race/ethnicity and included a term interacting poverty and gender, along with a number of covariates, including various longitudinal socioeconomic status measures and indicators for the intergenerational transmission of economic disadvantage and body weight.

Results: Reoccurring exposure to childhood poverty was positively related to OV/OB for white, black and Hispanic young adult women and inversely related for white young adult men. A direct relationship between the accumulation of childhood poverty and OV/OB was not found for black and Hispanic young adult men.

Conclusions: Helping families move out of poverty may improve the long-term health status of white, black and Hispanic female children as young adults. Community area interventions designed to change impoverished community environments and assist low-income families reduce family level correlates of poverty may help to reduce the weight disparities observed in young adulthood.

Method: We used National Longitudinal Study of Youth 1979 data and multivariate logistic regression models to examine the relationship between changes in health limitations and chronic conditions across survey waves and risk of mortgage default and foreclosure.

Results: We found that changes in health limitations and chronic conditions increased the risk of default and foreclosure between 2007 and 2010. These associations were partially mediated by changes in family income and loss of health insurance.

Conclusions: From a policy perspective, the strong link between the onset of illness and foreclosure suggests a need to re-examine the safety-nets that are available to individuals who become ill or disabled.

Methods: Using a longitudinal dataset of almost 16,000 neighbourhoods, we examine the relationship between neighbourhood disadvantage and functional disability. Leveraging a series of cross-lagged panel models, we account for reciprocal dynamics and a range of pertinent covariates while assessing differences across age- and sex-specific groups.

Results: Accounting for reciprocal effects, we found that the association between concentrated disadvantage and functional disability varies across age and sex groups. Concentrated disadvantage is most consistently associated with increased functional disability among boys (5–17 years), young men (18–34 years) and middle-aged men (35–64 years). Similar associations are found among girls (5–17 years) and middle-aged women (35–64 years).

Conclusion: Local neighbourhood economic conditions are significantly associated with functional disability among relatively young populations of males and females. Exposure to neighbourhood disadvantage and deprivation may accelerate disablement processes and shift the age curve of disability risk. social disinvestment

Methods: Relative inequality (risk ratios, RR) and absolute inequality (Slope Index of Inequality) were estimated for ALCHC during later childhood by maternal education categories and household income quintiles in early childhood. Estimates were adjusted for mother ethnicity, maternal age at birth, child sex and multiple births, and were pooled using meta-regression.

Results: Pooled estimates, with over 42 000 children, demonstrated social gradients in ALCHC for high maternal education versus low (RR 1.54, 95% CI 1.28 to 1.85) and middle education (RR 1.24, 95% CI 1.11 to 1.38); as well as for high household income versus lowest (RR 1.90, 95% CI 1.66 to 2.18) and middle quintiles (RR 1.34, 95% CI 1.17 to 1.54). Absolute inequality showed decreasing ALCHC in all cohorts from low to high education (range: −2.85% Sweden, −13.36% Canada) and income (range: −1.8% Sweden, −19.35% Netherlands).

Methods: A multiple regression analysis was used to analyse changes in BMI over a 4-year period using the National Longitudinal Survey of Youth. In all, 4890 US children, aged 2–16 at baseline, were included in the analysis.

Results: As compared with children of mothers who were employed full-time and did not receive welfare, children of mothers who experienced unemployment and received unemployment benefits were not more likely to have significantly different changes in BMI. Yet children of mothers who experienced unemployment and did not receive unemployment benefits were significantly more likely to have greater increases in BMI. These results were also shown in models which controlled for height. This supports the conclusion that adiposity changes, and not simply growth-rate differences, account for the different BMI changes between groups.

Conclusion: Aspects of maternal employment other than number of work hours are associated with child BMI, including unemployment events and what type of support a mother receives during the time of unemployment. This has implications for policies that relate to benefits for mothers who lose their jobs.

Methods: Data come from the US National Longitudinal Survey of Youth 1997. The main analytic sample includes 4714 respondents who were ever enrolled in college and who reported on sleep duration in 2010. Most respondents had completed their college education by 2010, when respondents were 25 to 31 years old. Multivariable linear regression models assessed the cross-sectional association between student loans accumulated over the course of college and sleep duration in 2010, as well as between student debt at age 25 and sleep duration in 2010.

Results: Black young adults with greater amounts of student loans or more student debt reported shorter sleep duration, controlling for occupation, hours worked, household income, parental net worth, marital status, number of children in the household and other sociodemographic and health indicators. There was no association between student loans or debt with sleep for white or latino adults and other racial/ethnic groups.

Conclusions: Student loans may contribute to racial inequities in sleep duration. Our findings also suggest that the student debt crisis may have important implications for individuals’ sleep, specifically and public health, more broadly.

Setting: The US population in the year 2000.

Participants: 4817 US adults about age 40, representative of the US population.

Main outcome measures: Two outcomes were studied: self reported general health status, dichotomised as \"fair\" or \"poor\" compared with \"excellent\", \"very good\", or \"good\", and depression as measured by a score on the Center for Epidemiologic Studies depression instrument >16.

Results: State generosity was significantly associated with a reduced odds of reporting poor general health (OR 0.84, 95%CI: 0.71 to 0.99), and the county unemployment rate with reduced odds of reporting depression (OR 0.91, 95%CI: 0.84 to 0.97). The measure of income inequality is a significant risk factor for reporting poor general health (OR 1.98, CI: 1.08 to 3.62), controlling for all ecological and individual covariates. In stratified models, the index of social capital is associated with reduced odds of reporting poor general health among black people and Hispanics (OR 0.40, CI: 0.18 to 0.90), but not significant among white people. The inequality measure is significantly associated with reporting poor general health among white people (OR 2.60, CI: 1.22 to 5.56) but not black people and Hispanics.

Conclusions: The effect of income inequality on health may work through the influence of invidious social comparisons (particularly among white subjects) and (among black subjects and Latinos) through a reduction in social capital. Researchers may find it fruitful to recognise the cultural specificity of any such effects.