Geographic differences in the magnitude of black-white disparities in having obesity.

Background: Obesity disparities in the United States are well documented, but the limited body of research suggests that geographic factors may alter the magnitude of these disparities. A growing body of evidence has identified a "rural mortality penalty" where morbidity and mortality rates are higher in rural than urban areas, even after controlling for other factors. Black-White differences in health and mortality are more pronounced in rural areas than in urban areas. Objective: Therefore, the purpose of this study was to explore how rural-urban status and region moderate Black-White health disparities in obesity. Methods: Data were abstracted from the 2012 Behavioral Risk Factor Surveillance System, with the sample being restricted to Black and White respondents (n = 403,231). Respondents' county of residence was linked to US Census information to obtain the county-level Index of Relative Rurality (IRR) and Census division. Crude and adjusted logistic regression models were utilized to assess the magnitude of Black-White disparities in having obesity (yes/no) by IRR quartile and by Census division. Results: Overall, Black-White differences in obesity were wider in rural than in urban counties, with a significant linear trend (p < 0.001). Furthermore, when stratified by US Census division, results revealed that disparities were significantly wider in rural than urban areas for respondents living in the Middle Atlantic and South Atlantic divisions. In contrast, the association was reversed for the remaining divisions (New England, East North Central, West North Central, Mountain, and Pacific), where the magnitude of the Black-White difference was the largest in urban areas. Conclusion: Findings highlight the need to understand and account for critical place-based factors that exacerbate racial obesity disparities to develop and maximize the effectiveness of policies and programs designed to reduce racial inequalities and improve population health.

Income and rural-urban status moderate the association between income inequality and life expectancy in US census tracts.

BACKGROUND: A preponderance of evidence suggests that higher income inequality is associated with poorer population health, yet recent research suggests that this association may vary based on other social determinants, such as socioeconomic status (SES) and other geographic factors, such as rural-urban status. The objective of this empirical study was to assess the potential for SES and rural-urban status to moderate the association between income inequality and life expectancy (LE) at the census-tract level. METHODS: Census-tract LE values for 2010-2015 were abstracted from the US Small-area Life Expectancy Estimates Project and linked by census tract to Gini index, a summary measure of income inequality, median household income, and population density for all US census tracts with non-zero populations (n = 66,857). Partial correlation and multivariable linear regression modeling was used to examine the association between Gini index and LE using stratification by median household income and interaction terms to assess statistical significance. RESULTS: In the four lowest quintiles of income in the four most rural quintiles of census tracts, the associations between LE and Gini index were significant and negative (p between < 0.001 and 0.021). In contrast, the associations between LE and Gini index were significant and positive for the census tracts in the highest income quintiles, regardless of rural-urban status. CONCLUSION: The magnitude and direction of the association between income inequality and population health depend upon area-level income and, to a lesser extent, on rural-urban status. The rationale behind these unexpected findings remains unclear. Further research is needed to understand the mechanisms driving these patterns.

Black/White Disparities in Obesity Widen with Increasing Rurality: Evidence from a National Survey.

Background: Racial health disparities in obesity and obesity-related conditions and behaviors are well documented, although a small body of research suggests that geographic factors (e.g., socioeconomic status [SES] and rural/urban status) may alter the magnitude of these disparities. Methods: This study explored how rurality moderates black/white health disparities using a nationally representative sample from the 2012 Behavioral Risk Factor Surveillance System (n=359,157). Respondents' county of residence was linked to the U.S. Census information to obtain the county-level Index of Relative Rurality (IRR). Weighted logistic regression was used to model obesity, diabetes, and lack of physical activity (PA) on race (black/white), IRR, and an interaction term of race and IRR, including covariates (age, sex, education, marital status, employment, and income). Results: Blacks were significantly more likely to have obesity, diabetes, and a lack of PA compared with whites. Irrespective of race, rural respondents were significantly more likely to have obesity (odds ratio [OR] 1.035, confidence interval [95% CI] 1.028-1.043) and a lack of PA (OR 1.045, 95% CI 1.038-1.053) than respondents in more urban areas. For obesity and diabetes, the interaction term for black×IRR quintile was significant and positive, indicating an increase in the magnitude of the black/white disparity with increasing rurality. Discussion: These findings underscore the need for policies and programs aimed to reduce racial disparities in obesity and related conditions to consider the geographic context in which these outcomes occur.

Sleep Quality Among Informal Caregivers During the COVID-19 Pandemic: A Cross-Sectional Study.

Sleep is an integral component of health. The impact of the COVID-19 pandemic on sleep quality among informal caregivers, individuals who provide unpaid care or assistance to family members or friends, assisting older adults is not well understood. Therefore, informal caregivers in the United States providing care for individuals aged 50+ were recruited via Amazon's Mechanical Turk, an online platform for enrolling study participants into social and behavioral science research, to complete an online survey. The sample of informal caregivers (n = 835) was 69% male and 55% non-Hispanic. Multivariable linear regression models were constructed to assess the associations between sleep disturbance scores (SDS) and sleep-related impairment scores (SIS) and caregiving-related measures (hours caregiving/week, length of time spent caregiving, and caregiver burden), demographics, and region of the United States. The analysis determined that Black (ß = 2.6, 95% CI [-4.3, -0.9]) and Asian informal caregivers (ß = -1.8, 95% CI [-3.4, -0.3]) had lower mean SIS than White caregivers, the referent group. In addition, increasing caregiver burden was associated with increased SDS (ß = 0.8, 95% CI [0.6, 1.0]) and SIS (ß = 1.3, 95% CI [0.7, 1.6]). In conclusion, higher caregiver burden was associated with higher SIS and SDS, suggesting that informal caregivers' sleep should be assessed, and when needed interventions should be offered.

Rural-Urban Differences in Caregiver Burden Due to the COVID-19 Pandemic among a National Sample of Informal Caregivers.

The objective of this exploratory study was to explore potential associations between changes to caregiver burden (CB) due to the COVID-19 pandemic and rural-urban status using a nationally representative sample of 761 informal caregivers. Tertiles of two measures of rural-urban status were used: Rural-Urban Commuting Areas (RUCAs) and population density. Bivariate and multivariable binary and ordinal logistic regression were used to asses study objectives. Using RUCAs, rural informal caregivers were more than twice as likely as urban informal caregivers to report a substantial increase in CB due to COVID-19 (OR 2.27, 95% CI [1.28-4.02]). Similar results were observed for population density tertiles (OR 2.20, 95% CI [1.22-3.96]). Having a COVID-19 diagnosis was also significantly associated with increased CB. Understanding and addressing the root causes of rural-urban disparities in CB among informal caregivers is critical to improving caregiver health and maintaining this critical component of the healthcare system.