Search

Abstract Background The United States (U.S.) has a growing population of Brazilian immigrant women. However, limited research has explored Pap tests and human papillomavirus (HPV) vaccination among this population. Methods Participants completed an online survey between July—August 2020. Bivariate analyses examined associations between healthcare-related variables (e.g., insurance, having a primary care provider) and demographics (e.g., age, education, income, marital status, years living in the U.S., primary language spoken at home) with 1) Pap test recency (within the past 3 years) and 2) HPV vaccination (0 doses vs. 1 + doses). Variables significant at p

IntroductionInformal caregiving is a critical component of the healthcare system despite numerous impacts on informal caregivers’ health and well-being. Racial and gender disparities in caregiving duties and health outcomes are well documented. Place-based factors, such as neighborhood conditions and rural–urban status, are increasingly being recognized as promoting and moderating health disparities. However, the potential for place-based factors to interact with racial and gender disparities as they relate to caregiving attributes jointly and differentially is not well established. Therefore, the primary objective of this study was to jointly assess the variability in caregiver health and aspects of the caregiving experience by race/ethnicity, sex, and rural–urban status.MethodsThe study is a secondary analysis of data from the 2021 and 2022 Behavioral Risk Factor Surveillance System (BRFSS) from the Centers for Disease Control and Prevention. Multivariable logistic regression or Poisson regression models assessed differences in caregiver attributes and health measures by demographic group categorized by race/ethnicity, sex, and rural–urban status.ResultsRespondents from rural counties were significantly more likely to report poor or fair health (23.2% vs. 18.5%), have obesity (41.5% vs. 37.1%), and have a higher average number of comorbidities than urban caregivers. Overall, rural Black male caregivers were 43% more likely to report poor or fair health than White male caregivers (OR 1.43, 95% CI 1.21, 1.69). Urban female caregivers across all racial groups had a significantly higher likelihood of providing care to someone with Alzheimer’s disease than rural White males (p

Abstract 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.

Abstract 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

Abstract Background Informal caregivers providing unpaid assistance may be vulnerable to changes in health behaviors due to modifications in caregiving during the COVID-19 pandemic. Therefore, this cross-sectional study explored self-reported changes in physical activity (PA), sedentary behavior, and screen time among informal caregivers providing care for older adults aged 50+ during the pandemic. Methods Study participants were recruited via Amazon’s Mechanical Turk and reported their perceived changes (increased a lot, increased a little, remained the same, decreased a little, decreased a lot) in moderate-intensity PA (MPA), vigorous-intensity PA (VPA), sedentary behavior, and screen time (weekday and weekend) during the pandemic. For analytic purposes, response categories were categorized into three-level ordinal variables—increased (increased a lot, increased a little), no change (remained the same), decreased (decreased a little, decreased a lot). Multinomial logistic regression models assessed the likelihood of changes (vs. no change) in MPA, VPA, sedentary behavior, and screen time (weekday, weekend) based on caregiving and demographic characteristics. Results In total, 2574 individuals accessed the study link, 464 of whom did not meet eligibility requirements. In addition, people who completed 80% or less of the survey (n = 1171) and/or duplicate IP addresse (n = 104) were excluded, resulting in an analytic sample of n = 835. The sample was 69% male, had a mean age of 34 (SD = 9.7), and 48% reported increased VPA, while 55% reported increased MPA. The majority also reported increased sedentary behavior, as well as increased screen time. Respondents living with their care recipient were more likely to report increased weekday screen time (Odds Ratio [OR] = 1.55, 95% CI 1.11–2.16) and sedentary behavior (OR = 1.80, 95% CI 1.28–2.53) than respondents not living with the care recipient. Those living with their care recipient were also more likely to reported increased MPA (OR = 1.64, 95% CI 1.16–2.32), and VPA (OR = 1.53, 95% CI 1.09–2.15), but also more likely to report a decrease in VPA (OR = 1.75, 95% CI 1.14–2.70). Conclusion The majority of respondents reported that their MPA, VPA PA, sedentary behavior, and screen time had changed during the pandemic. Living with the care recipient was associated with both positive and negative changes in behavior. Future research can explore factors associated with these reported changes in behavior.

Abstract Background Efforts to understand and address the causes of place-based health disparities have focused primarily on understanding the social determinants of health on a large geographic level, such as the region, state, or county. However, there is a growing need to assess and understand how place-based characteristics at smaller geographic areas relate to of local place-based neighborhood characteristics on population health. Therefore, the objective of this study was to evaluate the magnitude of the associations between social determinants of health and life expectancy (LE) and related measures on the community level. Methods LE at birth (LE0), remaining LE at age 65 (LE65), and age-specific mortality rates (ASMR) were calculated from mortality data (2009–2011) collected by the Rhode Island Department of Health (RIDoH) using abridged life table methods for each RI city/town. The city/town-specific LE and ASMR were linked to data collected by the US Census, RIDoH, the Federal Bureau of Investigation, and other databases that include information about multiple social, environmental, and demographic determinants of health. Bivariate correlations between city/town-level LE0, LE65, and ASMR and social determinants: demographics, household composition, income and poverty, education, environment, food insecurity, crime, transportation, and rural-urban status were examined. Results LE0 (range: 75.9–83.3 years) was strongly associated with the percent of the population with a graduate/professional degree (r = 0.687, p

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 (β = 2.6, 95% CI [−4.3, −0.9]) and Asian informal caregivers (β = −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 (β = 0.8, 95% CI [0.6, 1.0]) and SIS (β = 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.

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.

Abstract Although a preponderance of research indicates that increased income inequality negatively impacts population health, several international studies found that a greater income inequality was associated with better population health when measured on a fine geographic level of aggregation. This finding is known as a “Swiss paradox”. To date, no studies have examined variability in the associations between income inequality and health outcomes by spatial aggregation level in the US. Therefore, this study examined associations between income inequality (Gini index, GI) and population health by geographic level using a large, nationally representative dataset of older adults. We geographically linked respondents’ county data from the 2012 Behavioral Risk Factor Surveillance System to 2012 American Community Survey data. Using generalized linear models, we estimated the association between GI decile on the state and county levels and five population health outcomes (diabetes, obesity, smoking, sedentary lifestyle and self-rated health), accounting for confounders and complex sampling. Although state-level GI was not significantly associated with obesity rates (b = − 0.245, 95% CI − 0.497, 0.008), there was a significant, negative association between county-level GI and obesity rates (b = − 0.416, 95% CI − 0.629, − 0.202). State-level GI also associated with an increased diabetes rate (b = 0.304, 95% CI 0.063, 0.546), but the association was not significant for county-level GI and diabetes rate (b = − 0.101, 95% CI − 0.305, 0.104). Associations between both county-level GI and state-level GI and current smoking status were also not significant. These findings show the associations between income inequality and health vary by spatial aggregation level and challenge the preponderance of evidence suggesting that income inequality is consistently associated with worse health. Further research is needed to understand the nuances behind these observed associations to design informed policies and programs designed to reduce socioeconomic health inequities among older adults.

In our retrospective cohort study, we evaluated trends in pharmacist-administered pediatric influenza vaccination rates in the United States and corresponding state-level pharmacist pediatric vaccination authorization models, including minimum age requirements, vaccination protocols, and/or prescription requirements. An administrative health claims database was used to capture influenza vaccinations in children less than 18 years old with 1 year of continuous enrollment and joinpoint regression was used to assess trends. Of the 3,937,376 pediatric influenza vaccinations identified over the study period, only 3.2% were pharmacist-administered (87.7% pediatrician offices, 2.3% convenience care clinics, 0.8% emergency care, and 6.0% other locations). Pharmacist-administered pediatric influenza vaccination was more commonly observed in older children (mean age 12.65 ± 3.26 years) and increased significantly by 19.2% annually over the study period (95% confidence interval 9.2%-30.2%, p < 0.05). The Northeast, with more restrictive authorization models, represented only 2.2% (n = 2816) of all pharmacist-administered pediatric influenza vaccinations. Utilization of pharmacist-administered pediatric influenza vaccination remains low. Providing children with greater access to vaccination with less restrictions may increase overall vaccination rates. Due to the COVID-19 pandemic and the Public Readiness and Emergency Preparedness Act, pharmacists will play a major role in vaccinating children.

The COVID-19 pandemic has had a dramatic impact on global economies and societies. Although social distancing policies are needed to contain the spread and impact of COVID-19, they also impose a psychological and economic burden on people who are already experiencing increased distress such as caregivers. Yet, few measures have been developed and validated to measure the psychosocial impact of COVID-19. Utilizing item response theory (IRT), the purpose of this study was to develop and psychometrically validate a measure of psychosocial functioning—the Psychosocial Functioning during COVID-19 (PFC-19) Questionnaire—to assess changes in social interaction, mental health, health behavior, and global functioning among a sample of informal caregivers during the COVID-19 pandemic. The analytic sample ( n = 733) was recruited from Amazon Mechanic Turk (MTurk) (69% male, 55% white). Results suggest a two-factor measure, assessing global functioning (14 items) and affective response (8 items), with strong evidence for reliability, validity, and dimensionality. Future research should replicate this factor structure in other samples.

This study explored self-reported changes in caregiving intensity (CI) and caregiver burden (CB) among informal caregivers due to the COVID-19 pandemic overall and by gender. Informal caregivers for someone age 50+ completed a survey via Amazon’s MTurk in June 2020. Participants reported changes in CI and CB due to COVID-19 and provided demographic information. Multinomial logistic regression models assessed changes in CI and CB attributed to the COVID-19 pandemic overall and by gender. The sample ( n = 835) was 68.5% male and had an average age of 34 years ( SD 9.8); 55.7% had increased CI, and 53.1% had increased CB attributed to the pandemic. Increased CB due to COVID-19 was associated with increased CI (OR 5.67, 95% CI 3.92–8.00). Male caregivers with decreased CI due to COVID-19 were nearly seven times as likely as those with no change in CI to have reduced CB due to COVID-19 (OR 6.91, 95% CI 3.29–14.52). Women with decreased CI due to COVID-19 were over eight times as likely to have reduced CB due to COVID (OR 8.30, 95% CI 2.66–25.91). Results indicate that many caregivers experienced increases in CI and CB since the start of the COVID-19 pandemic, and that these changes are complex and vary by gender.

Objectives To determine if adults with overweight or obesity received counselling from their healthcare providers (HCPs) to lose weight and/or adopt healthful behaviours associated with weight loss, and whether they took action on their HCPs’ recommendations.Design Cross-sectional analysis of 2011–2018 National Health and Nutrition Examination Survey (NHANES) data.Sample NHANES respondents aged 18+ who were overweight/obese and had seen an HCP in the previous 12 months (n=13 158).Methods Respondents reported if their HCPs recommended they control/lose weight, increase exercise/physical activity (PA) and/or reduce fat/calorie intake, and if they adopted the offered recommendation(s). Weighted logistic regression models examined receipt of HCP counselling by sex, age, race/ethnicity, and weight status accounting for demographic characteristics and complex sampling. Similar analyses examined reported adoption of HCPs’ recommendations.Results The sample was 53.1% women, 45.0% were overweight and 55.0% had obesity. In total, 40.4% received counselling to control/lose weight, 49.5% to increase exercise/PA and 38.9% to reduce fat/calorie intake. The following groups were less likely (p

Abstract Background The relationship between informal caregiving intensity and caregiver health is well-established, though research suggests this may vary by caregiver demographics. The aim of this exploratory study is to assess the association between caregiving intensity and three dimensions of quality of life outcomes, and determine how caregiver sociodemographics change the nature of this relationship among informal adult children caregivers. Methods Using the 2011 National Study of Caregiving, associations between caregiving intensity and quality of life were examined in caregivers providing care to an aging parent (n = 1014). Logistic regression was used to model caregiver quality of life on caregiving intensity using an ordinal composite measure of caregiving activities, including Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL), hours per month, and length of caregiving, stratified by race/ethnicity, gender, age, and family income. Odds ratios and corresponding 95% confidence intervals were calculated. Results Associations between caregiving intensity and quality of life varied substantially by race/ethnicity, gender, age, and annual family income. White caregivers were significantly more likely to experience negative emotional burden when providing high intensity care (ADL: 1.92, Hours: 3.23). Black caregivers were more likely to experience positive emotions of caregiving (ADL: 2.68, Hours: 2.60) as well as younger caregivers (Hours: 8.49). Older caregivers were more likely to experience social burden when providing high ADL, IADL, and monthly hours of care. Conclusions These findings demonstrate the complex and multi-dimensional nature of caregiving, and emphasize the need to develop approaches that are tailored to the specific health needs of subpopulations of informal caregivers.

Few studies have focused on understanding how sociodemographic factors impact healthy ageing in the rapidly growing population of Alaskan older adults. Therefore, the objectives of this study are to compare the health of Alaskan older adults to those in the contiguous US, and determine how the associations differ between older adults in Alaska and the contiguous US. We abstracted 165,295 respondents age 65+ from the 2016 Behavioral Risk Factor Surveillance System. We used generalised linear models to assess the associations between sociodemographic factors and six health outcomes accounting for confounders and complex sampling. In the contiguous US, females were less likely than males to be obese (OR 0.96, 95%CI 0.96–0.97), while in Alaska, females were more likely to be obese (OR 1.24, 95%CI 1.19–1.29). In the contiguous US, Alaska Natives/American Indians were more likely than respondents of other races to be smokers (OR 1.62, 95%CI 1.60–1.63), while in Alaska, the association between race and smoking was not significant (OR 1.00, 95%CI 0.94–1.06). These differences between Alaska and the contiguous US results suggest that programs designed to reduce disparities and promote healthy behaviours may need to be tailored to meet the unique needs and challenges of older adults living in Alaska.

Background: From 1970 to 2010, the Alaskan population increased from 302,583 to 698,473. During that time, the growth rate of Alaskan seniors (65+) was 4 times higher than their national counterparts. Ageing in Alaska requires confronting unique environmental, sociodemographic and infrastructural challenges, including an extreme climate, geographical isolation and less developed health care infrastructure compared to the continental US. Objective: The objective of this analysis is to compare the health needs of Alaskan seniors to those in the continental US. Design: We abstracted 315,161 records of individuals age 65+ from the 2013 and 2014 Behavioral Risk Factor Surveillance System, of which 1,852 were residents of Alaska. To compare residents of Alaska to residents of the 48 contiguous states we used generalized linear models which allowed us to adjust for demographic differences and survey weighting procedures. We examined 3 primary outcomes – general health status, health care coverage status and length of time since last routine check-up. Results: Alaskan seniors were 59% less likely to have had a routine check-up in the past year and 12% less likely to report excellent health status than comparable seniors in the contiguous US. Conclusions: Given the growth rate of Alaskan seniors and inherent health care challenges this vulnerable population faces, future research should examine the specific pathways through which these disparities occur and inform policies to ensure that all US seniors, regardless of geographical location, have access to high-quality health services.

The association between adolescents’ weight perception and their physical activity (PA) and sedentary behaviors remains unclear. Therefore, these associations were explored using data from 2438 adolescents aged 12–19 years who participated in the National Health and Nutrition Examination 2011–2014 Survey. Respondents reported weight perception, and their weight perception accuracy was determined by examining whether the measured weight and perceived weight were concordant. Respondents also reported sedentary time (sitting time and screen time), PA, and intention to lose weight. Linear and logistic regression models were conducted to determine whether adolescents’ PA, sedentary behaviors, and weight loss intention differed by weight perception and weight perception accuracy adjusted for demographic variables accounting for complex sampling. About one-quarter (21.4%) of the respondents had obesity. For respondents who perceived themselves as being overweight/fat, despite greater weight loss intention, males reported more sitting time (512.7 ± 16.3 versus 474.1 ± 10.2 minutes/day, p

Introduction: Rural populations face numerous barriers to health, including poorer health care infrastructure, access to care, and other sociodemographic factors largely associated with rurality. Multiple measures of rurality used in the biomedical and public health literature can help assess rural-urban health disparities and may impact the observed associations between rurality and health. Furthermore, understanding what makes a place truly rural versus urban may vary from region to region in the United States.Purpose: The objectives of this study are to compare and contrast five common measures of rurality and determine how well-correlated these measures are at the national, regional, and divisional level, as well as to assess patterns in the correlations between the prevalence of obesity in the population aged 60+ and each of the five measures of rurality at the regional and divisional level.Methods: Five measures of rurality were abstracted from the US Census and US Department of Agriculture (USDA) to characterize US counties. Obesity data in the population aged 60+ were abstracted from the Behavioral Risk Factor Surveillance System (BRFSS). Spearman’s rank correlations were used to quantify the associations among the five rurality measurements at the national, regional, and divisional level, as defined by the US Census Bureau. Geographic information systems were used to visually illustrate temporal, spatial, and regional variability. Results: Overall, Spearman’s rank correlations among the five measures ranged from 0.521 (percent urban-Urban Influence Code) to 0.917 (Rural-Urban Continuum Code-Urban Influence Code). Notable discrepancies existed in these associations by Census region and by division. The associations between measures of rurality and obesity in the 60+ population varied by rurality measure used and by region. Conclusion: This study is among the first to systematically assess the spatial, temporal, and regional differences and similarities among five commonly used measures of rurality in the United States. There are important, quantifiable distinctions in defining what it means to be a rural county depending on both the geographic region and the measurement used. These findings highlight the importance of developing and selecting an appropriate rurality metric in health research.