Obesity Prevalence Among Adults Living in Metropolitan and Nonmetropolitan Counties - United States, 2016

Trends in Alzheimer's-Related Mortality Among Type 2 Diabetes Patients in the United States: 1999-2019

BACKGROUND

Recent research has shown that type 2 diabetes mellitus (T2DM) has increased the burden of Alzheimer's disease (AD) in the US aging population. However, trends in mortality from this comorbidity among adults aged ≥ 65 years have not been investigated.

OBJECTIVES

This study examined trends and disparities in AD-related mortality among older US adults with T2DM from 1999 to 2019.

METHODS

Data from the CDC WONDER database were analysed to assess AD-related mortality in patients with T2DM aged ≥ 65 between 1999 and 2019. Age-adjusted mortality rates (AAMRs) per 100,000 people and annual percent change (APC) were calculated and stratified by year, sex, race/ethnicity, age, urbanisation and geographical region.

RESULTS

From 1999 to 2019, there were 71,550 deaths with T2DM and AD among adults aged ≥ 65. AAMRs rose from 4.12 in 1999 to 11.65 in 2019, with the sharpest increase between 2014 and 2017 (APC: 10.81; 95% CI: -3.20 to 13.43). Women had slightly higher AAMRs than men, with rates increasing from 4.71 in 1999 to 11.61 in 2019 for women, and from 4.08 to 11.70 for men. Hispanic individuals saw the highest increase in AAMR (11.15), followed by non-Hispanic Black (9.30) and White populations (7.92). AAMRs were highest in the West (10.91) and the Midwest (9.62), while the Northeast (4.70) had the lowest. Nonmetropolitan areas had consistently higher AAMRs (10.74) than large metropolitan areas (6.68) and small/medium metropolitan areas (9.25). States in the top 90th percentile for T2DM-AD mortality included California, South Dakota and Kentucky, where rates were approximately eight times higher than in states in the lowest 10th percentile.

CONCLUSIONS

This study reveals a significant rise in T2DM-AD comorbidity-related mortality among older adults, especially among Hispanics, women and rural residents. These findings underscore the need for targeted interventions to reduce the burden in vulnerable populations.

Social Networks, Health Support, and Dietary Intake in Mothers Receiving Home Visiting Services

Home visiting programs (HVPs) provide services to pregnant individuals and parents of young children to improve families' health and well-being. However, little is known about these families' social contexts. This study explores the social networks and dietary intake of mothers enrolled in a HVP, focusing on health support and health undermining. Cross-sectional data from 76 mothers enrolled in a HVP in Los Angeles County were collected by interview, using validated measures. Almost all mothers (95.7%) had one or more health supporters, while 55.1% had one or more health underminers. Some key findings related to health support were that mothers with higher income had more health supporters in their network (b = 1.36, p = 0.03), and network members were more likely to be health supporters if they were a romantic partner (OR = 3.41, p < 0.001), a resource-based connection (OR = 3.46, p < 0.01), or if they lived in the same neighborhood as the mother (OR = 1.68, p < 0.05). Further, having a health supporter who lived in the same neighborhood was associated with consuming more (1 + daily servings) vegetables (OR = 3.0, p < 0.05) and no sugar-sweetened beverages (OR = 0.29, p < 0.05). There were fewer findings related to health undermining: network members more likely to be underminers were romantic partners (OR = 8.93, p < .0001), and those perceived as having overweight or obesity (OR = 3.98, p < 0.001), but health undermining did not predict dietary intake. Overall, a broad set of network features were linked with health support, and given that some types of support were linked with better diet, network-based diet interventions leveraging health support may be effective in this priority population.

Developing Components of an Integrated mHealth Dietary Intervention for Mexican Immigrant Farmworkers: Feasibility Usability Study of a Food Photography Protocol for Dietary Assessment

BACKGROUND

Rural-urban disparities in access to health services and the burden of diet-related noncommunicable diseases are exacerbated among Mexican immigrant farmworkers due to work demands, social and geographical isolation, literacy issues, and limited access to culturally and language-competent health services. Although mobile health (mHealth) tools have the potential to overcome structural barriers to health services access, efficacious mHealth interventions to promote healthy eating have not considered issues of low literacy and health literacy, and food preferences and norms in the Mexican immigrant farmworker population. To address this critical gap, we conducted a series of preliminary studies among Mexican immigrant farmworkers with the long-term goal of developing a culture- and literacy-specific smartphone app integrating dietary assessment through food photography, diet analyses, and a non-text-based dietary intervention.

OBJECTIVE

This study aimed to report adherence and reactivity to a 14-day food photography dietary assessment protocol, in which Mexican immigrant farmworker women were instructed to take photos of all foods and beverages consumed.

METHODS

We developed a secure mobile app with an intuitive graphical user interface to collect food images. Adult Mexican immigrant farmworker women were recruited and oriented to the photography protocol. Adherence and reactivity were examined by calculating the mean number of food photos per day over time, differences between the first and second week, and differences between weekdays and weekends. The type of foods and meals photographed were compared with reported intake in three 24-hour dietary recalls.

RESULTS

In total, 16 Mexican farmworker women took a total of 1475 photos in 14 days, with a mean of 6.6 (SD 2.3) photos per day per participant. On average, participants took 1 fewer photo per day in week 2 compared with week 1 (mean 7.1, SD 2.5 in week 1 vs mean 6.1, SD 2.6 in week 2; P=.03), and there was a decrease of 0.6 photos on weekdays versus weekends (mean 6.4, SD 2.5 on weekdays vs mean 7, SD 2.7 on weekends; P=.50). Of individual food items, 71% (352/495) of foods in the photos matched foods in the recalls. Of all missing food items (n=138) and meals (n=36) in the photos, beverages (74/138, 54%), tortillas (15/138, 11%), snacks 16/36, 44%), and dinners (10/36, 28%) were the most frequently missed. Most of the meals not photographed (27/36, 75%) were in the second week of the protocol.

CONCLUSIONS

Dietary assessment through food photography is feasible among Mexican immigrant farmworker women. For future protocols, substantive adjustments will be introduced to reduce the frequency of missing foods and meals. Our preliminary studies are a step in the right direction to extend the benefits of mHealth technologies to a hard-to-reach group and contribute to the prevention and control of diet-related noncommunicable diseases.

Weight status underestimation and weight management goals among adults in the rural South of the United States

PURPOSE

To examine factors associated with weight status underestimation and the relationship between weight status underestimation and weight loss as a weight management goal among adults living in the rural South.

METHODS

An anonymous survey was distributed at six primary care clinics and two churches in rural, South Carolina counties. Weight status underestimation was determined based on the difference between perceived weight status using standard body mass index (BMI) categories (underweight, normal weight, overweight, and obese) and BMI category from self-reported height and weight. Participants reported whether their weight management goal was to lose, maintain, or gain weight. Chi-square and binary logistic regressions were used for data analysis.

FINDINGS

A total of 185 respondents (76% female) at least partially completed the survey. Nearly 60% underestimated their weight status. Increasing BMI was associated with higher odds of weight status underestimation (OR: 1.10, 95%: 1.04, 1.15) and perceptions of being in fair or poor health was associated with lower odds of weight status underestimation relative to perceiving health as good or better (OR: 0.21, 95% CI: 0.06, 0.66). Among those with overweight and obesity, the association between underestimating weight status and having weight loss as a weight management goal was strong but not statistically significant (OR: 0.20, 95% CI: 0.04, 1.04).

CONCLUSIONS

Underestimating weight status was common among adults in the rural US South and was related to BMI and health status. Research is needed to determine if improving the accuracy of weight perceptions can promote weight management in the rural South.

Prevalence of Self-Reported Diagnosed Diabetes Among Adults, by County Metropolitan Status and Region, United States, 2019-2022

Introduction

Previous research suggests that rural-urban disparities in diabetes mortality, hospitalization, and incidence rates may manifest differently across US regions. However, no studies have examined disparities in diabetes prevalence by metropolitan residence and region.

Methods

We used data from the 2019-2022 National Health Interview Survey to compare diabetes status, socioeconomic characteristics, and weight status among adults in each census region (Northeast, Midwest, South, West) according to county metropolitan status of residence (large central metro, large fringe metro, small/medium metro, and nonmetro). We used χ2 tests and logistic regression models to assess the association of metropolitan residence with diabetes prevalence in each region.

Results

Diabetes prevalence ranged from 7.0% in large fringe metro counties in the Northeast to 14.8% in nonmetro counties in the South. Compared with adults from large central metro counties, those from small/medium metro counties had significantly higher odds of diabetes in the Midwest (age-, sex-, and race and ethnicity-adjusted odds ratio [OR] = 1.24; 95% CI, 1.06-1.45) and South (OR = 1.15; 95% CI, 1.02-1.30). Nonmetro residence was also associated with diabetes in the South (OR = 1.62 vs large central metro; 95% CI, 1.43-1.84). After further adjustment for socioeconomic and body weight status, small/medium metro associations with diabetes became nonsignificant, but nonmetro residence in the South remained significantly associated with diabetes (OR = 1.22; 95% CI, 1.07-1.39).

Conclusion

The association of metropolitan residence with diabetes prevalence differs across US regions. These findings can help to guide efforts in areas where diabetes prevention and care resources may be better directed.

School Nursing Workforce Differences Between Urban and Non-Urban Areas: Implications for School Health Services and Equity

The professional experiences of school nurses who work outside of urban areas are not often described. We used data from a 2019 statewide survey of school nurses to describe differences between the urban and non-urban (urban cluster and rural) school nurse workforce in New Mexico. Non-urban school nurses were twice as likely as urban nurses to provide clinical services to multiple school campuses (P < .001) and more likely to serve both elementary and secondary school settings (P = .002). They were less likely than urban school nurses to be bachelor's prepared, or to have received recent continuing education on diabetes (P < .001), reproductive health (P = 0.02), LGBQ+ and transgender student health (P < .001, for each), and suicide risk assessment and screening (P = .012). Our findings underscore concerns about geographic differences in the school nursing workforce in terms of educational preparation and student access that could potentially limit the school nurse role in advancing child health equity.

Longitudinal Exposure to Neighborhood Concentrated Poverty Contributes to Differences in Adiposity in Midlife Women

Background: Neighborhood poverty is associated with adiposity in women, though longitudinal designs, annually collected residential histories, objectively collected anthropometric measures, and geographically diverse samples of midlife women remain limited. Objective: To investigate whether longitudinal exposure to neighborhood concentrated poverty is associated with differences in body mass index (BMI) and waist circumference (WC) among 2,328 midlife women (age 42-52 years at baseline) from 6 U.S. cities enrolled in the Study of Women's Health Across the Nation (SWAN) from 1996 to 2007. Methods: Residential addresses and adiposity measures were collected at approximately annual intervals from the baseline visit through a 10-year follow-up. We used census poverty data and local spatial statistics to identify hot-spots of high concentrated poverty areas and cold-spots of low concentrated poverty located within each SWAN site region, and used linear mixed-effect models to estimate percentage differences (95% confidence interval [CI]) in average BMI and WC levels between neighborhood concentrated poverty categories. Results: After adjusting for individual-level sociodemographics, health-related factors, and residential mobility, compared to residents of moderate concentrated poverty communities, women living in site-specific hot-spots of high concentrated poverty had 1.5% higher (95% CI: 0.6, 2.3) BMI and 1.3% higher (95% CI: 0.5, 2.0) WC levels, whereas women living in cold-spots of low concentrated poverty had 0.7% lower (95% CI: -1.2, -0.1) BMI and 0.3% lower (95% CI: -0.8, 0.2) WC. Site-stratified results remained in largely similar directions to overall estimates, despite wide CIs and small sample sizes. Conclusions: Longitudinal exposure to neighborhood concentrated poverty is associated with slightly higher BMI and WC among women across midlife.

Patient, facility, and environmental factors associated with obesity treatment in US Veterans

Background

Identifying patient-, facility-, and environment-level factors that influence the initiation and retention of comprehensive lifestyle management interventions (CLMI) for urban and rural Veterans could improve obesity treatment and reach at Veterans Affairs (VA) facilities.

Aims

This study identified factors at these various levels that predicted treatment engagement, retention, and weight management among urban and rural Veterans.

Methods

A retrospective cohort study of 631,325 Veterans was designed using VA databases to identify Veterans with class II and III obesity during 2015-2017. Primary outcomes were initiation of CLMI, bariatric surgery, or obesity pharmacotherapy within 1 year of index date. Secondary outcomes included treatment retention and successful weight loss. Generalized linear mixed models were used to evaluate the relationships between factors and obesity-related outcomes, with rurality differences assessed through interaction terms.

Results

Patient characteristics associated with increased odds of initiating CLMI included female sex (p < 0.001), black race (p < 0.001), sleep apnea (p < 0.001), mood disorder (p < 0.001), and use of medications associated with weight loss (p < 0.001) or weight gain (p < 0.001). Facility use of telehealth was associated with greater odds of CLMI initiation in urban Veterans (p < 0.001) but lower retention in both populations (p = 0.003). Routine consideration of pharmacotherapy was associated with higher CLMI initiation. Environmental characteristics associated with increased odds of CLMI initiation included percent of population foreign born (OR = 1.03 per 10% increase; p < 0.001), percent black (p < 0.001), and high walkability index (p < 0.001). The relationship between total population and CLMI initiation differed by rurality, as greater population was associated with lower odds of CLMI initiation in urban areas (OR: 0.99 per 1000 population; p < 0.001), but higher odds in rural areas (OR:1.01, p = 0.01). Veterans in the south were less likely to initiate CLMI and had lower retention (p < 0.001).

Conclusion

Treatment and retention of CLMI among Veterans remain low, highlighting areas for improvement to expand its reach both urban and rural Veterans.

New Body Mass Index Cut-Off Point for Obesity Diagnosis in Young Thai Adults

Obesity is a global health threat affecting people of all ages, especially young adults. Early diagnosis of obesity allows for effective treatments and the prevention of its consequences. This study aimed to determine the prevalence of obesity in Thai young adults, evaluate the extent to which BMI values indicate excess adiposity, and identify the most appropriate BMI diagnostic cut-point based on body fat percentage. The study included 186 young adults aged 20 to 35 years. The diagnosis of obesity using body mass index (BMI) was compared with dual-energy X-ray absorptiometry-derived body fat percentage, considered the gold standard. The appropriate BMI cut-point was established using ROC curve analysis and the Youden index. Obesity was more common in women and in urban areas. BMI and body fat were significantly correlated; however, there was a high rate of false-negative obesity diagnosis based on the conventional BMI cut-off, a condition known as normal weight obesity (NWO). The newly proposed BMI cut-off points that best correlated with body fat and corrected false negatives were 22.1 kg/m2 for men and 22.5 kg/m2 for women. These new BMI cut-points should be applied together with clinical evaluations for obesity assessment in this particularly high-risk group.

Estimating the Impact of Rurality in Disparities in Cancer Mortality

PURPOSE

Estimation of the independent effect of rurality on cancer mortality requires causal inference methodology and consideration of area-level socioeconomic status and rural designations.

METHODS

Using SEER data, we identified key incident cancers diagnosed between 2000 and 2016 at age ≥20 years (N = 3,788,273), examining a 20% random sample (n = 757,655). Standardized competing risk and survival models estimated the association between rural residence, defined by Rural-Urban Continuum Codes, and cancer-specific and all-cause mortality, controlling for age at cancer diagnosis, sex, race/ethnicity, year of diagnosis, and Area Deprivation Index (ADI). We estimated the attributable fraction (AF) of rurality and high ADI (ADI > median) to the probability of mortality. Finally, we examined county measurement issues contributing to mortality rates discordant from hypothesized rates.

RESULTS

The 5-year standardized failure probability for cancer mortality for rural patients was 33.9% versus 31.56% for urban. The AF for rural residence was 1.04% at year 1 (0.89% by year 5), the highest among local stage disease (Y1 2.1% to Y5 1.9%). The AF for high ADI was 3.33% in Y1 (2.87% in Y5), while the joint effect of rural residence and high ADI was 4.28% in Y1 (3.71% in Y5). Twenty-two percent of urban counties and 30% of rural were discordant. Among discordant urban counties, 30% were only considered urban because of adjacency to metro area. High ADI was associated with urban discordance and low ADI with rural discordance.

CONCLUSION

Rural residence independently contributes to cancer mortality. The rural impact is the greatest among those with localized disease and in high deprivation areas. Rural-urban county designations may mask high-need urban counties, limiting eligibility to state and federal resources dedicated to rural areas.

Contribution of County Characteristics to Disparities in Rural Mortality After Cancer Diagnosis

INTRODUCTION

Rural disparities in cancer outcomes have been widely evaluated, but limited evidence is available to describe what characteristics of rural environments contribute to the increased risk of poor outcomes. Therefore, this manuscript sought to assess the mediating effects of county characteristics on the relationship between urban/rural status and mortality among patients with cancer, characterize county profiles, and determine at-risk county profiles alongside rural settings.

METHODS

Patients diagnosed with cancer between 2000 and 2016 were assessed using Surveillance, Epidemiology and End Results data linked to the 2010 Rural-Urban Commuting Codes and 2010 County Health Rankings. There were 757,655 patients representing 596 counties (of 3,143 in the U.S.) and 12 states. Mediation analyses, conducted in 2023, estimated the direct contribution of rurality to 5-year all-cause survival and the contribution of the rural effect indirectly through County Health Ranking domains. Latent class analysis and survival models identified county groupings and estimated the hazard of mortality associated with class membership.

RESULTS

Rankings for premature death, clinical care, and physical environment resulted in rural patients having 17.9%-20.2% less survival time than urban patients. Of this, 4.1%-12.6% of the total excess risk was mediated by these characteristics. Patients living in rural and high-risk county classes saw higher all-cause mortality than those in urban lower-risk counties (hazard ratio=1.04, 95% CI=1.01, 1.08 and 1.07, 95% CI=1.03, 1.11).

CONCLUSIONS

Counties with poorer health rankings had increased mortality risks regardless of rurality; however, the poor rankings, notably health behaviors and social and economic factors, elevated the risk for rural counties.

Using dialogue-centered approaches to community-engaged research: an application of dialectical inquiry

Rural communities across the United States experience increased risk and prevalence of chronic diseases associated with both individual and community-based factors. Thus, there is a need for rural capacity development for chronic disease prevention. Traditional health promotion and intervention approaches often focus on diet-related health disparities from a positivist, evidence-based paradigm. To counter positivist bias within health promotion research, a hybridized approach is proposed using a critical-constructivist paradigm incorporating dialectical thinking, appreciative inquiry, and dialectical inquiry to address cultural and structural barriers, as well as community-based social norms, through evaluation of community-based health promotion interventions. Three dialectical models were identified through interviews with community coalition members: social ties, infrastructure, and worldviews, examining underlying assumptions and counter assumptions. By revealing the dialectic assumptions and counter assumptions within project implementation, practitioners can engage in constructive dialogue with communities to determine more effective and culturally responsive pathways for project development.

Physical Activity According to Diabetes and Metropolitan Status, U.S., 2020 and 2022

INTRODUCTION

Physical activity can reduce morbidity and mortality among adults with diabetes. Although rural disparities in physical activity exist among the general population, it is not known how these disparities manifest among adults with diabetes.

METHODS

Data from the 2020 and 2022 National Health Interview Survey were analyzed in 2023 to assess the prevalence of meeting aerobic and muscle-strengthening recommendations according to the 2018 Physical Activity Guidelines for Americans during leisure time. Physical activity prevalence was computed by diabetes status, type of physical activity, and urban/rural residence (large central metropolitan, large fringe metropolitan, medium/small metropolitan, and nonmetropolitan). Logistic regression models were used to estimate prevalence and prevalence ratios of meeting physical activity recommendations by urban/rural residence across diabetes status.

RESULTS

Among adults with diabetes in nonmetropolitan counties, only 23.8% met aerobic, 10.9% met muscle-strengthening, and 6.2% met both physical activity recommendations. By contrast, among adults with diabetes in large fringe metropolitan counties, 32.1% met aerobic, 19.7% met strengthening, and 12.0% met both guidelines. Multivariable adjusted prevalence of meeting muscle-strengthening recommendations was higher among participants with diabetes in large fringe metropolitan than among large central metropolitan counties (prevalence ratio=1.27; 95% CI=1.03, 1.56). Among those without diabetes, adjusted prevalence of meeting each recommendation or both was lower in nonmetropolitan and small/medium metropolitan than in large central metropolitan counties.

CONCLUSIONS

Adults with diabetes are less likely to meet the physical activity recommendations than those without, and differences exist according to urban/rural status. Improving physical activity among rural residents with diabetes may mitigate disparities in diabetes-related mortality.

Patterns, Prognostic Implications, and Rural-Urban Disparities in Optimal GDMT Following HFrEF Diagnosis Among Medicare Beneficiaries

BACKGROUND

Patterns and disparities in guideline-directed medical therapy (GDMT) uptake for heart failure with reduced ejection fraction (HFrEF) across rural vs urban regions are not well described.

OBJECTIVES

This study aims to evaluate patterns, prognostic implications, and rural-urban differences in GDMT use among Medicare beneficiaries following new-onset HFrEF.

METHODS

Patients with a diagnosis of new-onset HFrEF in a 5% Medicare sample with available data for Part D medication use were identified from January 2015 through December 2020. The primary exposure was residence in rural vs urban zip codes. Optimal triple GDMT was defined as ≥50% of the target daily dose of beta-blockers, ≥50% of the target daily dose of angiotensin-converting enzyme inhibitors/angiotensin receptor blocker or any dose of sacubitril/valsartan, and any dose of mineralocorticoid receptor antagonist. The association between the achievement of optimal GDMT over time following new-onset HFrEF diagnosis and risk of all-cause mortality and subsequent HF hospitalization was also evaluated using adjusted Cox models. The association between living in rural vs urban location and time to optimal GDMT achievement over a 12-month follow-up was assessed using cumulative incidence curves and adjusted Fine-Gray subdistribution hazard models.

RESULTS

A total of 41,296 patients (age: 76.7 years; 15.0% Black; 27.6% rural) were included. Optimal GDMT use over the 12-month follow-up was low, with 22.5% initiated on any dose of triple GDMT and 9.1% on optimal GDMT doses. Optimal GDMT on follow-up was significantly associated with a lower risk of death (HR: 0.89 [95% CI: 0.85-0.94]; P < 0.001) and subsequent HF hospitalization (HR: 0.93 [95% CI: 0.87-0.98]; P = 0.02). Optimal GDMT use at 12 months was significantly lower among patients living in rural (vs urban) areas (8.4% vs 9.3%; P = 0.02). In adjusted analysis, living in rural (vs urban) locations was associated with a significantly lower probability of achieving optimal GDMT (HR: 0.92 [95% CI: 0.86-0.98]; P = 0.01 Differences in optimal GDMT use following HFrEF diagnosis accounted for 16% of excess mortality risk among patients living in rural (vs urban) areas.

CONCLUSIONS

Use of optimal GDMT following new-onset HFrEF diagnosis is low, with substantially lower use noted among patients living in rural vs urban locations. Suboptimal GDMT use following new-onset HFrEF was associated with an increased risk of mortality and subsequent HF hospitalization.

The Association Between Physical Activity, Obesity, and Cognition in Middle-Aged and Older Adults

As rates of obesity continue to rise, so does the impact of obesity on cognitive function. Engaging in physical activity is one pathway through which individuals can help maintain cognitive function. This study examined whether any link between exercise and cognitive function was associated with weight characteristics. Data from 6,012 participants in the Health and Retirement Study were used. The association between participation in light or moderate physical activity and better cognitive function was particularly strong for overweight or obese adults and less so for those who were normal weight. Overall, the findings suggested that while being physically active is associated with better cognitive function regardless of weight, the associations were stronger for individuals who were overweight/obese compared with those who were normal weight. Given the results were particularly pronounced for waist circumference (relative to body mass index), further research should be conducted to examine if individuals with greater abdominal adiposity may benefit most from staying active in terms of their cognitive function.

Training Promotoras to Implement a Mindfulness-Based Healthy Lifestyle Program in Rural Communities

Partnering with promotoras to implement a healthy lifestyle intervention has proven efficacious in reaching community members whom they serve. However, there are no clearly defined guidelines for implementing promotora training, especially when it involves developing mindfulness skills in promotoras unfamiliar with this concept. This is a report about the facilitators and barriers to training promotoras to implement a mindfulness-enhanced healthy eating and physical activity intervention, ADAPT+, using the Train-the-Trainer model. Following this model, we laid the foundations for sustained implementation of ADAPT+ even after the training process. Future studies may examine how these guidelines help map promotora training in other mindfulness-related interventions.

Promoting healthful food purchases through in-store interventions: Empirical evidence from rural food deserts

Effective ways to promote healthful food intake in rural areas are understudied. The paper evaluated whether a two-component, in-store intervention designed to encourage healthy food purchases was associated with improved healthfulness scores of food items purchased by shoppers in rural food deserts. One component introduced a point-of-sales label that assigned a single numerical score to each food item facilitating direct comparisons of the product's nutrition with those of other products shelved around it. The other component was a one-day nutrition education workshop promoted within the store. Interventions took place in 2015 at two stores in rural counties in the U.S. Midwest. Four stores in similar communities were selected as the control group. We applied a difference-in-difference model to estimate changes in the healthfulness of food items purchased attributable to the intervention among shoppers at the treatment stores (n = 486) and control stores (n = 10,759) using store transaction data. Healthfulness of food items was measured in terms of food scores published by the Environmental Working Group on a 1-10 scale. Both components had minimal impacts on the scores, although 0.2 and 0.1 points increases in the score per item and score per dollar were statistically significant at the 1% level respectively. A year after the intervention, these small effects of the intervention further diminished compared to the immediately after implementation. Results suggest the average effects of intervention across the study communities had limited practical significance but benefited some rural residents who were exposed to the intervention.

Clinical correlates of negative health events and disparities among adults with epilepsy enrolled in a self-management clinical trial

AIMS

Despite advances in care, people with epilepsy experience negative health events (NHEs), such as seizures, emergency department (ED) visits and hospitalizations. This analysis using baseline data from an epilepsy self-management clinical trial targeting people from rural regions and other underserved populations assessed the relationship between demographic and clinical variables vs. NHEs.

METHODS

Data to evaluate disparities and clinical correlates was collected using patient surveys from a baseline sample of 94 participants in a larger prospective study of 160 individuals with epilepsy who experienced an NHE within the last six months. Demographic characteristics, mental and physical functional status assessed using 36-Item Short Form Health Survey questionnaire version 2 (SF-36v2), depression assessed with the 9-item Patient Health Questionnaire (PHQ-9), quality of life assessed with the 10-item Quality of Life in Epilepsy Inventory (QOLIE-10), self-efficacy assessed the Epilepsy Self-Efficacy Scale (ESES), social support assessed with the Multidimensional Scale of Perceived Social Support (MSPSS), self-management assessed with the Epilepsy Self-Management Scale (ESMS), and stigma assessed with the Epilepsy Stigma Scale (ESS) were all examined in association with past 6-month total NHE frequency as well as NHE sub-categories of past 30-day and 6-month seizure counts, self-harm attempts, ED visits and hospitalizations. An exploratory evaluation of NHE correlates in relation to the Rural Urban Continuum Code (RUCC) residence classification compared 3 subgroups of increasing rurality. Descriptive statistics were generated for demographic and clinical variables and NHEs, and exploratory analyses compared the distribution of demographic, clinical, and NHE variables by RUCC categorization.

RESULTS

The mean age was 38.5 years (SD 11.9), predominantly female (N= 62, 66.0%) and white (N=81, 86.2%). Just a little under half (N=43, 45.7%) of participants had annual incomes of less than $25,000, and 40% (N=38) were rural residents (RUCC >3). The past 6-month NHEs count was 20.4 (SD 32.0). Seizures were the most common NHE with a mean 30-day seizure frequency = 5.4 (SD 11.8) and 6-month seizure frequency of 18.7 (SD 31.6). Other NHE types were less common with a past 6-month self-harm frequency of 0.16 (SD 1.55), ED visit frequency of 0.72 (SD 1.10), and hospitalization frequency of 0.28 (SD 1.02). There were few significant demographic and clinical correlates for total and sub-categories of NHEs. Worse physical health status, as measured by the physical component summary (PCS) of the SF-36v2, was significantly associated with 6-month seizure counts (p=.04). There were no significant differences between the 3 RUCC subgroups on demographic variables. However, past 30-day seizure count, past 6-month seizure count and total past 6-month NHE counts were all higher among individuals from more rural settings (p-values <.01 for each).

CONCLUSIONS

Rural adults with epilepsy were more likely to have a greater number of seizures and more epilepsy complications in general. Worse physical health function was also associated with more epilepsy complications. However, this analysis found few other demographic and clinical correlates of cumulative NHEs among adults with epilepsy. Additional efforts are needed to investigate health disparities among people with epilepsy who live in rural regions or who have poor physical health function.

Influence of the food environment on obesity risk in a large cohort of US veterans by community type

OBJECTIVE

The aim of this study was to examine relationships between the food environment and obesity by community type.

METHODS

Using electronic health record data from the US Veterans Administration Diabetes Risk (VADR) cohort, we examined associations between the percentage of supermarkets and fast-food restaurants with obesity prevalence from 2008 to 2018. We constructed multivariable logistic regression models with random effects and interaction terms for year and food environment variables. We stratified models by community type.

RESULTS

Mean age at baseline was 59.8 (SD = 16.1) years; 93.3% identified as men; and 2,102,542 (41.8%) were classified as having obesity. The association between the percentage of fast-food restaurants and obesity was positive in high-density urban areas (odds ratio [OR] = 1.033; 95% CI: 1.028-1.037), with no interaction by time (p = 0.83). The interaction with year was significant in other community types (p < 0.001), with increasing odds of obesity in each follow-up year. The associations between the percentage of supermarkets and obesity were null in high-density and low-density urban areas and positive in suburban (OR = 1.033; 95% CI: 1.027-1.039) and rural (OR = 1.007; 95% CI: 1.002-1.012) areas, with no interactions by time.

CONCLUSIONS

Many healthy eating policies have been passed in urban areas; our results suggest such policies might also mitigate obesity risk in nonurban areas.

The relationship between sleep quantity, sleep quality and weight loss in adults: A scoping review

Sleep is hypothesized to interact with weight gain and loss; however, modelling this relationship remains elusive. Poor sleep perpetuates a cascade of cardiovascular and metabolic consequences that may not only increase risk of adiposity, but also confound weight loss efforts. We conducted a scoping review to assess the research on sleep and weight loss interventions. We searched six databases for studies of behavioural weight loss interventions that included assessments of sleep in the general, non-clinical adult human population. Our synthesis focused on dimensions of Population, Intervention, Control, and Outcomes (PICO) to identify research and knowledge gaps. We identified 35 studies that fell into one of four categories: (a) sleep at baseline as a predictor of subsequent weight loss during an intervention, (b) sleep assessments after a history of successful weight loss, (c) concomitant changes in sleep associated with weight loss and (d) experimental manipulation of sleep and resulting weight loss. There was some evidence of improvements in sleep in response to weight-loss interventions; however, randomized controlled trials of weight loss interventions tended not to report improvements in sleep when compared to controls. We conclude that baseline sleep characteristics may predict weight loss in studies of dietary interventions and that sleep does not improve because of weight loss alone. Future studies should enrol large and diverse, normal, overweight and obese short sleepers in trials to assess the efficacy of sleep as a behavioural weight loss treatment.

Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study

Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.

Rural cancer disparities from Oklahoma cancer and vital records registries 2016-2020

OBJECTIVES

Compared to Oklahoma, 33 states have higher all-cause cancer incidence rates, but only three states have higher all-cause cancer mortality rates. Given this troubling gap between Oklahoma's cancer incidence and mortality rankings, in-depth examination of cancer incidence, staging, and mortality rates among this state's high-risk populations is warranted. This study provides in-depth information on overall and cause-specific cancer incidence and mortality for the rural and urban Oklahoma populations classified by Rural-Urban Continuum Codes (RUCC).

METHODS

Data were publicly available and de-identified, accessed through Oklahoma Statistics on Health Available for Everyone (OK2SHARE). Statistical analysis included calculating age-specific rates, age-adjusted rates, and percentages, as well as assessing temporal patterns using average annual percent change with 95 % confidence intervals determined by Joinpoint regression analysis.

FINDINGS

Urban areas had a higher proportion of female breast cancer cases, while large and small rural areas had higher rates of lung and bronchus cancer. Urban residents were more likely to have private insurance and less likely to have Medicare compared to rural residents. Cancer incidence rates increased with age, and men had higher mortality rates than women. Lung and bronchus cancer was the leading cause of cancer death, with lower rates in urban areas compared to rural areas.

CONCLUSIONS

Findings demonstrate the need to improve the early detection of cancer among the rural populations of Oklahoma. Additionally, the high mortality rates for most types of cancer experienced by the state's rural population underscores the need to improve cancer detection and treatment in these locations.

Rural/urban differences in health care utilization and costs by perinatal depression status among commercial enrollees

PURPOSE

To understand differences in health care utilization and medical expenditures by perinatal depression (PND) status during pregnancy and 1-year postpartum overall and by rural/urban status.

METHODS

We estimated differences in health care utilization and medical expenditures by PND status for individuals with an inpatient live-birth delivery in 2017, continuously enrolled in commercial insurance from 3 months before pregnancy through 1-year postpartum (study period), using MarketScan Commercial Claims data. Multivariable regression was used to examine differences by rurality.

FINDINGS

Ten percent of commercially insured individuals had claims with PND. A smaller proportion of rural (8.7%) versus urban residents (10.0%) had a depression diagnosis (p < 0.0001). Of those with PND, a smaller proportion of rural (5.5%) versus urban residents (9.6%) had a depression claim 3 months before pregnancy (p < 0.0001). Compared with urban residents, rural residents had greater differences by PND status in total inpatient days (rural: 0.7, 95% confidence interval [CI]: 0.6-0.9 vs. urban: 0.5, 95% CI: 0.5-0.6) and emergency department (ED) visits (rural: 0.7, 95% CI: 0.6-0.9 vs. urban: 0.5, 95% CI: 0.4-0.5), but a smaller difference by PND status in the number of outpatient visits (rural: 9.2, 95% CI: 8.2-10.2 vs. urban: 13.1, 95% CI: 12.7-13.5). Differences in expenditures for inpatient services by PND status differed by rural/urban status (rural: $2654; 95% CI: $1823-$3485 vs. urban: $1786; 95% CI: $1445-$2127).

CONCLUSIONS

Commercially insured rural residents had more utilization for inpatient and ED services and less utilization for outpatient services. Rural locations can present barriers to evidence-based care to address PND.

Adolescent cancer prevention in rural, pediatric primary care settings in the United States: A scoping review

Adolescence is a critical period for establishing habits and engaging in health behaviors to prevent future cancers. Rural areas tend to have higher rates of cancer-related morbidity and mortality as well as higher rates of cancer-risk factors among adolescents. Rural primary care clinicians are well-positioned to address these risk factors. Our goal was to identify existing literature on adolescent cancer prevention in rural primary care and to classify key barriers and facilitators to implementing interventions in such settings. We searched the following databases: Ovid MEDLINE®; Ovid APA PsycInfo; Cochrane Library; CINAHL; and Scopus. Studies were included if they reported on provider and/or clinic-level interventions in rural primary care clinics addressing one of these four behaviors (obesity, tobacco, sun exposure, HPV vaccination) among adolescent populations. We identified 3,403 unique studies and 24 met inclusion criteria for this review. 16 addressed obesity, 6 addressed HPV vaccination, 1 addressed skin cancer, and 1 addressed multiple behaviors including obesity and tobacco use. 10 studies were either non-randomized experimental designs (n = 8) or randomized controlled trials (n = 2). The remaining were observational or descriptive research. We found a dearth of studies addressing implementation of adolescent cancer prevention interventions in rural primary care settings. Priorities to address this should include further research and increased funding to support EBI adaptation and implementation in rural clinics to reduce urban-rural cancer inequities.

Trends in Maternal Weight Disparities: Statewide Differences in Rural and Urban Minnesota Residents From 2012 to 2019

INTRODUCTION

Despite research showing substantial weight disparities along the rural-urban continuum, little work has attempted to identify differences in prepregnancy weight status or gestational weight gain (GWG) outcomes between rural and urban birthing people. As such, the goals of this research were to 1) document the prevalence of prepregnancy overweight and obesity and excessive GWG in rural and urban birthing people and 2) examine changes in rural and urban prepregnancy overweight or obesity and excessive GWG over time.

METHODS

Birth certificate data provided sociodemographic variables, prepregnancy body mass index, GWG, and rurality status on 465,709 respondents who gave birth in Minnesota from 2012 to 2019. A series of regression models estimated risk differences in 1) prepregnancy weight status and 2) excessive GWG between rural and urban respondents over time, controlling for relevant covariates.

RESULTS

Rural individuals had a 4.9 percentage-point (95% confidence interval, 4.5-5.3) higher risk of having prepregnancy overweight or obesity compared with urban individuals, and a 2.6 percentage-point (95% confidence interval, 1.9-3.3) higher risk of gaining excessive gestational weight. The disparities in prepregnancy overweight or obesity and excessive gestational weight between rural and urban individuals widened over time.

CONCLUSIONS

These findings contribute to accumulating evidence documenting notable health disparities between rural and urban individuals during the perinatal period and support the need to develop prevention and treatment efforts focused on improving the weight-related health of individuals living in rural communities.

Contextualizing physical activity in rural adults: Do relationships between income inequality, neighborhood environments, and physical activity exist?

OBJECTIVE

To examine if income inequality, social cohesion, and neighborhood walkability are associated with physical activity among rural adults.

DATA SOURCE

Cross-sectional data came from a telephone survey (August 2020-March 2021) that examined food access, physical activity, and neighborhood environments across rural counties in a southeastern state.

STUDY DESIGN

Multinomial logistic regression models assessed the likelihood of being active versus inactive and insufficiently active versus inactive in this rural population. Coefficients are presented as relative risk ratios (RRRs). Statistical significance was determined using 95% confidence intervals (CIs). All analyses were performed in STATA 16.1.

DATA COLLECTION/EXTRACTION METHODS

Trained university students administered the survey. Students verbally obtained consent, read survey items, and recorded responses into Qualtrics software. Upon survey completion, respondents were mailed a $10 incentive card and printed informed consent form. Eligible participants were ≥18 years old and current residents of included counties.

PRINCIPAL FINDINGS

Respondents in neighborhoods with relatively high social cohesion versus low social cohesion were more likely to be active than inactive (RRR = 2.50, 95% CI: 1.27-4.90, p < 0.01), after accounting for all other variables in the model. Income inequality and neighborhood walkability were not associated with different levels of physical activity in the rural sample.

CONCLUSIONS

Study findings contribute to limited knowledge on the relationship between neighborhood environmental contexts and physical activity among rural populations. The health effects of neighborhood social cohesion warrant more attention in health equity research and consideration when developing multilevel interventions to improve the health of rural populations.

Cancer Epidemiology in the Northeastern United States (2013-2017)

We tested the hypotheses that adult cancer incidence and mortality in the Northeast region and in Northern New England (NNE) were different than the rest of the United States, and described other related cancer metrics and risk factor prevalence. Using national, publicly available cancer registry data, we compared cancer incidence and mortality in the Northeast region with the United States and NNE with the United States overall and by race/ethnicity, using age-standardized cancer incidence and rate ratios (RR). Compared with the United States, age-adjusted cancer incidence in adults of all races combined was higher in the Northeast (RR, 1.07; 95% confidence interval [CI] 1.07-1.08) and in NNE (RR 1.06; CI 1.05-1.07). However compared with the United States, mortality was lower in the Northeast (RR, 0.98; CI 0.98-0.98) but higher in NNE (RR, 1.05; CI 1.03-1.06). Mortality in NNE was higher than the United States for cancers of the brain (RR, 1.16; CI 1.07-1.26), uterus (RR, 1.32; CI 1.14-1.52), esophagus (RR, 1.36; CI 1.26-1.47), lung (RR, 1.12; CI 1.09-1.15), bladder (RR, 1.23; CI 1.14-1.33), and melanoma (RR, 1.13; CI 1.01-1.27). Significantly higher overall cancer incidence was seen in the Northeast than the United States in all race/ethnicity subgroups except Native American/Alaska Natives (RR, 0.68; CI 0.64-0.72). In conclusion, NNE has higher cancer incidence and mortality than the United States, a pattern that contrasts with the Northeast region, which has lower cancer mortality overall than the United States despite higher incidence.

Significance

These findings highlight the need to identify the causes of higher cancer incidence in the Northeast and the excess cancer mortality in NNE.

A Survey of Cancer Risk Behaviors, Beliefs, and Social Drivers of Health in New Hampshire and Vermont

Compared with urban areas, rural areas have higher cancer mortality and have experienced substantially smaller declines in cancer incidence in recent years. In a New Hampshire (NH) and Vermont (VT) survey, we explored the roles of rurality and educational attainment on cancer risk behaviors, beliefs, and other social drivers of health. In February-March 2022, two survey panels in NH and VT were sent an online questionnaire. Responses were analyzed by rurality and educational attainment. Respondents (N = 1,717, 22%) mostly lived in rural areas (55%); 45% of rural and 25% of urban residents had high school education or less and this difference was statistically significant. After adjustment for rurality, lower educational attainment was associated with smoking, difficulty paying for basic necessities, greater financial difficulty during the COVID-19 pandemic, struggling to pay for gas (P < 0.01), fatalistic attitudes toward cancer prevention, and susceptibility to information overload about cancer prevention. Among the 33% of respondents who delayed getting medical care in the past year, this was more often due to lack of transportation in those with lower educational attainment (21% vs. 3%, P = 0.02 adjusted for rurality) and more often due to concerns about catching COVID-19 among urban than rural residents (52% vs. 21%; P < 0.001 adjusted for education). In conclusion, in NH/VT, smoking, financial hardship, and beliefs about cancer prevention are independently associated with lower educational attainment but not rural residence. These findings have implications for the design of interventions to address cancer risk in rural areas.

Significance

In NH and VT, the finding that some associations between cancer risk factors and rural residence are more closely tied to educational attainment than rurality suggest that the design of interventions to address cancer risk should take educational attainment into account.

State-Specific Prevalence of Severe Obesity Among Adults in the US Using Bias Correction of Self-Reported Body Mass Index

INTRODUCTION

Adults with severe obesity are at increased risk for poor metabolic health and may need more intensive clinical and community supports. The prevalence of severe obesity is underestimated from self-reported weight and height data. We examined severe obesity prevalence among US adults by sociodemographic characteristics and by state after adjusting for self-report bias.

METHODS

Using a validated bias-correction method, we adjusted self-reported body mass index (BMI) data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) by using measured data from the National Health and Nutrition Examination Survey. We compared bias-corrected prevalence of severe obesity (BMI ≥40) with self-reported estimates by sociodemographic characteristics and state.

RESULTS

Self-reported BRFSS data significantly underestimated the prevalence of severe obesity compared with bias-corrected estimates. In 2020, 8.8% of adults had severe obesity based on the bias-corrected estimates, whereas 5.3% of adults had severe obesity based on self-reported data. Women had a significantly higher prevalence of bias-corrected severe obesity (11.1%) than men (6.5%). State-level prevalence of bias-corrected severe obesity ranged from 5.5% (Massachusetts) to 13.2% (West Virginia). Based on bias-corrected estimates, 16 states had a prevalence of severe obesity greater than 10%, a level not seen in the self-reported estimates.

CONCLUSION

Self-reported BRFSS data underestimated the overall prevalence of severe obesity by 40% (5.3% vs 8.8%). Accurate state-level estimates of severe obesity can help public health and health care decision makers prioritize and plan to implement effective prevention and treatment strategies for people who are at high risk for poor metabolic health.

A qualitative study of ecological and motivational factors to inform weight management interventions for Black men in the rural South of the United States

RATIONALE AND OBJECTIVE

Black men in the rural South of the United States (US) are underrepresented in weight management behavioral trials. Qualitative research is needed to inform interventions that can reduce obesity and health disparities in this population. We explored how intrapersonal, social, and environmental factors affect motivation and weight-related behaviors and how to culturally adapt behavioral interventions for Black men in the rural South.

METHODS

We conducted individual telephone interviews with 23 Black men (mean age 50 ± 14 years) with overweight or obesity living in rural South Carolina communities in 2020 and 2021. Interviews were audio recorded, professionally transcribed, and coded by two men's health researchers who achieved an intercoder reliability of 70%. Content analysis using QSR NVivo 12 was used to generate themes using deductive and inductive approaches.

RESULTS

Physical health and health behaviors were perceived as key determinants of overall health. Family, friends, and other social contacts often provided positive social support that increased motivation but also hindered motivation by engaging in behaviors men were trying to avoid. Younger participants had stronger views of rural environments not supporting healthy lifestyles, which compounded personal challenges such as time constraints and lack of motivation. Comfort was discussed as a critical program consideration, and gender concordance among program participants and facilitators was perceived as promoting comfort. Participants noted preferences and benefits of in-person, group programs emphasizing physical activity, and younger participants more strongly endorsed programs that incorporated sports and competition.

CONCLUSIONS

Findings from this study provide important evidence to inform the development of weight management interventions for Black men in the rural US South. Based on these findings, an innovative, competitive "football-themed" weight management program promoting peer support and integrating competitive physical activities is being evaluated for younger Black men in the rural South.

Defining rurality: an evaluation of rural definitions and the impact on survival estimates

BACKGROUND

Patients with cancer living in rural areas have inferior cancer outcomes; however, studies examining this association use varying definitions of "rural," complicating comparisons and limiting the utility of the results for policy makers and future researchers.

METHODS

Surveillance, Epidemiology, and End Results data (2000-2016) were used to assess risk of cancer mortality and mortality from any cause across 4 definitions of rurality: Urban Influence codes (UIC), National Center for Health Statistics (NCHS), Rural-Urban continuum codes (RUCC), and Index of Relative Rurality. Binary (urban vs rural) and ternary (urban, micropolitan, rural) definitions were evaluated. Multivariable parametric survival models estimated hazards of mortality overall and among 3 cancer groupings: screening related, obesity related, and tobacco related. Definition agreement was also assessed.

RESULTS

Overall, 3 788 273 patients with an incident cancer representing 605 counties were identified. There was little discordance between binary definitions of rural vs urban and moderate agreement at the 3 levels. Adjusted models using binary definitions revealed 15% to 17% greater hazard of cancer mortality in rural compared with urban. At the 3 levels when comparing rural with metropolitan, RUCC and NCHS saw similarly increased hazard ratios; however, Index of Relative Rurality did not. Screening-related cancers saw the highest hazards of mortality and the largest divergence between definitions. Obesity-related and tobacco-related cancers saw similarly increased hazards of mortality at the binary and ternary levels.

CONCLUSIONS

Hazard of death is similar across binary definitions; however, this differed when categorized as ternary or continuous, especially among screening-related cancers. Results suggest that study purpose should direct choice of definitions and categorization.

Evaluation Design for The Two Georgias Initiative: Assessing Progress Toward Health Equity in the Rural South

As persistent inequities in health gained increased attention nationally due to COVID-19 and racial justice protests in 2020, it has become increasingly important to evaluate both the process and outcomes associated with coalition-based efforts to address health inequities. The Two Georgias Initiative supports coalitions in 11 rural counties to (1) achieve greater health equity, (2) improve health and health care, (3) build healthier rural communities and improve social conditions that impact the health of rural populations, and (4) build community, organizational, and individual leadership capacity for health equity. Rural communities suffer significant health disparities relative to urban areas, and also experience internal inequities by race and poverty level. The evaluation framework for The Two Georgias Initiative provides a comprehensive mixed methods approach to evaluating both processes and outcomes. Early results related to community readiness and capacity to address health inequities, measured through a coalition member survey (n = 236) conducted at the end of the planning phase, suggest coalitions were in the preparation stage, with higher levels of readiness among coalition members and organizations/groups similar to the coalition members' own, lower levels among public officials and other leaders, and the lowest levels among county residents. In addition, coalition members reported more experience with downstream drivers (e.g., access to care) of health than upstream drivers (e.g., affordable housing, environmental or racial justice). By providing a logic model, evaluation questions and associated indicators, as well as a range of data collection methods, this evaluation approach may prove practical to others aiming to evaluate their efforts to address health equity.

Association of Rurality With Risk of Heart Failure

Importance

Rural populations experience an increased burden of heart failure (HF) mortality compared with urban populations. Whether HF incidence is greater among rural individuals is less known. Additionally, the intersection between racial and rural health inequities is understudied.

Objective

To determine whether rurality is associated with increased risk of HF, independent of cardiovascular (CV) disease and socioeconomic status (SES), and whether rurality-associated HF risk varies by race and sex.

Design, Setting, and Participants

This prospective cohort study analyzed data for Black and White participants of the Southern Community Cohort Study (SCCS) without HF at enrollment who receive care via Centers for Medicare & Medicaid Services (CMS). The SCCS is a population-based cohort of low-income, underserved participants from 12 states across the southeastern United States. Participants were enrolled between 2002 and 2009 and followed up until December 31, 2016. Data were analyzed from October 2021 to November 2022.

Exposures

Rurality as defined by Rural-Urban Commuting Area codes at the census-tract level.

Main Outcomes and Measures

Heart failure was defined using diagnosis codes via CMS linkage through 2016. Incidence of HF was calculated by person-years of follow-up and age-standardized. Sequentially adjusted Cox proportional hazards regression models tested the association between rurality and incident HF.

Results

Among 27 115 participants, the median (IQR) age was 54 years (47-65), 18 647 (68.8%) were Black, and 8468 (32.3%) were White; 5556 participants (20%) resided in rural areas. Over a median 13-year follow-up, age-adjusted HF incidence was 29.6 (95% CI, 28.9-30.5) per 1000 person-years for urban participants and 36.5 (95% CI, 34.9-38.3) per 1000 person-years for rural participants (P < .001). After adjustment for demographic information, CV risk factors, health behaviors, and SES, rural participants had a 19% greater risk of incident HF (hazard ratio [HR], 1.19; 95% CI, 1.13-1.26) compared with their urban counterparts. The rurality-associated risk of HF varied across race and sex and was greatest among Black men (HR, 1.34; 95% CI, 1.19-1.51), followed by White women (HR, 1.22; 95% CI, 1.07-1.39) and Black women (HR, 1.18; 95% CI, 1.08-1.28). Among White men, rurality was not associated with greater risk of incident HF (HR, 0.97; 95% CI, 0.81-1.16).

Conclusions and Relevance

Among predominantly low-income individuals in the southeastern United States, rurality was associated with an increased risk of HF among women and Black men, which persisted after adjustment for CV risk factors and SES. This inequity points to a need for additional emphasis on primary prevention of HF among rural populations.

Rural-Urban Disparities in Adverse Maternal Outcomes in the United States, 2016-2019

Objectives. To describe differences in maternal admissions to the intensive care unit (ICU) and mortality in rural versus urban areas in the United States. Methods. We performed a nationwide analysis and calculated age-standardized rates and rate ratios (RRs) of maternal ICU admission and mortality per 100 000 live births between 2016 and 2019 in rural versus urban areas. Results. From 2016 to 2019, there was no significant increase in age-standardized rates of maternal ICU admissions in rural (170.6-192.3) or urban (161.7-172.4) areas, with a significantly higher rate, albeit a relatively small difference, in rural versus urban areas (2019 RR = 1.14; 95% confidence interval [CI] = 1.04, 1.20). Maternal mortality increased in both rural (66.9-81.7 deaths per 100 000 live births) and urban (38.1-42.3) areas and was nearly 2 times higher in rural areas (2019 RR = 1.93; 95% CI = 1.71, 2.17). Conclusions. Pregnant individuals in rural areas are at higher risk for ICU admission and mortality than are their urban counterparts. Significant increases in maternal mortality occurred in rural and urban areas. Public Health Implications. Public health efforts need to focus on resource-limited rural areas to mitigate geographic disparities in maternal morbidity and mortality. (Am J Public Health. 2023;113(2): 224-227.https://doi.org/10.2105/AJPH.2022.307134).

Urban-Rural Disparities in Pulmonary Hypertension-Related Mortality Between 2004 and 2019: A Call to Improve Access to Specialty Care Centers for Rural Residents in the United States

As rural-urban pulmonary hypertension (PH)-related mortality trends have not been reported past 2011, it is important to update the literature to provide guidance for necessary initiatives geared at minimizing barriers to social determinants of health. We extracted PH-related data between 2004 and 2019 from the Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research (CDC WONDER). Crude-mortality rate and age-adjusted mortality rate (AAMR) were determined. Associated annual percent changes and average annual percentage changes (AAPCs) were computed using Joinpoint Regression Program trend analysis software. A total of 353, 916 PH-related deaths occurred in the study population within the United States between 2004 and 2019 out of 3,326,222,482 total deaths. The overall rural PH-related AAMR was 10.75 per 100,000 individuals. The overall urban PH-related AAMR was 9.70 per 100,000 individuals. Both rural and urban county subgroups demonstrated increases in AAMR during the study period. Notably, 8.5% of specialty centers are in rural counties while 91.5% of centers are located in urban counties. Given the crucial role of early treatment at specialty centers in PH disease courses, we highlight higher mortality rates among rural county individuals. Specialty center accessibility for these patients must improve.

Application of the consolidated framework for implementation research to understand implementation context of a cardiovascular disease risk-reduction intervention in rural churches

Rural populations in the USA face higher rates of cardiovascular disease (CVD) incidence and mortality relative to non-rural and often lack access to health-promoting evidence-based interventions (EBIs) to support CVD prevention and management. Partnerships with faith organizations offer promise for translating preventative EBIs in rural communities; however, studies demonstrating effective translation of EBIs in these settings are limited. We used the Consolidated Framework for Implementation Research (CFIR) and a multiple case study approach to understand the role of internal organizational context within 12 rural churches in the implementation of a 12-week CVD risk-reduction intervention followed by a 24-month maintenance program implemented in southernmost Illinois. The study involved qualitative analysis of key informant interviews collected before (n = 26) and after (n = 15) the intervention and monthly implementation reports (n = 238) from participating churches using a deductive analysis approach based on the CFIR. Internal context across participating churches varied around organizational climate and culture in four thematic areas: (i) religious basis for health promotion, (ii) history of health activities within the church, (iii) perceived need for the intervention, and (iv) church leader engagement. Faith organizations may be ideal partners in rural health promotion research but may vary in their interest and capacity to collaborate. Identifying contextual factors within community organizations is a first step to facilitating rural, community-based EBI implementation and outcomes.

Obesity treatment initiation, retention, and outcomes in the Veterans Affairs MOVE! Program among rural and urban veterans

Objective

Rural veterans have high obesity rates. Yet, little is known about this population's engagement with the Veterans Affairs (VA) weight management program (MOVE!). The study objective is to determine whether MOVE! enrollment, anti-obesity medication use, bariatric surgery use, retention, and outcomes differ by rurality for veterans with severe obesity.

Methods

This is a retrospective cohort study using Veterans Health Administration patient databases, including VA patients with severe obesity during 2015-2017. Patients were categorized using Rural-Urban Commuting Area codes. Primary outcomes included proportion of patients and risk-adjusted likelihood of initiating VA MOVE!, anti-obesity medication, or bariatric surgery and risk-adjusted highly rural|Hazard Ratio (HR) of any obesity treatment. Secondary outcomes included treatment retention (≥12 weeks) and successful weight loss (5%) among patients initiating MOVE!, and risk-adjusted odds of retention and successful weight loss.

Results

Among 640,555 eligible veterans, risk-adjusted relative likelihood of MOVE! treatment was significantly lower for rural and HR veterans (HR = 0.83, HR = 0.67, respectively). Initiation rates of anti-obesity medication use were significantly lower as well, whereas bariatric surgery rates, retention, and successful weight loss did not differ.

Conclusions

Overall treatment rates with MOVE!, bariatric surgery, and anti-obesity medications remain low. Rural veterans are less likely to enroll in MOVE! and less likely to receive anti-obesity medications than urban veterans.

Measuring rurality in health services research: a scoping review

PURPOSE

This study is a scoping review of the different methods used to measure rurality in the health services research (HSR) literature.

METHODS

We identified peer-reviewed empirical studies from 2010-2020 from seven leading HSR journals, including the Journal of Rural Health, that used any definition to measure rurality as a part of their analysis. From each study, we identified the geographic unit (e.g., county, zip code) and definition (e.g., Rural Urban Continuum Codes, Rural Urban Commuting Areas) used to classify categories of rurality. We analyzed whether geographic units and definitions used to classify rurality differed by focus area of studies, including costs, quality, and access to care. Lastly, we examined the number of rural categories used by authors to assess rural areas.

FINDINGS

In 103 included studies, five different geographic units and 11 definitions were used to measure rurality. The most common geographic units used to measure rurality were county (n = 59, 57%), which was used most frequently in studies examining cost (n = 12, 75%) and access (n = 33, 57.9%). Rural Urban Commuting Area codes were the most common definition used to measure rurality for studies examining access (n = 13, 22.8%) and quality (n = 10, 44%). The majority of included studies made rural versus urban comparisons (n = 82, 80%) as opposed to focusing on rural populations only (n = 21, 20%). Among studies that compared rural and urban populations, most studies used only one category to identify rural locations (n = 49 of 82 studies, 60%).

CONCLUSION

Geographic units and definitions to determine rurality were used inconsistently within and across studies with an HSR focus. This finding may affect how health disparities by rural location are determined and thus how resources and federal funds are allocated. Future research should focus on developing a standardized system to determine under what circumstances researchers should use different geographic units and methods to determine rurality by HSR focus area.

Using CDC’s Worksite Health ScoreCard to Evaluate Nutrition and Physical Activity Interventions in Priority Communities in New York State, 2015–2020

The aim of our study was to evaluate a statewide obesity prevention program, designed to improve two social determinants of health (access to healthy foods and opportunities for physical activity) by changing worksite policies, systems, and environments. We used data on social determinants of health to identify priority communities and funded 25 contractors to recruit and engage worksites in those communities. We employed a pre/post quasi-experimental design and used the Centers for Disease Control and Prevention’s Worksite Health ScoreCard to assess implementation of nutrition and physical activity strategies. Contractors collaborated with partners to conduct pre- and postassessments at 313 worksites. Worksites were successful at implementing all except one strategy; implementation doubled for eight strategies and tripled for three. We used weighted scores to incorporate strength of the evidence-base and level of impact into our analysis; increases in strategy implementation were statistically significant in worksites that provided places to purchase food and beverages and in those that did not. Our study revealed several important recommendations for practice. Use available data to prioritize communities experiencing health disparities and identify PSE change strategies to increase access to nutritious foods and physical activity opportunities. Engage with reputable community-based partners from different sectors when designing and implementing programs. Use standardized tools, such as the Worksite Health ScoreCard, to evaluate public health initiatives.

Exploring differences in adolescent BMI and obesity-related behaviors by urban, suburban, and rural status

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Rurality was not significantly associated with BMI percentiles.

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Suburban adolescents had significantly different dietary behaviors.

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Being non-Hispanic black was a predictor of BMI and obesity-related risk factors.

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Low household income was a predictor of BMI and obesity-related risk factors.

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Additional obesity research using a three-category rurality classification is needed.

Data from the nationally representative 2014 Family Life, Activity, Sun, Health, and Eating (FLASHE) study was examined to identify differences in adolescent Body Mass Index (BMI) and obesity-related behaviors by rurality status (i.e., urban, suburban, rural) while accounting for relevant demographics (i.e., sex, race/ethnicity, household income). This secondary, cross-sectional analysis included 1,353 adolescents. Analyses included descriptive statistics, one-way analysis of variance, Chi-squared tests, and multiple linear regression models (reported significance level p < 0.05). Rurality was not associated with BMI when controlling for demographics. However, relative to rural adolescents, suburban adolescents had significantly higher junk food, sugar-sweetened beverages (SSB), sugary food (all β=+0.2, p ≤ 0.001), and fruit/vegetable intake (β=+0.1, p ≤ 0.05). Compared to Non-Hispanic White adolescents, Non-Hispanic Black adolescents had significantly higher BMI (β=+4.4, p ≤ 0.05), total sedentary time (β=+4.1, p ≤ 0.001), junk food, SSB, and sugary food intake (all β=+0.2, p ≤ 0.05). Relative to their lower-income household counterparts, adolescents from higher-income households had significantly lower BMI (β = -9.7, p ≤ 0.001), junk food (β = -0.2, p ≤ 0.05), and SSB intake (β = -0.5, p ≤ 0.001). Contrary to literature, rurality was not a significant predictor of adolescent BMI. While suburban status was significantly associated with several diet-related risk factors, it was not in the direction anticipated. Being non-Hispanic Black and from a low-income household had the greatest influence on adolescent BMI. Findings highlight the importance of using a three-category classification for rurality.

Factors associated with large-for-gestational-age infants born after frozen embryo transfer cycles

Objective

To examine trends of frozen embryo transfer (FET) proportions and large-for-gestational-age (LGA) incidence and determine risk factors for LGA infants after FET.

Design

Retrospective cohort study.

Setting

Not applicable.

Patients

Frozen embryo transfer cycles.

Interventions

None.

Main Outcome Measures

Singleton LGA infant.

Results

The percentage of FETs increased from 20%-74% of transfers, whereas the rate of LGA among FET singleton births decreased from 18%-12% during 2004-2018. In a subanalysis of 127,525 FET-associated singleton live births during 2016-2018, patient factors associated with LGA were higher-than-normal maternal body mass index (body mass index [BMI], 25.0-29.9 kg/m²; adjusted relative risk [aRR], 1.31; 95% confidence interval [CI], 1.26-1.36; BMI, 30.0-34.9 kg/m²; aRR, 1.48; 95% CI, 1.41-1.55; and BMI, >35 Kg/m²; aRR, 1.68; 95% CI, 1.59-1.77) and ≥1 prior birth vs. none. Low maternal BMI (<18.5 vs. 18.5-24.9 kg/m²) and cycles involving patients who were non-Hispanic (NH) Asian/Native Hawaiian/Pacific Islander, NH Black, or Hispanic (compared with NH White) were at lower risk of LGA infants. Cycle factors associated with LGA included gestational carrier use (aRR, 1.25; 95% CI, 1.16-1.34) and donor sperm (aRR, 1.17; 95% CI, 1.10-1.25).

Conclusions

Although the number and proportion of FET cycles increased from 2004-2018, the rate of LGA after FET decreased. Maternal BMI, parity, and race/ethnicity were the strongest risk factors for LGA infants after FET.

Urban and rural differences in new onset type 2 diabetes: Comparisons across national and regional samples in the diabetes LEAD network

Introduction

Geographic disparities in diabetes burden exist throughout the United States (US), with many risk factors for diabetes clustering at a community or neighborhood level. We hypothesized that the likelihood of new onset type 2 diabetes (T2D) would differ by community type in three large study samples covering the US.

Research design and methods

We evaluated the likelihood of new onset T2D by a census tract-level measure of community type, a modification of RUCA designations (higher density urban, lower density urban, suburban/small town, and rural) in three longitudinal US study samples (REGARDS [REasons for Geographic and Racial Differences in Stroke] cohort, VADR [Veterans Affairs Diabetes Risk] cohort, Geisinger electronic health records) representing the CDC Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network.

Results

In the REGARDS sample, residing in higher density urban community types was associated with the lowest odds of new onset T2D (OR [95% CI]: 0.80 [0.66, 0.97]) compared to rural community types; in the Geisinger sample, residing in higher density urban community types was associated with the highest odds of new onset T2D (OR [95% CI]: 1.20 [1.06, 1.35]) compared to rural community types. In the VADR sample, suburban/small town community types had the lowest hazard ratios of new onset T2D (HR [95% CI]: 0.99 [0.98, 1.00]). However, in a regional stratified analysis of the VADR sample, the likelihood of new onset T2D was consistent with findings in the REGARDS and Geisinger samples, with highest likelihood of T2D in the rural South and in the higher density urban communities of the Northeast and West regions; likelihood of T2D did not differ by community type in the Midwest.

Conclusions

The likelihood of new onset T2D by community type varied by region of the US. In the South, the likelihood of new onset T2D was higher among those residing in rural communities.

Sociotechnical Intervention for Improved Delivery of Preventive Cardiovascular Care to Rural Communities: Participatory Design Approach

Background

Clinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed.

Objective

A multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic.

Methods

The design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm.

Results

New workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm.

Conclusions

Participatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.

Cancer disparities in the context of rurality: risk factors and screening across various U.S. rural classification codes

PURPOSE

Prior cancer research is limited by inconsistencies in defining rurality. The purpose of this study was to describe the prevalence of cancer risk factors and cancer screening behaviors across various county-based rural classification codes, including measures reflecting a continuum, to inform our understanding of cancer disparities according to the extent of rurality.

METHODS

Using an ecological cross-sectional design, we examined differences in cancer risk factors and cancer screening behaviors from the Behavioral Risk Factor Surveillance System and National Health Interview Survey (2008-2013) across rural counties and between rural and urban counties using four rural-urban classification codes for counties and county-equivalents in 2013: U.S. Office of Management and Budget, National Center for Health Statistics, USDA Economic Research Service's rural-urban continuum codes, and Urban Influence Codes.

RESULTS

Although a rural-to-urban gradient was not consistently evident across all classification codes, the prevalence of smoking, obesity, physical inactivity, and binge alcohol use increased (all p_trend < 0.03), while colorectal, cervical and breast cancer screening decreased (all p_trend < 0.001) with increasing rurality. Differences in the prevalence of risk factors and screening behaviors across rural areas were greater than differences between rural and urban counties for obesity (2.4% vs. 1.5%), physical activity (2.9% vs. 2.5%), binge alcohol use (3.4% vs. 0.4%), cervical cancer screening (6.8% vs. 4.0%), and colorectal cancer screening (4.4% vs. 3.8%).

CONCLUSIONS

Rural cancer disparities persist across multiple rural-urban classification codes, with marked variation in cancer risk factors and screening evident within rural regions. Focusing only on a rural-urban dichotomy may not sufficiently capture subpopulations of rural residents at greater risk for cancer and cancer-related mortality.

Willingness to pay for a telemedicine-delivered healthy lifestyle programme

INTRODUCTION

Effective weight-management interventions require frequent interactions with specialised multidisciplinary teams of medical, nutritional and behavioural experts to enact behavioural change. However, barriers that exist in rural areas, such as transportation and a lack of specialised services, can prevent patients from receiving quality care.

METHODS

We recruited patients from the Dartmouth-Hitchcock Weight & Wellness Center into a single-arm, non-randomised study of a remotely delivered 16-week evidence-based healthy lifestyle programme. Every 4 weeks, participants completed surveys that included their willingness to pay for services like those experienced in the intervention. A two-item Willingness-to-Pay survey was administered to participants asking about their willingness to trade their face-to-face visits for videoconference visits based on commute and copay.

RESULTS

Overall, those with a travel duration of 31-45 min had a greater willingness to trade in-person visits for telehealth than any other group. Participants who had a travel duration less than 15 min, 16-30 min and 46-60 min experienced a positive trend in willingness to have telehealth visits until Week 8, where there was a general negative trend in willingness to trade in-person visits for virtual. Participants believed that telemedicine was useful and helpful.

CONCLUSIONS

In rural areas where patients travel 30-45 min a telemedicine-delivered, intensive weight-loss intervention may be a well-received and cost-effective way for both patients and the clinical care team to connect.