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Background. Chronic inflammatory rheumatic diseases (IRDs) are immune-mediated disorders that may be affected by geographical barriers, such as rurality and distance from specialized centers, potentially limiting access to care. Evidence regarding the impact of these factors on clinical outcomes remains scarce and mostly derived from non-European settings. This study investigates, within an Italian hub-and-spoke healthcare model, the influence of rurality, distance from the referral center, and other factors, such as comorbidities, sex, age, and type of IRD, on clinical outcomes among patients residing in the province of Udine.   Materials and Methods. The study conducted a retrospective cohort analysis using administrative healthcare data, encompassing all incident diagnoses of IRDs (chronic arthritis, connective tissue diseases, and systemic vasculitis) from 2014 to 2023. Rurality was defined as residence in areas with a population density <150 inhabitants/km²; distance from the specialized center was measured in kilometers, with a threshold of 27 km (75th percentile). Comorbidities were classified according to the Charlson Comorbidity Index (CCI): mild (CCI=1), moderate (CCI=2), severe (CCI≥3). Outcomes analyzed included first hospitalizations and emergency department (ED) visits for avoidable IRD-related complications (infections, cardiovascular events, osteoporotic complications) and all-cause mortality. Statistical analysis: multivariable Cox regression.   Results. A total of 3,044 patients were included, 66.33% female, with a median follow-up of 4.9 (2.4–7.3) years. Median age at diagnosis was 57 (47–67) years. The distribution of IRDs was as follows: chronic arthritis (73.95%), connective tissue diseases (17.53%), and vasculitis (8.51%). Overall, 32.95% of patients lived in rural areas, and 24.31% resided more than 27 km from the center. Rurality and distance did not correlate with an elevated risk of hospitalization, emergency department visits, or mortality (p>0.05). Female sex was protective against ED visits and infection-related hospitalizations but associated with a higher risk of osteoporotic fractures. Being older and having more than one health problem at the same time were both strong predictors of hospitalization for all avoidable complications, especially in patients with moderate (CCI=2) and severe (CCI≥3) comorbidities. A similar trend was noted for preventable emergency department visits, with an increased risk in patients with CCI=2, CCI≥3, and older age; no emergency department visits were documented for osteoporotic complications. The risk of death went up with age and was higher in people with CCI=2 or CCI≥3. Women had a lower risk than men. Connective tissue diseases were also associated with increased mortality risk. All statistically significant hazard ratios are reported in the Figure.   Conclusions: In the Province of Udine, rurality and distance do not influence clinical outcomes in patients with IRDs, suggesting that a centralized healthcare system may help mitigate inequalities in access to care. However, age, male sex, and comorbidity burden remain key negative determinants, underscoring the importance of early identification of high-risk patients and the adoption of integrated management strategies.

In this report, the authors provide comprehensive and up-to-date US data on disparities in cancer occurrence, major risk factors, and access to and utilization of preventive measures and screening by sociodemographic characteristics. They also review programs and resources that have reduced cancer disparities and provide policy recommendations to further mitigate these inequalities. The overall cancer death rate is 19% higher among Black males than among White males. Black females also have a 12% higher overall cancer death rate than their White counterparts despite having an 8% lower incidence rate. There are also substantial variations in death rates for specific cancer types and in stage at diagnosis, survival, exposure to risk factors, and receipt of preventive measures and screening by race/ethnicity, socioeconomic status, and geographic location. For example, kidney cancer death rates by sex among American Indian/Alaska Native people are ≥64% higher than the corresponding rates in each of the other racial/ethnic groups, and the 5-year relative survival for all cancers combined is 14% lower among residents of poorer counties than among residents of more affluent counties. Broad and equitable implementation of evidence-based interventions, such as increasing health insurance coverage through Medicaid expansion or other initiatives, could substantially reduce cancer disparities. However, progress will require not only equitable local, state, and federal policies but also broad interdisciplinary engagement to elevate and address fundamental social inequities and longstanding systemic racism.

Monitoring and improving rural health is challenging because of varied and conflicting concepts of just what rural means. Federal, state, and local agencies and data resources use different definitions, which may lead to confusion and inequity in the distribution of resources depending on the definition used. This article highlights how inconsistent definitions of rural may lead to measurement bias in research, the interpretation of research outcomes, and differential eligibility for rural-focused grants and other funding. We conclude by making specific recommendations on how policy makers and researchers could use these definitions more appropriately, along with definitions we propose, to better serve rural residents. We also describe concepts that may improve the definition of and frame the concept of rurality.

Physical Activity (PA) and human mental health seem to be influenced by seasons (Arnardottir et al., 2017; Martinez, 2018). However, little is known about how these parameters vary in elderlies living alone in rural areas, especially those in social isolation (SI). Moreover, it is still unclear how seasonality influences sleep patterns in this population. This study explores seasonal variations in PA, depressive symptoms, and sleep patterns among older adults living alone in a rural area. Fifty-nine participants (80.07±6.31 years) who lived alone in rural areas of Vila Real were included. Sociodemographic data and Lubben Social Network Scale-6 (LSNS-6), were assessed. PA and sleep patterns were measured using a GT9X accelerometer, and depressive symptoms were assessed through the Geriatric Depression Scale (GDS). Sleep parameters included the clock time of in-bed time and get-up time, total minutes spent in bed, total sleep time (TST), wake after sleep onset (WASO), and total counts. Seasonal variations were analysed by considering weather factors, including average temperature, precipitation levels, daylight duration, sunset time, and sunrise time. In summer, participants engaged in significantly higher levels of light physical activity (t =-2.15, p=0.04), moderate-vigorous physical activity (t=4.17, p=0.00), and daily steps (t=2.78, p=0.00). In contrast, winter was associated with more sedentary behaviour (t=-2.56, p=0.01). Depressive symptoms were found to be lower during the summer (t=0.99, p=0.00), while a significant correlation was observed between depression symptoms and winter precipitation (rho=0.34). On the other hand, SI was lower in the summer, showing a correlation with the longer period of daylight (rho=-0.39), sunrise time (rho=-0.44), and sunset time (rho=0.39). The sleep patterns indicated that during winter, these older adults went to bed earlier (t=-6.49, p=0.00) and got up later (t=2.02, p=0.04), had more total minutes in bed (t=-6.69, p=0.00), and exhibited longer TST (t=-7.41, p=0.00). During winter, sleep increased (p=0.02), and the temperature was correlated with total minutes in bed (rho=0.32), TST (rho=0.30), and bedtime (rho=-0.33). However, during winter, sunrise and sunset time correlated with WASO (rho=-0.33, rho=0.33, respectively), and total counts (rho=-0.32, rho=0.32, respectively). In conclusion, this study highlights the influence of seasonal variations, including weather factors, on physical activity, depressive symptoms, and sleep patterns in older adults living alone in rural areas. The findings emphasise the importance of considering these seasonal dynamics in designing tailored interventions to promote physical activity, improve mental well-being, and enhance sleep quality in this vulnerable population.

This study examines family-centered strategies used by Maine’s Part C early intervention providers to support families residing in rural communities who may be experiencing vulnerabilities. Through focus groups, early intervention providers shared strategies and barriers when supporting families. Rurality-focused segments impacting early intervention services were identified in the following themes: (a) teaming and collaboration, (b) professional learning, (c) Part C implementation, and (d) resources and services. State funding and policies that focus on rurality may be an important contributing factor to strengthening structural inequities and increasing recruitment and retention of providers in rural areas.

This paper discusses the challenges faced by rural learners in South Africa in the context of the world pandemic commonly known as COVID-19. Rural learners face unprecedented challenges in adjusting to a new mode of life and learning, the latter being characterised by the predominant use of online, learning management systems and low-tech applications. The paper is informed by critical emancipatory research, I used participatory action research. A total of 10 learners and five teachers participated via Whatsapp. The paper answers two questions: what are the learning challenges faced by rural learners in South Africa, and how can online learning be enhanced in the context of COVID-19? The findings suggest that, while the South African government is promoting online learning as the only alternative in the context of COVID-19, this mode excludes many rural learners from teaching and learning, due to a lack of resources to connect to the internet, the learning management system, and low-tech software. The paper argues that rural learners are critical stakeholders in education and in the fight against COVID-19, and they cannot be left behind in efforts to fight the pandemic. 

The concept of disaster resilience has gained attention in political spheres and news outlets over the past few years, yet relatively few empirical measures of the concept exist. Furthermore, research into urban resilience has dwarfed our understanding of disaster resilience in rural places. This schism in what is known about the differences between urban and rural places becomes the topic of this article. Employing a suite of spatial and statistical techniques using an established measure of community resilience, the Baseline Resilience Indicators for Communities (BRIC), we focus on two key questions to better explain the resilience divide between urban and rural areas of the United States. Nonparametric rank analysis, analysis of variance, and logistic regression help describe the relationships between rurality and disaster resilience in contrast to resilience in urban areas. Pinpointing the driving factors, or characteristics, of resilience in rural America compared to metropolitan America, accomplished through binary logistic regression, revealed notable distinctions. Resilience in urban areas is primarily driven by economic capital, whereas community capital is the most important driver of disaster resilience in rural areas. Within rural areas there is considerable spatial variability in the components of disaster resilience. This suggests that attempts to enhance resilience cannot be approached using a one-size-fits-most strategy given the variability in the primary drivers of disaster resilience at county scales.

Objectives. To examine 47 years of US urban and rural mortality trends at the county level, controlling for effects of education, income, poverty, and race. Methods. We obtained (1) Centers for Disease Control and Prevention WONDER (Wide-ranging ONline Data for Epidemiologic Research) data (1970–2016) on 104 million deaths; (2) US Census data on education, poverty, and race; and (3) Bureau of Economic Analysis data on income. We calculated ordinary least square regression models, including interaction models, for each year. We graphed standardized parameter estimates for 47 years. Results. Rural–urban mortality disparities increased from the mid-1980s through 2016. We found education, race, and rurality to be strong predictors; we found strong interactions between percentage poverty and percentage rural, indicating that the largest penalty was in high-poverty, rural counties. Conclusions. The rural–urban mortality disparity was persistent, growing, and large when compared to other place-based disparities. The penalty had evolved into a high-poverty, rural penalty that rivaled the effects of education and exceeded the effects of race by 2016. Public Health Implications. Targeting public health programs that focus on high-poverty, rural locales is a promising strategy for addressing disparities in mortality.

The unique health and aging challenges of rural populations often go unnoticed. In fact, the rural United States is home to disproportionate shares of older and sicker people, there are large and growing rural–urban and within-rural mortality disparities, many rural communities are in population decline, and rural racial/ethnic diversity is increasing. Yet rural communities are not monolithic, and although some rural places are characterized by declining health, others have seen large improvements in population health. We draw on these realities to call for new research in five areas. First, research is needed to better describe health disparities between rural and urban areas and, because rural places are not monolithic, across rural America. Second, research is needed on how trends in rural population health and aging are affecting rural communities. Third, research is needed on the ways in which economic well-being and livelihood strategies interact with rural health and aging. Fourth, we need to better understand the health implications of the physical and social isolation characterizing many rural communities. Finally, we argue for new research on the implications of local natural environments and climate change for rural population health and aging.