Rural-urban Health Disparities Research Articles

Reduced burden on urban hospitals by strengthening rural health facilities: Perspective from India

ABSTRACT In India, rural–urban health disparities have been persisting over a period. Migration of patients from rural to urban is an integral part of population dynamics thereby creating an additional burden on urban hospitals. Over the decade, India has made significant advances in health in reducing the rural-urban gap. The article highlights how the strengthening of rural healthcare facilities has reduced the burden of urban hospitals. Secondary data on the usage of public and private healthcare facilities from two rounds of the National Family Health Survey (NFHS) conducted in 2016 and 2021 and the Rural Health Statistics 2021-2022 were analyzed. The proportion of beneficiaries seeking care from public health facilities has increased from 41.9% to 45.7% in rural areas and 31% to 35.3% in urban areas between 2014 to 2017. The institutional deliveries have increased from 56% to 69.2% in rural areas and from 42% to 48.3% in urban areas. The State and local level interventions such as the upgradation of existing physical infrastructure, human resources, regular supply of medicines and consumables, development of referral linkages, patient transportation, and enhancing community participation have strengthened the rural healthcare system. Adequate utilization of the resources is crucial to addressing the lag and alleviating the rural-urban divide.

DOES RURAL LIVING INCREASE RISK FOR MORTALITY? FINDINGS FROM TWO LONGITUDINAL STUDIES

Abstract Most prior work on rural-urban health disparities has been conducted with population-level data, which is limited in its capacity for causal inferences about individuals and lifespan health. The present study leverages individual-level longitudinal data, spanning up to 29 years, to understand how rurality-urbanicity predicts individuals’ risk for all-cause mortality; whether these effects hold above and beyond socioeconomic status; and whether the association between rurality-urbanicity and mortality risk varies by sex, socioeconomic status, race, and partner status. The present pre-registered study uses data from two large longitudinal studies of U.S. Americans (HRS and MIDUS; total N = ~55,000), who reported on their socio-demographic characteristics, had their addresses linked to geographical indicators (i.e., rural-urban continuum codes), and have data from the National Death Index regarding vital status and survival time. Using a series of cox proportional hazards regression models, findings showed that suburban and rural residents were at a 12% and 18% greater risk for earlier all-cause mortality compared to urban residents in HRS, but the effects of rurality-urbanicity on mortality risk were non-significant in MIDUS. The longitudinal effects of rurality-urbanicity on mortality risk were largely independent of the effects of socioeconomic status. Finally, the three statistically significant interaction effects suggest that the strength and direction of the association between rurality-urbanicity and mortality risk differed by race (HRS, MIDUS) and partner status (HRS). The discussion elaborates on the theoretical implications for our understanding of social determinants of health, as well as practical implications for health equity in rural areas.

Rurality and race in heart failure risk: Insights from the Southern Community Cohort Study.

Rural-urban health disparities are apparent in the burden of disease and health outcomes, including cardiovascular disease (CVD), specifically heart failure (HF). However, the factors influencing these disparities are not fully understood. Study and results: Among 27,115 participants in the Southern Community Cohort Study (SCCS) (mean age: 54 years (47-65)), 18,647 (68.8%) were black, 8,468 (32.3%) were white, and 20% resided in rural areas. Over a median 13-year follow-up period, 7,542 HF events occurred (rural = 1,865 vs. urban = 5,677). The age-adjusted HF incidence was 29.6 (95% CI, 28.9-30.5) and 36.5 (95% CI, 34.9-38.3) per 1,000 person-years for urban and rural participants, respectively (P<.001). The risk of HF associated with rurality varied by race and sex. Rural black men had the highest risk across all groups (HR, 1.34; 95% CI, 1.19-1.51) (age-adjusted incidence rate: 40.4/1000 person-years (95% CI, 36.8-44.3)) followed by black women (HR, 1.18; 95% CI, 1.08-1.28) and white women (HR, 1.22; 95% CI, 1.07-1.39). Rurality was not associated with HF risk among white men (HR, 0.97; 95% CI, 0.81-1.16). This large study shows that rural populations have an increased incidence of HF, which is particularly striking among women and black men, independent of individual-level biological, behavioral, and sociocultural risk factors. It also shows the need for further investigation into the rurality-associated risk of HF, the impact of preventive care utilization on the risk of HF and interpersonal, community, or societal factors that could contribute to rural-urban disparities. This will help to guide public health efforts aimed at HF prevention among rural populations.

Rural-urban disparities in health outcomes, clinical care, health behaviors, and social determinants of health and an action-oriented, dynamic tool for visualizing them.

While rural-urban disparities in health and health outcomes have been demonstrated, because of their impact on (and intervenability to improve) health and health outcomes, we sought to examine cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and social determinants of health (SDOH) between rural and non-rural counties in the pre-pandemic era (2015 to 2019), and to present a Health Equity Dashboard that can be used by policymakers and researchers to facilitate examining such disparities. Therefore, using data obtained from 2015-2022 County Health Rankings datasets, we used analysis of variance to examine differences in 33 county level attributes between rural and non-rural counties, calculated the change in values for each measure between 2015 and 2019, determined whether rural-urban disparities had widened, and used those data to create a Health Equity Dashboard that displays county-level individual measures or compilations of them. We followed STROBE guidelines in writing the manuscript. We found that rural counties overwhelmingly had worse measures of SDOH at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures. When rural-urban gaps narrowed, it tended to be in measures wherein rural counties were outperforming urban ones in the earlier period. In conclusion, our findings highlight the need for policymakers to prioritize rural settings for interventions designed to improve health outcomes, likely through improving health behaviors, clinical care, social and environmental factors, and physical environment attributes. Visualization tools can help guide policymakers and researchers with grounded information, communicate necessary data to engage relevant stakeholders, and track SDOH changes and health outcomes over time.

Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand

ObjectivesExamine the impact of two generic—urban–rural experimental profile (UREP) and urban accessibility (UA)—and one purposely built—geographic classification for health (GCH)—rurality classification systems on the identification of rural–urban health disparities in...

RURAL-URBAN DISPARITIES IN COMORBIDITY BETWEEN OBESITY AND DIABETES AMONG OLDER ADULTS

Abstract Obesity and diabetes are common among older Americans. While obesity is a risk factor for diabetes, research on rural-urban health disparities among older adults has reported paradoxical results. That is, living in rural areas is a risk marker of obesity, whereas rural residency is inversely associated with diabetes. This study utilized multiple national data resources to diagnose the inconsistency, including the 2006-2016 RAND HRS Longitudinal data, biomarker data, the 2010 US Census, and the USDA food access information. HRS survey participants 65 to 97 years of age comprised the study sample (N=6,476). General generalized estimating equation models with survey weights were used to estimate associations with measured obesity and diabetes. Age, race, Hispanic status, foreign-born status, education, marital status, working status, wealth, physical activity, smoking, drinking, ADL, IADL, loneliness, depression, cholesterol, HDL, CRP, and Cystatin C were included as control variables. Findings suggest that rurality is associated with a higher likelihood of obesity and a lower likelihood of diabetes, particularly for older women. However, by adjusting for confounders, these relationships disappeared. Instead, social isolation indicated significant associations with a lower likelihood of obesity and a higher likelihood of diabetes among older women. Contrary to prior research, the food desert and food insecurity measures did not correlate with both outcome variables. Further investigation on food consumption and dietary behavior among socially isolated older women is needed.

Understanding the longitudinal dynamics of rural–urban mental health disparities in later life in China

Objectives Understanding longitudinal patterns of rural–urban mental health disparities is vital for effective intervention and policy development in China. However, few studies have estimated separate effects of birth-cohort and ageing and examined the role of community resources on health inequalities. Methods Drawing data from the China Health and Retirement Longitudinal Study (2011–2018), this study employed multilevel modelling to identify the mental health trajectories of rural, peri-urban, urban older adults by cohort and the community effects. Results The changes in the mental health gap between rural, peri-urban and urban older adults over time varied by birth cohorts. Among younger cohorts aged under 65, the mental health disparities between rural, peri-urban and urban residents increased as they got older. Underdeveloped community infrastructure greatly explained the rural health disadvantage. Conclusion The study indicates increasing rural–urban health disparities at the onset of later life. Improving community infrastructure in rural and peri-urban areas is vital to minimise rural–urban health gaps.

Prevalence of obesity and elevated body mass index along a progression of rurality: A cross-sectional study - The Canadian Longitudinal Study on Aging.

Obesity is an important public health concern, and large studies of rural-urban differences in prevalence of obesity are lacking. Our purpose is to compare body mass index (BMI) and obesity in Canada using an expanded definition of rurality. A cross-sectional analysis of self-reported BMI across diverse communities of Canadians aged 45-85 years was conducted using data from the Canadian Longitudinal Study on Aging (CLSA), a national sample representative of community-dwelling residents. Rurality was identified in the CLSA based on residential postal codes, which were divided into 4 categories: urban, peri-urban, mixed and rural. Logistic regression models were constructed to calculate adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs) between obesity (BMI ≥30 kg/m2 from self-reported weight and height) and rurality, adjusting for age, sex, province, marital status, number of residents in household and household income. Twenty-one thousand one hundred and twenty-six Canadian residents aged 45-85 years, surveyed during 2010-2015, were included. 26.8% were obese. Obesity was less prevalent amongst urban (25.2%) than rural (30.3%, P < 0.0001), mixed (28.7%, P < 0.0001) or peri-urban communities (28.1%, P < 0.0001). When compared to urban areas, the aOR (95% CI) for obesity was 1.09 (1.00-1.20) in rural regions and 1.20 (1.08-1.35) in peri-urban settings. In areas of mixed urban and rural residence, the aOR was 1.12 (0.99-1.27). One in four Canadian adults were obese. Living in a non-urban setting is an independent risk factor for obesity. Rural-urban health disparities could underlie rural-urban differences, but further research is needed.

Rural-urban disparities in the nutritional status of younger adolescents in Tanzania.

Research on geographic differences in health focuses largely on children less than five years; little is known about adolescents-and even less regarding younger adolescents-a vulnerable group at a critical stage of the life course. Africa's rapid population growth and urbanization rates, coupled with stagnant rates of undernutrition, further indicate the need for country-specific data on rural-urban health disparities to inform development policies. This study examined rural-urban disparities in body mass index-for-age-and-sex (BAZ) and height-for-age-and-sex z-scores (HAZ) among younger adolescents in Tanzania. Participants were randomly selected adolescents aged 10-14 years (N = 1,125) residing in Kilosa (rural) and Moshi (urban) districts of Tanzania. Individual and household-level data were collected using surveys and anthropometric data was collected on all adolescents. Age, sex, household living conditions, and assets were self-reported. BAZ and HAZ were calculated using the WHO reference guide. The prevalence of undernutrition was 10.9% among rural and 5.1% among urban adolescents (p<0.001). Similarly, stunting prevalence was greater in rural (64.5%) than urban (3.1%) adolescents (p<0.001). After adjusting for covariates, rural residence was significantly and inversely associated with BAZ (B = -0.29, 95% CI: -0.52, -0.70, p = 0.01), as well as with HAZ (B = -1.79, 95% CI: -2.03, -1.54, p<0.001). Self-identified males had lower BAZ (B = -0.23, 95% CI: -0.34, -0.11, p<0.001) and HAZ (B = -0.22, 95% CI: -0.35, -0.09, p = 0.001) than self-identified female adolescents. Rural-urban disparities in nutritional status were significant and gendered. Findings confirm place of residence as a key determinant of BAZ and HAZ among younger adolescents in Tanzania. Targeted gender-sensitive interventions are needed to limit growth faltering and improve health outcomes in rural settings.

Rural-urban Health Disparities for Cardiovascular Disease in the Republic of Kazakhstan

BACKGROUND: An important global public health problem in many economically developed countries, in particular Kazakhstan, is the leading cause of incidence of and mortality from cardiovascular disease (CVD). AIM: The purpose of our study was to compare incidence and mortality from CVD in urban and rural areas of the Republic of Kazakhstan in 2019 based on statistics. METHODS: We conducted a descriptive study, which based on secondary data from the statistical reports on the Republic of Kazakhstan «Health of the Republic of Kazakhstan and the activities of health care organizations». Information from this database were generated by 14 provinces, three cities of republican significance and whole Kazakhstan. In addition, these indicators were divided for rural and urban settlements. RESULTS: Data from our epidemiological study of the incidence of and mortality from cardiovascular disease (CVD) in all provinces indicate a relatively high incidence of and mortality from CVD among urban population of the Republic of Kazakhstan. Among urban residents (per 100,000 population), the highest incidence rates of CVD was in Akmola province (3771.7), the ischemic heart disease (IHD) was in Almaty city (850.8), arterial hypertension (AH) was in Akmola province (2623.8), acute myocardial infarction was in the North Kazakhstan province (212.9), the highest mortality from CVD was in Karaganda province (365), the IHD was in East Kazakhstan province (135.4). Among rural residents (per 100,000 population), there are high incidence rates of CVD in the East Kazakhstan province (3452.8), the IHD in Zhambyl province (713.1), AH in the East Kazakhstan province (1871.4), acute myocardial infarction in Atyrau province (148.3), the highest mortality from CVD was in East Kazakhstan province (201.9), and the IHD also in East Kazakhstan province (77.3). CONCLUSIONS: There is rural-urban health inequality in many countries and CVD is not the exclusion. In the Republic of Kazakhstan, the rural population has got lower rates of incidence and mortality. This may be due to the bad access to medical facilities that are typical for rural areas of Kazakhstan and the shortage of modern diagnostic equipment which may interfere with timely diagnosis.

Assessment of patient satisfaction, financial, and environmental impacts of teleoncology.

278 Background: There was rapid adoption of teleoncology at Veterans Health Administration (VHA) during the COVID-19 pandemic. One-third of 9 million VHA-enrolled Veterans live in rural areas. While digital solutions can expand capacity, enhance care access, and reduce financial burden, they may also exacerbate rural-urban health disparities. Careful evaluation of patients’ perceptions and policy tradeoffs are necessary to optimize teleoncology post-pandemic. Methods: Patients with ≥1 teleoncology visit with medical, surgical, or radiation oncology between March 2020 and June 2020 identified retrospectively. Validated, Likert-type survey assessing patient satisfaction developed. Follow-up survey conducted on patients with ≥1 teleoncology visit from August 2020 to January 2021. Travel distance, time, cost, and carbon dioxide (CO2) emissions calculated based on zip codes. Results: 100 surveys completed (response rate, 62%). Table with demographics. Patients overall satisfied with teleoncology (83% ‘Agree’ or ‘Strongly Agree’) but felt less satisfied than in-person visits (47% ‘Agree’ or ‘Strongly Agree’). Audiovisual component improved patient perception of involvement in care (two-sided, p = 0.0254), ability to self-manage health/medical needs (p = 0.0167), and comparability to in person visits (p = 0.0223). Follow-up survey demonstrated similar satisfaction. Total travel-related savings: 86,470 miles, 84,374 minutes, $49,720, and 35.5 metric tons of CO2. Conclusions: Veterans are broadly satisfied with teleoncology. Audiovisual capabilities are critical to satisfaction. This is challenging for rural populations with lack of technology access. Patients experienced financial and time savings, and society benefitted from reduced carbon emissions. Continued optimization needed to enhance patient experience and address secondary effects.[Table: see text]

Teleoncology for Veterans: High Patient Satisfaction Coupled With Positive Financial and Environmental Impacts.

There was rapid adoption of teleoncology care in the Veterans Health Administration during the COVID-19 pandemic. One third of 9 million Veterans Health Administration enrolled Veterans live in rural areas. Although digital solutions can expand capacity, enhance care access, and reduce financial burden, they may also exacerbate rural-urban health disparities. Careful evaluation of patients' perceptions and policy tradeoffs are necessary to optimize teleoncology postpandemic. Patients with ≥ 1 teleoncology visit with medical, surgical, or radiation oncology between March 2020 and June 2020 were identified retrospectively. Validated, Likert-type survey assessing patient satisfaction was developed. Follow-up survey was conducted on patients with ≥ 1 teleoncology visit from August 2020 to January 2021. Travel distance, time, cost, and carbon dioxide emissions were calculated based on zip codes. A hundred surveys were completed (response rate, 62%). Patients overall were satisfied with teleoncology (83% Agree or Strongly Agree) but felt less satisfied than in-person visits (47% Agree or Strongly Agree). Audiovisual component improved patient perception of involvement in care, ability to self-manage health or medical needs, and comparability to in-person visits. Follow-up survey demonstrated similar satisfaction. Total travel-related savings are as follows: 86,470 miles, 84,374 minutes, $49,720 US dollars, and 35.5 metric tons of carbon dioxide. Veterans are broadly satisfied with teleoncology. Audiovisual capabilities are critical to satisfaction. This is challenging for rural populations with lack of technology access. Patients experienced financial and time savings, and society benefitted from reduced carbon emissions. Continued optimization is needed to enhance patient experience and address secondary effects.

Rural-urban differences in stroke risk

Rural-urban health disparities in life expectancy are large and increasing, with the rural-urban disparity stroke mortality serving as a potential contributor. Data from Vital Statistics shows an unexplained temporal pattern in the rural-urban disparity in stroke-specific mortality, with the magnitude of the disparity increasing from 15% to 25% between 1999 and 2010, but subsequently decreasing to 8% by 2019. This recent decrease in the magnitude of the rural-urban disparity in stroke mortality appears to be driven by a previously unreported plateauing of stroke mortality in urban areas and a continued decline of stroke mortality in rural areas. There is a need to better understand the contributors to these temporal changes; however, a general lack of temporal data on potential contributors prevents this investigation. However considering contributors to the rural-urban differences pooled across time, an overall a higher stroke incidence in rural areas appears to be a contributor to the higher rural stroke mortality, with this higher incidence potentially associated with a higher prevalence of stroke risk factors in rural areas. Conversely, studies assessing rural-urban disparities in stroke case fatality show smaller and inconsistent associations. To the extent that disparities in case fatality do exist, there are many studies showing rural-urban disparities in stroke care could be contributing. While these data offer insights to the overall rural-urban disparities in stroke mortality, additional data are needed to help understand temporal changes in the magnitude of the rural-urban stroke mortality disparity.

Rural-urban differences in informal caregiving and health-related quality of life.

Rural-urban health disparities are pervasive among older adults. Rural US locations have a disproportionately high population of older adults, have reduced access to services, and are therefore more reliant on family and friends for care. However, little is known about rural-urban disparities among the 40+ million informal caregivers nationwide. There is a critical need to understand how rural-urban disparities impact caregiving experiences and health-related quality of life (HRQoL). The objectives of this study were to assess rural-urban differences in informal caregiving status, caregiving intensity (caregiving hours/week and types of care provided), and caregiver HRQoL. Data were abstracted from the 2018 Behavioral Risk Factor Surveillance System. The primary measure of rural-urban status was "metropolitan status." Associations between rural-urban status and caregiving and rural-urban differences in caregiving intensity and HRQoL were examined using generalized linear models, controlling for confounding and accounting for complex sampling. Rural respondents were more likely to be caregivers than urban respondents (OR = 1.17, 95% CI: 1.02-1.34). After adjustment for confounders, rural caregivers were more likely than urban caregivers to provide 20 or more hours of caregiving per week (OR 1.38, 95% CI: 1.07-1.77), although the findings for health and HRQoL were somewhat mixed. Results indicate that rural older adults offer more care than urban counterparts, which may have implications for caregiver health, well-being, and quality of life. These results can inform policies designed to improve caregiver health, and facilitate the translation and adaptation of existing policies, programs, and interventions to address rural caregivers' needs.

Strategies for Disseminating and Implementing COVID-19 Vaccines in Rural Areas.

The United States has well-documented rural–urban health disparities, and it is imperative that these are not exacerbated by an inefficient rollout of coronavirus disease 2019 (COVID-19) vaccines to rural areas. In addition to the preexisting barriers to delivering and receiving health care in rural areas, such as high patient:provider ratios and long geographic distances between patients and providers, rural residents are significantly more likely to say they have no intention of receiving a COVID-19 vaccine, compared with urban residents. To overcome these barriers and ensure that rural residents receive the vaccine, officials and communities should look to previous research on how to communicate vaccine information and implement successful vaccination programs in rural areas for guidance and concrete strategies to use in their local efforts.Graphical

Rural-Urban Differences in Informal Caregiving, Caregiving Intensity, and Health-Related Quality of Life

Introduction Rural areas in the US have a disproportionately high population of older adults and have reduced access to services. Older adults in rural areas are more reliant on family and friends for care. However, little is known about rural-urban disparities among the 40+ million caregivers nationwide. As rural-urban health disparities are pervasive among older adults, there is a need to understand how rural-urban disparities impact caregiving experiences and health-related quality of life (HRQoL). The objectives of this study were to examine rural-urban differences caregiving, caregiving intensity (caregiving hours/week and types of care provided), and caregiver HRQoL. Methods Data were abstracted from the 2009 Behavioral Risk Factor Surveillance System (latest dataset to include county of residence and caregiver module). The primary measure of rural-urban status was Index of Relative Rurality (IRR) decile. Associations between rural-urban status and caregiving and rural-urban differences in caregiving intensity and HRQoL were examined using generalized linear models, controlling for confounding and accounting for complex sampling. Results Rural respondents were more likely to be caregivers than urban respondents (IRR decile OR=1.015, 95%CI 1.014-1.016). Rural caregivers, on average, provided 2.43 hours/week more caregiving for each one-decile increase in IRR decile (95%CI 2.34-2.52) and had worse overall HRQoL (OR=1.34, 95%CI 1.33-1.35). Conclusion Rural informal caregivers offer higher levels of care than urban counterparts, and increased caregiving in rural areas is associated with reduced HRQoL. These results can inform policies designed to improve caregiver health, and facilitate the translation of existing programs and interventions to address rural caregivers’ needs.

Defining Rurality: Structural Relationships, Regional Differences, and Applications for Research With Older Adults

There is no universal definition of rurality due to the heterogeneity in what makes a place “rural” or “urban”. This study explored how elements of rurality are related to each other, and how the elements that define rurality vary by region. Data were abstracted for all 1948 non-metropolitan counties in the contiguous 48 states on rurality. K-means cluster analyses (k=4-8) were conducted to examine classification structures among component variables examining regional differences. In the South region, the majority (51.2%) were “Type 2” counties: low population size and density but higher urbanized population. The Midwest had a majority of “Type 3” counties (56.4%): intermediate for population size and density, but higher distances to metro areas. These exploratory findings underscore the heterogeneity and regional variability in rurality and how those measures are structurally related to each other, and essential to understanding those factors that truly drive rural-urban health disparities for older adults. Part of a symposium sponsored by the Rural Aging Interest Group.

Estimating rural–urban disparities in self-rated health in China: Impact of choice of urban definition

Objective: We use the 2014 China Family Panel Studies (CFPS), which includes anchoring vignettes, to provide an up-to-date assessment of rural–urban health disparities as measured by self-rated health (SRH) in China. Our analysis is based on multiple definitions (hukou and the two different residence-based definitions) of rural–urban and migration status; previous research was inconclusive due to the use of different definitions and concerns about status-based differential health expectations (reporting heterogeneity). Results: We find a nonlinear difference between rural and urban Chinese in how they self-assess health status, regardless of the urban definition used. Urban respondents do not always hold a higher standard for self-assessment of health. Instead, their rating styles depend on the level of latent health. After controlling for the reporting heterogeneity, we find on average a slight urban advantage in SRH, but it is most pronounced when using the statistical (density dependent) definition of urban. Contribution: We study rural–urban health disparities based on three different urban definitions and migration status. Although we examine the urban definitions that are specific to China, we demonstrate a mindful approach when multiple definitions exist and caution against any simplistic approach that ignores context-specific urban definition. We also provide clear illustrations of the different types of reporting heterogeneity, as well as a way to visualize the cut-points, thresholds, and latent health estimates.

The southern rural health and mortality penalty: A review of regional health inequities in the United States.

Rural-urban differences in morbidity and mortality across the United States have been well documented and termed the "rural mortality penalty". However, research studies frequently treat rural areas as homogeneous and often do not account for geospatial variability in rural health risks by both county, state, region, race, and sex within the United States. Additionally, people living in the rural South of the US have higher rates of morbidity and mortality compared to both their urban counterparts and other rural areas. Of those living in southern rural communities, people of color experience higher rates of death and disease compared to white populations. Although there is a wealth of research that uses individual-level behaviors to explain rural-urban health disparities, there is less focus on how community and structural factors influence these differences. This review focuses on the "southern rural health penalty", a term coined by the authors, which refers to the high rate of mortality and morbidity in southern rural areas in the USA compared to both urban areas and non-southern rural places. We use macrosocial determinants of health to explain possible reasons for the "southern rural health penalty". This review can guide future research on rural health between southern and non-southern populations in the US and examine if macrosocial determinants of health can explain health disparities within southern rural populations.

Rural–Urban Differences in Health Care Access and Utilization under the Medicaid Expansion

AbstractThe Affordable Care Act's Medicaid expansion ushered in substantial increases in enrollment in the public insurance program. I analyzed rural–urban differences in access to health insurance, financial access to primary care providers, use of preventive care, and use of emergency department for medical reasons that did not lead to hospitalization. Low‐income rural residents were found to have greater reduction in likelihood of uninsurance than their urban counterparts. While an increase in the likelihood of routine check‐ups was found to be significant only among rural residents, a decrease in the likelihood of repeated visits to the emergency departments for nonurgent reasons was only significant among the urban population. I highlight the importance of understanding rural experiences to reduce rural–urban health disparities.