Trends in lifespan variation across the spectrum of rural and urban places in the United States, 1990-2017

Double vulnerability? Examining the effect of living in nonmetropolitan areas within non-expansion Medicaid states on health status among working-age adults in the United States, 2022-2024

Objective

To examine whether living in nonmetropolitan areas within a state that has not expanded Medicaid is associated with poor/fair self-reported health status among working-age adults in the United States.

Methods

We analyzed data from the 2022-2024 Current Population Survey (n = 220, 601, ages 25-64). Self-reported health was dichotomized as having reported poor/fair or good/very good/excellent health status. We produced a four-level measure of the overlap between residential and policy contexts indicating whether the respondent lived in a metropolitan or nonmetropolitan area within a state that had or had not expanded Medicaid coverage by 2023. Multilevel logistic regression models were fit to examine the association between our measure of residence-policy overlaps and poor/fair self-reported health status while accounting for individual and state-level characteristics.

Results

About 3.7 % of respondents resided in nonmetropolitan areas within non-expansion states. Approximately 11.4 % of respondents reported poor/fair self-reported health, with respondents living in nonmetropolitan areas within non-expansion states having the highest rates of poor/fair self-reported health status (18.1 %). Living in a nonmetropolitan area within non-expansion states was associated with higher odds of poor/fair self-reported health status for the overall population and by sex.

Conclusion

In this nationally representative and racially diverse sample, we found that individuals residing in nonmetropolitan areas in non-expansion Medicaid states were more likely to report poor/fair self-reported health status. This effect was present for the majority of the population subgroups. Our findings underscore the double vulnerability faced by populations living in these residence-policy overlaps and the need for targeted interventions.

Diverging cognitive benefits from education between rural and urban middle-aged and older adults in the USA

BACKGROUND

Subjective cognitive decline (SCD) is defined as self-reported increase in confusion or memory loss. There is limited research on the interplay between rural-urban residence and education on SCD.

AIMS

Examine rural-urban differences in SCD, and whether education moderates this relationship.

METHOD

Respondents aged ≥45 years were queried about SCD in the 2022 Behavioral Risk Factor Surveillance System data, creating a sample size of 63 890. A logistic regression analysed the association between rural-urban residence and SCD, and moderation was tested by an interaction with education.

RESULTS

SCD was more common among rural (12.0%) compared with urban (10.7%) residents. Rural residence was associated with 9% significantly higher odds of SCD compared with urban residence after adjusting for sociodemographic and health covariates (adjusted odds ratio (aOR) = 1.09, P = 0.01). There was a negative relationship between education level and SCD, including the association of college degree with 15% lower odds of SCD compared with less than high school degree (aOR = 0.85, P < 0.01). Education was a significant moderator, with higher education associated with lower odds of SCD for urban, but not rural, residents.

CONCLUSIONS

Rural setting and lower education were associated with higher odds of SCD, but higher education was protective for only urban residents. These results indicate that higher education may be a gateway for more opportunities and resources in urban settings, with cascading impacts on cognition. Future research should examine reasons for the diverging cognitive benefits from education depending on rural-urban residence.

Long-Term Mortality Following SARS-CoV-2 Infection in Rural Versus Urban Dwellers With Autoimmune or Inflammatory Rheumatic Disease: A Retrospective Cohort Analysis From the National COVID Cohort Collaborative

OBJECTIVE

Autoimmune or inflammatory rheumatic diseases (AIRDs) increase the risk for poor COVID-19 outcomes. Although rurality is associated with higher post-COVID-19 mortality in the general population, whether rurality elevates this risk among people with AIRD is unknown. We assessed associations between rurality and post-COVID-19 all-cause mortality, up to two years post infection, among people with AIRD using a large nationally sampled US cohort.

METHODS

This retrospective study used the National COVID Cohort Collaborative, a medical records repository containing COVID-19 patient data. We included adults with two or more AIRD diagnostic codes and a COVID-19 diagnosis documented between April 2020 and March 2023. Rural residency was categorized using patient residential zip codes. We adjusted for AIRD medications and glucocorticoid prescription, age, sex, race and ethnicity, tobacco or substance use, comorbid burden, and SARS-CoV-2 variant-dominant periods. Multivariable Cox proportional hazards with inverse probability treatment weighting assessed associations between rurality and two-year all-cause mortality.

RESULTS

Among the 86,467 SARS-CoV-2-infected persons with AIRD, we observed a higher risk for two-year post-COVID-19 mortality in rural versus urban dwellers. Rural-residing persons with AIRD had higher two-year all-cause mortality risk (adjusted hazard ratio 1.24, 95% confidence interval 1.19-1.29). Glucocorticoid, immunosuppressive, and rituximab prescriptions were associated with a higher risk for two-year post-COVID-19 mortality, whereas risk with nonbiologic or biologic disease-modifying antirheumatic drugs was lower.

CONCLUSION

Rural residence in people with AIRD was independently associated with higher two-year post-COVID-19 mortality in a large US cohort after adjusting for background risk factors. Policymakers and health care providers should consider these findings when designing interventions to improve outcomes in people with AIRD following SARS-CoV-2 infection, especially among high-risk rural residents.

A descriptive examination of rurality in the Environmental influences on Child Health Outcomes Cohort: Implications, illustrations, and future directions

PURPOSE

The Environmental influences on Child Health Outcomes (ECHO) Cohort has enrolled over 60,000 children to examine how early environmental factors (broadly defined) are associated with key child health outcomes. The ECHO Cohort may be well-positioned to contribute to our understanding of rural environments and contexts, which has implications for rural health disparities research. The present study examined the outcome of child obesity to not only illustrate the suitability of ECHO Cohort data for these purposes but also determine how various definitions of rural and urban populations impact the presentation of findings and their interpretation.

METHODS

This analysis uses data from children in the ECHO Cohort study who had residential address information between January 2010 and October 2023, including a subset who also had height and weight data. Several rural-urban classification schemes were examined with and without collapsing into binary rural/urban groupings (ie, the Rural-Urban Continuum Codes, 2010 Rural-Urban Commuting Area [RUCA] Codes, and Urban Influence Codes).

FINDINGS

Various rural/urban definitions and classification schemes produce similar obesity prevalence (17%) when collapsed into binary categories (rural vs urban) and for urban participants in general. When all categories within a classification scheme are examined, however, the rural child obesity prevalence ranges from 5.8% to 24%.

CONCLUSIONS

Collapsing rural-urban classification schemes into binary groupings erases nuance and context needed for interpreting findings, ultimately impacting health disparities research. Future work should leverage both individual- and community-level datasets to provide context, and all categories of classification schemes should be used when examining 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.

Regional disparities in Dementia-free Life Expectancy in Japan: An ecological study, using the Japanese long-term care insurance claims database

BACKGROUND

The number of people with dementia increases in an aging society; therefore, promoting policies for dementia throughout the community is crucial to creating a dementia-friendly society. Understanding the status of older adults with dementia in each region of Japan will be a helpful indicator. We calculated Dementia-free Life Expectancy and aimed to examine regional disparities and their associated factors.

METHODS

We calculated Dementia-free Life Expectancy and Life Expectancy with Dementia for each secondary medical area in Japan based on the Degree of Independence in Daily Living for the Demented Elderly, using data extracted from the Japanese long-term care insurance claims database. We then conducted a partial least squares regression analysis, the objective variables being Dementia-free Life Expectancy and Life Expectancy with Dementia for both sexes at age 65, and explanatory regional-level variables included demographic, socioeconomic, and healthcare resources variables.

RESULTS

The mean estimated regional-level Dementia-free Life Expectancy at age 65 was 17.33 years (95% confidence interval [CI] 17.27-17.38) for males and 20.05 years (95% CI 19.99-20.11) for females. Three latent components identified by partial least squares regression analysis represented urbanicity, socioeconomic conditions, and health services-related factors of the secondary medical areas. The second component explained the most variation in Dementia-free Life Expectancy of the three, indicating that higher socioeconomic status was associated with longer Dementia-free Life Expectancy.

CONCLUSIONS

There were regional disparities in secondary medical area level Dementia-free Life Expectancy. Our results suggest that socioeconomic conditions are more related to Dementia-free Life Expectancy than urbanicity and health services-related factors.