Rural-urban health disparities for mood disorders and obesity in a midwestern community

Rural Health Disparities in Allergy, Asthma, and Immunologic Diseases: The Current State and Future Direction for Clinical Care and Research

Rural health disparities are well documented and continue to jeopardize the long-term health and wellness for the millions of individuals who live in rural America. The disparities observed between urban and rural residents encompass numerous morbidity and mortality measures for several chronic diseases and have been referred to as the "rural mortality penalty." Although the unmet health needs of rural communities are widely acknowledged, little is known about rural health disparities in allergies, asthma, and immunologic diseases. Furthermore, the intersection between rural health disparities and social determinants of health has not been fully explored. To achieve a more complete understanding of the factors that perpetuate rural health disparities, greater research efforts followed by improved practice and policy are needed that account for the complex social context within rural communities rather than a general comparison between urban and rural environments or focusing on biomedical factors. Moreover, research efforts must prioritize community inclusion throughout rural areas through meaningful engagement of stakeholders in both clinical care and research. In this review, we examine the scope of health disparities in the rural United States and the impact of social determinants of health. We then detail the current state of rural health disparities in the field of allergy, asthma, and immunology. To close, we offer future considerations to address knowledge gaps and unmet needs for both clinical care and research in addressing rural health disparities.

Impact of socioeconomic status on depression clinical outcomes at six months in a Midwestern, United States community

INTRODUCTION

Lower socioeconomic status (SES) has been associated with poor healthcare outcomes in depression. However, reliable individual-level SES data rarely exists for clinical research. The HOUSES index relies on publicly available data allowing for evaluation of individual-level SES on patient outcomes.

HYPOTHESIS

Primary care patients with depression within the lower SES quartile (Quartile 1 vs. Quartile 4, of the HOUSES index) would experience worse clinical outcomes of their symptoms six months after diagnosis.

STUDY DESIGN

A retrospective cohort study which followed 4313 adult primary care patients that were diagnosed with depression during the study period of 2008-2015. The outcome measures were the six month PHQ-9 scores.

RESULTS

At six months, a higher HOUSES quartile was associated with greater odds of remission of depressive symptoms (RDS) and lower odds of persistent depressive symptoms (PDS), after controlling for covariates. Patients in Quartile 4 had 27% more likelihood of RDS and a 24% lower likelihood of PDS at six months compared to a Quartile 1 patient.

LIMITATIONS

As a retrospective study only can observe associations but not causation. Only one institution participated and not all treatments were readily available, limiting the generalizability of these findings.

CONCLUSIONS

Lower SES as demonstrated by a lower HOUSES quartile (Quartile 1 versus 4) was associated with lower odds of RDS and increased odds of PDS at six months. HOUSES index is a useful tool for identifying patients at risk for worse clinical outcomes and may help health care systems plan resource allocation for depression care.

Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis

Objective

To perform a geospatial and temporal trend analysis for coronavirus disease 2019 (COVID-19) in a Midwest community to identify and characterize hot spots for COVID-19.

Participants and Methods

We conducted a population-based longitudinal surveillance assessing the semimonthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, through October 31, 2020. As urban areas accounted for 84% of the population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hot spots for COVID-19 in urban areas (Rochester and other small cities) of Olmsted County, MN, during the study period by using kernel density analysis with a half-mile bandwidth.

Results

As of October 31, 2020, a total of 37,141 individuals (30%) were tested at least once, of whom 2433 (7%) tested positive. Testing rates among race groups were similar: 29% (black), 30% (Hispanic), 25% (Asian), and 31% (white). Ten urban hot spots accounted for 590 cases at 220 addresses (2.68 cases per address) as compared with 1843 cases at 1292 addresses in areas outside hot spots (1.43 cases per address). Overall, 12% of the population residing in hot spots accounted for 24% of all COVID-19 cases. Hot spots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hot spots tended to be minorities and from a lower socioeconomic background.

Conclusion

Geographic and residential risk factors might considerably account for the overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (eg, testing/tracing and vaccine rollout) for populations at risk for COVID-19.