Ethnicity, Socioeconomic Status, and Health Disparities in a Mixed Rural-Urban US Community—Olmsted County, Minnesota

Wi, Chung Il; Sauver, Jennifer L. St.; Jacobson, Debra J.; Pendegraft, Richard; Lahr, Brian D.; Ryu, Euijung; Beebe, Timothy J.; Sloan, Jeff A.; Rand-Weaver, Jennifer; Krusemark, Elizabeth A.; Choi, Yuri; Juhn, Young J.

doi:10.1016/j.mayocp.2016.02.011

Cited by 42 publications

(20 citation statements)

References 27 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results for prevalence of mood disorders are in contrast to prior work that found no differences based on location of residence [11,17,18]. A prior study conducted by our team [35] examined prevalence of mood disorders, along with four other health conditions, by HOUSES and race/ethnicity in a mixed rural-urban Minnesota population-based sample. That investigation found that health disparities still existed across different SES levels, with greater prevalence of mood disorder observed with higher SES among minority subjects.…”

Section: Discussioncontrasting

confidence: 99%

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

Patten

Juhn

Ryu

et al. 2020

J. Clin. Trans. Sci.

Self Cite

View full text Add to dashboard Cite

Introduction: Prior studies indicate greater disease burden for obesity among rural compared with urban residents but no differences for mood disorder based on geographic location. Recent attention has focused on the need to examine regional rural–urban disparities in disease burden. We focused on mood disorders and obesity prevalence within three southeastern Minnesota counties served by the Mayo Clinic Center for Translational Science Award, in Rochester, Minnesota, as these were top priorities identified in community health needs assessments. Methods: Cross-sectional study to assess the association of rural–urban locality on 5-year (2009–2014) prevalence of mood disorder and obesity obtained using the Rochester Epidemiological Project medical records linkage system, among subjects residing in three mixed rural–urban counties on April 1, 2014. Multivariable analyses adjusted for demographics, socioeconomic status using an individual housing-based measure, and counties. Results: The study cohort (percent rural location) included 91,202 (15%) for Olmsted, 10,197 (51%) in Dodge, and 10,184 (57%) in Wabasha counties. On multivariate analysis, 5-year prevalence of mood disorders and obesity was significantly greater for urban compared with rural residents, after adjusting for confounders; odds ratios (95% confidence intervals): 1.21 (1.17–1.26), P < 0.001, and 1.05 (1.01–1.10), P = 0.016, respectively. Observed effects were not modified in additional models adjusted for health care utilization (HCU; ≥1 general medical examination visit and flu vaccination). Conclusions: Rural–urban health disparities for burden of mood disorders and obesity are independent of socioeconomic status and HCU in a Midwestern community. It is important to assess potential regional heterogeneity of rural–urban disparities on health outcomes.

show abstract

Section: Discussioncontrasting

confidence: 99%

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

Patten

Juhn

Ryu

et al. 2020

J. Clin. Trans. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The effect of socioeconomic status (eg, the clustering of education level, rural/urban residential location, employment status or household income), across ethnic groups may be particularly important and influential as it may emphasize differences within Whites and between average-to-high socioeconomic status Whites and all other ethnic groups, potentially regardless of SES. 39,40 Health disparities among different ethnic minorities are similarly "confounded" by differences in psychosocial factors such as stress, self-esteem, mental illness, access to health care, neighbourhood characteristics (crime, safety, air pollution, noise) which can all disrupt normal physiological functioning including sleep. 14 However, we found significant differences in sleep duration in multivariate analysis after controlling for many of these variables (Tables 3 and 5), suggesting that the associations are robust.…”

Section: Strengths and Weaknesses Of The Current Studymentioning

confidence: 99%

<p>The Relationship of Sleep Duration with Ethnicity and Chronic Disease in a Canadian General Population Cohort</p>

Singh¹,

Hall²,

Reynolds³

et al. 2020

NSS

View full text Add to dashboard Cite

These authors contributed equally to this work Study Objectives: Sleep duration is an important marker of sleep quality and overall sleep health. Both too little and too much sleep are associated with poorer health outcomes. We hypothesized that ethnicity-specific differences in sleep duration exist. Methods: This cross-sectional study utilized questionnaire data from the Ontario Health Study (OHS), a multi-ethnic population-based cohort of Canadian adult residents aged 18 to 99 years, who provided medical, socio-demographic, and sleep information. Generalised linear models were used to investigate the association of sleep duration with ethnicity.Results: The study sample consisted of 143,307 adults (60.4% women). The sample was multi-ethnic, including self-identified Aboriginal, Arab, Black, Chinese, Filipino, Hispanic, Japanese, Korean, Mixed (>1 ethnicity), South Asian, South-East Asian, West Asian, and White ethnicities. Univariate analyses found that mean sleep duration compared to the White reference group (7.34 hours) was shorter in the Filipino (6.93 hours, 25 min less), Black (6.96 hours, 23 min less), Japanese (7.02 hours, 19 min less), Chinese (7.23 hours, 7 min less), and Mixed (7.27 hours, 4 min less) groups (all P<0.001). Mean sleep duration was shorter in men (7.25 hours) compared to women (7.37 hours) in the cohort as a whole (P<0.001), and in all ethnic groups (P<0.001). Multivariate analyses, adjusted for a wide range of potential risk factors, and analysis of sleep duration as a categorical variable ("short", "average", and "long" sleepers) confirmed these relationships. Both sleep duration and ethnicity were independent significant predictors of a range of physician-diagnosed morbidities including diabetes, stroke, and depression. Conclusion: Important differences exist in sleep duration between ethnic groups and may contribute to observed health disparities. Our results highlight the need for ethnicity-specific targeted education on the importance of prioritizing sleep for good health, and the need to account appropriately for ethnicity in future epidemiological, clinical, and translational research into sleep and related conditions.

show abstract

“…A high HOUSES score correlates with high SES. Previous research has been used to document the successful application of HOUSES as an effective SES measure in clinical research, [55][56][57][58][59][60][61][62] and HOUSES was recently found to be a useful surrogate for SES to assess risk stratification and hospitalization in adults with multiple chronic conditions. 63 Children with missing address information were excluded from the HOUSES analyses.…”

Section: Sociodemographic Variablesmentioning

confidence: 99%

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

Bjur

Ryu

et al. 2019

Hospital Pediatrics

Self Cite

View full text Add to dashboard Cite

A B S T R A C TOBJECTIVES: Children with multiple complex chronic conditions (MCCs) represent a small fraction of our communities but a disproportionate amount of health care cost and mortality. Because the temporal trends of children with MCCs within a geographically well-defined US pediatric population has not been previously assessed, health care planning and policy for this vulnerable population is limited. METHODS:In this population-based, repeated cross-sectional study, we identified and enrolled all eligible children residing in Olmsted County, Minnesota, through the Rochester Epidemiology Project, a medical record linkage system of Olmsted County residents. The pediatric complex chronic conditions classification system version 2 was used to identify children with MCCs. Five-year period prevalence and incidence rates were calculated during the study period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and characterized by age, sex, ethnicity, and socioeconomic status (SES) by using the housing-based index of socioeconomic status, a validated individual housing-based SES index. Age-, sex-, and ethnicity-adjusted prevalence and incidence rates were calculated, adjusting to the 2010 US total pediatric population. RESULTS:Five-year prevalence and incidence rates of children with MCCs in Olmsted County increased from 1200 to 1938 per 100 000 persons and from 256 to 335 per 100 000 person-years, respectively, during the study period. MCCs tend to be slightly more prevalent among children with a lower SES and with a racial minority background. CONCLUSIONS:Both 5-year prevalence and incidence rates of children with MCCs have significantly increased over time, and health disparities are present among these children. The clinical and financial outcomes of children with MCCs need to be assessed for formulating suitable health care planning given limited resources.

show abstract

Ethnicity, Socioeconomic Status, and Health Disparities in a Mixed Rural-Urban US Community—Olmsted County, Minnesota

Cited by 42 publications

References 27 publications

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

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

<p>The Relationship of Sleep Duration with Ethnicity and Chronic Disease in a Canadian General Population Cohort</p>

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

Contact Info

Product

Resources

About