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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 3 of 3 Research Studies DisplayedAnderson MC, Evans E, Zonfrillo MR
Rural/urban differences in discharge from rehabilitation in older adults with traumatic brain injury.
This study compared differences in outcomes for older adults with traumatic brain injury (TBI) in rural and urban settings by 1) comparing the rates of successful community discharge; and 2) reasons for not achieving successful discharge. This retrospective national cohort study looked at skilled nursing facility (SNF) patients aged 66 and older using Medicare inpatient claims with Minimum Data Set assessments. A total of 11,771 SNFs were identified with a total population of 61,021 Medicare beneficiaries discharged to a SNF following hospitalization for TBI between 2011 and 2015. Patients in rural settings had lower rates of successful discharge compared with patients in urban settings (52.1% vs 58.5%). Reasons for unsuccessful discharge differed between rural and urban settings with rural patients less likely to discharged from SNF within 100 days although they were less likely to be rehospitalized within 30 days of SNF discharge.
AHRQ-funded; HS000011.
Citation: Anderson MC, Evans E, Zonfrillo MR .
Rural/urban differences in discharge from rehabilitation in older adults with traumatic brain injury.
J Am Geriatr Soc 2021 Jun;69(6):1601-08. doi: 10.1111/jgs.17065..
Keywords: Elderly, Brain Injury, Trauma, Rural Health, Urban Health, Rehabilitation, Nursing Homes
Yoon S, Odlum M, Lee Y
Applying deep learning to understand predictors of tooth mobility among urban Latinos.
In this study, the investigators applied deep learning algorithms to build correlate models that predicted tooth mobility in a convenience sample of urban Latinos. The authors suggest that their application was useful for gaining insights into the most important modifiable and non-modifiable factors predicting tooth mobility, and maybe useful for guiding targeted interventions in urban Latinos.
AHRQ-funded; HS019853.
Citation: Yoon S, Odlum M, Lee Y .
Applying deep learning to understand predictors of tooth mobility among urban Latinos.
Stud Health Technol Inform 2018;251:241-44..
Keywords: Dental and Oral Health, Elderly, Racial and Ethnic Minorities, Urban Health
Yoon S, Choi T, Odlum M
Machine learning to identify behavioral determinants of oral health in inner city older Hispanic adults.
In this study, the investigators applied machine learning techniques to a community-based behavioral dataset to build prediction models to gain insights about minority dental health and population aging as the foundation for future interventions for urban Hispanics. Their application of machine learning techniques identified emotional and systemic factors such as chronic stress and health literacy as the strongest predictors of self-reported dental health among hundreds of possible variables.
AHRQ-funded; HS019853.
Citation: Yoon S, Choi T, Odlum M .
Machine learning to identify behavioral determinants of oral health in inner city older Hispanic adults.
Stud Health Technol Inform 2018;251:253-56..
Keywords: Dental and Oral Health, Elderly, Racial and Ethnic Minorities, Urban Health