Background This study examined racial/ethnic differences in health-related quality of life (HRQOL) among adults and identified variables associated with HRQOL by race/ethnicity. Methods This study was conducted under a cross-sectional design. We used the 2011–2016 Hawaii Behavioral Risk Factor Surveillance System data. HRQOL were assessed by four measures: self-rated general health, physically unhealthy days, mentally unhealthy days, and days with activity limitation. Distress was defined as fair/poor for general health and 14 days or more for each of the other three HRQOL measures. We conducted multivariable logistic regressions with variables guided by Anderson’s behavioral model on each distress measure by race/ethnicity. Results Among Hawaii adults, 30.4% were White, 20.9% Japanese, 16.8% Filipino, 14.6% Native Hawaiian and Pacific Islander (NHPI), 5.9% Chinese, 5.2% Hispanics, and 6.2% Other. We found significant racial/ethnic differences in the HRQOL measures. Compared to Whites, Filipinos, Japanese, NHPIs, and Hispanics showed higher distress rates in general health, while Filipinos and Japanese showed lower distress rates in the other HRQOL measures. Although no variables were consistently associated with all four HRQOL measures across all racial/ethnic groups, history of diabetes were significantly associated with general health across all racial/ethnic groups and history of depression was associated with at least three of the HRQOL measure across all racial/ethnic groups. Conclusions This study contributes to the literature on disparities in HRQOL and its association with other variables among diverse racial/ethnic subgroups. Knowing the common factors for HRQOL across different racial/ethnic groups and factors specific to different racial/ethnic groups will provide valuable information for identifying future public health priorities to improve quality of life and reduce health disparities.

As we anticipate a growing population of older adults, we will see an increase in chronic conditions such as dementia and falls. To meet these public health needs, we must systematically provide screening, education, preventive care, and supportive care for older patients and their caregivers in a primary care setting. This will require a workforce trained in providing for the complex medical and psychosocial needs of an older adult population in an interprofessional and collaborative fashion. By integrating geriatric screening tools into an interdisciplinary Annual Wellness Visit teaching clinic, we were able to successfully improve rates of geriatric screening for dementia, depression, falls, medication reconciliation and advance care planning. We also saw improvements in patient care and satisfaction and provided the opportunity for interprofessional collaboration and education for students in medicine, nursing, pharmacy and social work.

Introduction:To identify factors associated with readmission and development of an equation predicting unsuccessful discharge to enable more effective targeting of resources, decrease burden on hospitals and improve quality of care for the patients.Methods: Prospective Observational study conducted over 12 months between November 2013 and November 2014 on patients readmitted to MAPU in last 28 days from the first index admission in MAPU. Demographic data (age, gender, race, diagnosis on admission and re-admission, social circumstances) was collected along with data on patient's ADL, performance status, length of stay, number of co-morbidities and medications.Results: Total number of patients studied was 179 with male to female ratio of 1:1.2 with mean age of 70.12 years. Of interest, 40% of people were either divorced or widowed. Around 70% of people were pensioners. 60% were current/reformed smokers and approximately half had history of moderate to heavy drinking. Significant proportion suffered from mood/anxiety disorders (40%). Most of the people were independent with activities of daily living and communication. But around half of the patients (48.6%) complained that pain limits their activity and nearly one-fifth had h/o falls. Around threefourth of patients were discharged on six or more medication on index admission with one-fourth of patients being discharged on opioids. Nearly half (48%) had high/very high Charlson's co-morbidity index with nearly two-thirds having more than two variables on Elixhauser index suggesting increased disease burden in this cohort. More than one-third (42%) had high LACE 1 score predisposing them to readmission. Most of the patients readmitted with the same diagnosis were admitted due to disease progression/complication despite optimum care. And most of the patients readmitted with different diagnosis were admitted due to new condition unrelated to the index admission. The relationship between re-admission status and other factors was tested with Chi-square tests for all categorical variables after dividing the cohort into two groups: one with ≤3 admissions and other with >3 admissions (86 vs 93 patients respectively). Significant relationship was found between number of re-admissions and LACE score, employment status, education and number of medications on discharge and high risk medications on discharge. A stepwise logistic regression with backward elimination found employment status, education, high risk medications as significant and LACE score almost significant predictors in the model. Conclusions:Our high risk patient group included elderly frail pensioners with increased burden of chronic diseases, social isolation, mood disorders, poor education and polypharmacy with previous h/o readmissions. De-prescribing, better patient/carer education, proper assessment and management of functional needs including pain, disability and comorbidities with transitional care strategies integrating pre-discharge needs assessment with post-discharge care may reduce the readmission rat...

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