Objective: Despite their vastly different historical backgrounds, unique languages and variable pre- and post-immigration experiences, Asian-Americans are considered to share stressors surrounding immigration, but there is a gap in describing manifestations of possible mental distress. Thus, the purpose of this study was to explore and compare differences in factors associated with psychological distress among Asian subgroups including Chinese, Filipino, Vietnamese, Korean, Japanese, and non-Hispanic Whites. Design: Using a cross-sectional study design, California Health Interview Survey (CHIS) 2011/2012 data were analyzed. The sample consisted of 29,142 participants: 25,645 non-Hispanic Whites, 3497 non-Hispanic Asian-Americans, 1156 Chinese, 471 Filipinos, 864 Vietnamese, 704 Koreans, and 302 Japanese. Sociodemographic characteristics included gender, age group, marital status, education, poverty level, working status, health insurance, level of acculturation, social cohesion, neighborhood safety, and civic engagement. Physical health status included disability and chronic illness. Psychological distress was evaluated using the Kessler 6 (K6) scale. Results: Results showed that psychological distress levels ranged between 1.96 and 4.52 (p < .05) out of 24 and associated factors were significantly different among the five Asian subgroups and non-Hispanic Whites. Conclusions: The current study highlights the differences in characteristics of psychological distress among Asian subgroups. It underscores the significance of understanding individualized cultural and historical background in each Asian subgroup and subsequently developing and applying appropriate interventions for those groups. In addition, different influencing factors should be applied to assess and prioritize the needs of Asian subgroups to improve psychological distress. The study also warrants further investigation and careful description of each Asian subgroups.

Park H, et al. J Korean Acad Community Health Nurs. 2017 Sep;28(3):280-290. https://doi.org/10.12799/jkachn.2017.28.3.280

Background: Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with acute stroke and compared its accuracy with that of the existing fall risk prediction tool, the Morse Fall Scale (MFS). Methods: This is a retrospective nested case-control study. The initial sample size was 8462 admitted to a single cerebrovascular specialty hospital with acute stroke. A total of 156 fall events occurred, and each fall case was randomly matched with six control cases. Six ML algorithms were used, namely, regularized logistic regression, support vector machine, naïve Bayes (NB), k-nearest neighbors, random forest, and extreme-gradient boosting (XGB). Results: We included 156 in the fall group and 934 in the non-fall group. The mean ages of the fall and non-fall groups were 68.3 (± 12.2) and 65.3 (± 12.9) years old, respectively. The MFS total score was significantly higher in the fall group (54.3 ± 18.3) than in the non-fall group (37.7 ± 14.7). The area under the receiver operating curve (AUROC) of the MFS in predicting falls was 0.76 (0.73–0.79). XGB had the highest AUROC of 0.85 (0.78–0.92), and XGB and NB had the highest F1 score of 0.44. Conclusions: The AUROC values of all of ML algorithms were similar to those of the MFS in predicting fall risk in patients with acute stroke, allowing for accurate and efficient fall screening.

Social status might be a determinant of occupational health inequalities. This study analyzed the effects of social status on both work environments and health outcomes. The study sample consisted of 27,598 wage employees aged 15 years and older from among the Korean Working Condition Survey participants in 2011. Work environments included atypical work, physical risks, ergonomic risks, work demands, work autonomy, social supports, and job rewards. Health outcomes comprised general health, health and safety at risk because of work, the World Health Organization-5 Well-being Index, work-related musculoskeletal disease, and work-related injury. Multivariable logistic-regression models were used to identify the associations between social status and work environments and health outcomes. Employees in the demographically vulnerable group had lower occupational status compared with their counterparts. Low social status was largely related to adverse work environments. Especially, precarious employment and manual labor occupation were associated with both adverse work environments and poor health outcomes. Precarious and manual workers should take precedence in occupational health equity policies and interventions. Their cumulative vulnerability, which is connected to demographics, occupational status, adverse work environments, or poor health outcomes, can be improved through a multilevel approach such as labor market, organizations, and individual goals.

While Asians are becoming the largest ethnic group in the United States, studies have focused on Asians as a single population. The purpose of this study was to explore the racial and ethnic mental health differences between non-Hispanic Whites and Asians, with an emphasis on understudied subgroups, from the California Health Interview Survey 2011/2012. In this dataset Asians had significantly lower adjusted odds ratios for both mental distress and serious mental illness. However, when Asians were divided into subgroups and compared to Whites, Vietnamese and Japanese subgroups were significantly lower than Whites for mental distress while Koreans were significantly higher. Vietnamese and Chinese were found to have significantly less serious mental illness than Whites in the subgroup analyses. Our results underscore the importance of recognizing that Asian subgroups should not be overlooked, and all Asians should not automatically be treated as a homogenous group.