Chenjuan Ma

Abstract

While home health agencies (HHAs) can seek accreditation to recognize their quality of service, it is unknown whether agencies with accreditation perform better in providing care than those without accreditation. Using 5-year data from national data sources, the aims of this study were: 1) to depict characteristics of HHAs with and without accreditation; and 2) to examine the relationship between accreditation status and HHA performance on quality-of-care metrics. This study analyzed 7,697 agencies in the US and found that 1) agencies that were for-profit, urban, not-hospital-affiliated, single-branch, Medicare enrolled only, and without hospice program were more likely to have accreditation; and 2) overall, accredited agencies performed better on the three commonly used quality indicators, timely initiation of care, hospitalization, and emergency department visit, though not all the observed differences were substantial in absolute value. Our results provide unique empirical information to agencies considering seeking accreditation.

Abstract

Background: Perinatal depression is a global mental health problem. Studies have suggested that perinatal depression is related to poor sleep quality during pregnancy. However, evidence on the influence and mechanism of sleep quality on the risk of developing perinatal depression remains limited and inconclusive. Methods: A systematic review was conducted in PubMed, Web of Science, Embase, CINAHI and Cochrane Library for relevant original quantitative studies published in English. A hand search of the reference list of relevant studies was also performed. Meta-analysis was performed using RevMan software and a random-effects model. Potential heterogeneity source was explored by subgroup and sensitivity analyses, and potential publication bias was tested using funnel plots and Begg's test. Results: A total of ten studies involving 39,574 participants were included in our meta-analysis. Overall, women who experienced poor sleep quality during pregnancy were at a significantly higher risk of developing depression, with antenatal depression 3.72 times higher, postpartum depression 2.71 times higher, and perinatal depression 3.46 times higher, compared to those did not experience poor sleep quality. Limitations: Different measuring tools and unobserved confounding factors may make some bias in our result. What's more, not all included studies were initially designed to assess the association between antenatal sleep quality and the risk of developing perinatal depression. Conclusion: Our meta-analysis found that antenatal sleep quality was negatively associated with the risk for perinatal depression. Our findings highlight the importance of improving sleep quality during pregnancy for mental health among perinatal women.

Abstract

Background: In home health care, language barriers are understudied. Language barriers between patients and providers are known to affect a variety of patient outcomes. How a patient's language preference influences hospital readmission risk from home health care has yet to be determined. Objective: To determine if home care patients’ language preference is associated with their risk for hospital readmission from home health care within 30 days of hospital discharge. Design: Retrospective cross-sectional study of hospital readmissions from an urban home health care agency's administrative records and the national electronic home health care record for the United States, captured between 2010 and 2015. Setting: New York City, New York, USA. Participants: The dataset comprised 90,221 post-hospitalization patients and 6.5 million home health care visits. Methods: First, a Chi-square test was used to determine if there were significant differences in crude readmission rates based on language group. Inverse probability of treatment weighting was used to adjust for significant differences in known hospital readmission risk factors between to examine all-cause hospital readmission during a home health care stay. The final matched sample included 87,561 patients with a language preference of English, Spanish, Russian, Chinese, or Korean. English-speaking patients were considered the comparison group to the non-English speaking patients. A Marginal Structural Model was applied to estimate the impact of non-English language preference against English language preference on rehospitalization. The results of the marginal structural model were expressed as an odds ratio of likelihood of readmission to the hospital from home health care. Results: Home health patients with a non-English language preference had a higher hospital readmission risk than English-speaking patients. Crude readmission rate for the limited English proficiency patients was 20.4% (95% CI, 19.9–21.0%) overall compared to 18.5% (95% CI, 18.7–19.2%) for English speakers (p < 0.001). Being a non-English-speaking patient was associated with an odds ratio of 1.011 (95% CI, 1.004–1.018) in increased hospital readmission rates from home health care (p = 0.001). There were also statistically significant differences in readmission rate by language group (p < 0.001), with Korean speakers having the lowest rate and Spanish speakers having the highest, when compared to English speakers. Conclusions: People with a non-English language preference have a higher readmission rate from home health care. Hospital and home healthcare agencies may need specialized care coordination services to reduce readmission risk for these patients. Tweetable abstract: A new US-based study finds that home care patients with language barriers are at higher risk for hospital readmission.

Abstract

EHRs provide an opportunity to conduct research on underrepresented oncology populations with mental health and substance use disorders. However, a lack of data quality may introduce unintended bias into EHR data. The objective of this article is describe our analysis of data quality within automated comorbidity lists commonly found in EHRs. Investigators conducted a retrospective chart review of 395 oncology patients from a safety-net integrated healthcare system. Statistical analysis included κ coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak κ coefficients (κ = 0.2-0.39, P <.01) were noted for drug and alcohol use disorders indicating deficiencies in comorbidity documentation within the automated comorbidity list. Further, conditional logistic regression analyses revealed deficiencies in comorbidity documentation in patients with drug use disorders (odds ratio, 11.03; 95% confidence interval, 2.71-44.9; P =.01) and psychoses (odds ratio, 0.04; confidence interval, 0.02-0.10; P <.01). Findings suggest deficiencies in automatic comorbidity lists as compared with a review of provider narrative notes when identifying comorbidities. As healthcare systems increasingly use EHR data in clinical studies and decision making, the quality of healthcare delivery and clinical research may be affected by discrepancies in the documentation of comorbidities.

Abstract

The effect of the COVID-19 related stressors on the mental health of both infected patients and the general public has been well established. However, knowledge is limited on how psychosocial support buffers the association. This study aimed to examine this buffering role in the context of China. We utilized cross-sectional data collected online in mid-March 2020, involving 585 respondents. Mental health status was determined through depression symptoms and loneliness. COVID-19 related stressors include three aspects: perceived severity, perceived threat to life and health, and perceived risk of COVID-19 infection. Psychosocial support included family and social supports. Analyses include ordinary linear regression. The finding showed that psychosocial support buffered the negative effect of loneliness in the context of perceived severity of COVID-19, but appeared to intensify the negative effect of depression symptoms in the context of perceived threat to life and health. However, there was no significant buffering effect on depression or loneliness in the context of the perceived risk of infection. The buffering effect of psychosocial support on COVID-19 related stressors is of mixed patterns. This study contributes to the emerging body of literature trying to understand how the COVID-19 impacts the mental health of individuals.

Abstract

BACKGROUND: New graduate nurses experience difficulty in adapting to a new environment, which affects intent to leave. However, data on the factors contributing to difficulty in adapting and intent to leave among new graduate nurses are insufficient.PURPOSE: The aim of the study was to explore and compare factors associated with difficulty in adapting and the intent to leave among new graduate nurses in South Korea.METHODOLOGY: This cross-sectional study used secondary data analysis. Primary data were obtained from the 2015-2016 Korean National Graduates Occupational Mobility Survey. Descriptive statistics, independent t tests, and chi-square statistics with weighted samples besides multivariate logistic regression analyses were conducted (N = 467).RESULTS: Roughly 46% of nurses reported difficulty adapting, and 16% expressed their intent to leave. The factors linked to difficulty in adapting were working at large hospitals with rotating shifts, low person-job fit, and low satisfaction with personal competency; intent to leave was associated with high monthly salary and low satisfaction with the workplace (all ps < .05). Difficulty in adapting did not equate to their intent to leave.CONCLUSIONS: There were high rates of difficulty in adapting and intent to leave among new graduate nurses. Although different factors were associated with difficulty adapting and intent to leave, workplace condition is a common factor.PRACTICE IMPLICATIONS: Different strategies are needed to improve adaptation and intention of leaving among new graduate nurses. For better adaptation, developing training programs enhancing professional competency with a sufficient training period is required. In addition, providing staff and resources to reduce the intent to leave is crucial.

Abstract

During the coronavirus disease 2019 (COVID-19) pandemic, home health aides (HHAs) provided daily medical and personal care to community-dwelling older adults and those with chronic conditions. Prior qualitative studies have found that providing care during COVID-19 left HHAs susceptible to physical, financial, and emotional risks. However, limited quantitative data exist The objective of this study was to assess the impact of COVID-19 on HHAs and to understand the challenges and opportunities for current and future pandemic planning. A cross-sectional survey of 256 HHAs in the downstate New York region was conducted by telephone in English, French Creole, Chine, Spanish, and Russian between August and November 2020. The survey found that HHAs experienced a variety of physical, financial, and emotional challenges during COVID-19. To better support this work force, action by public health officials and policymakers is warranted, particularly with respect to workplace protections and safety, mental health, compensation, and access to basic resources.

Abstract

Purpose: Home health care is one of the fastest growing health care sectors in the United States. However, little is known of differences in trends in quality performance between rural and urban home health agencies over time. This study aimed to examine disparities in quality performance between rural and urban home health agencies between 2014 and 2018. Methods: This is a cohort study using 2014-2018 national Home Health Compare data and Providers of Service Profile data, including 7,908 home health agencies, of which 1,537 were rural agencies. Quality performance measures included timely initiation of care, hospitalization, and emergency department (ED) visits. Two-level hierarchical regression models were used to identify rural-urban differences in these quality indicators over time when controlling organizational characteristics. Findings: Rural agencies were less likely to be for-profit and accredited, and more likely to be hospital-based, serve both Medicare and Medicaid beneficiaries, and have hospice programs. Rural agencies consistently outperformed on timely initiation of care over time, and urban agencies consistently outperformed on hospitalization and ED visits over time. These gaps between rural and urban agencies were steady over time except the gap in hospitalization, which slightly narrowed over time (Coef. = 0.11, P =.001 for urban and year interaction term). Conclusions: Significant differences exist in quality of care between rural and urban home health agencies and such differences have not been significantly narrowed over time. To reduce rural-urban disparities, policy makers should take into account unique challenges faced by urban and rural agencies when making policy decisions.