Understanding the groups of care transition strategies used by U.S. hospitals: An application of factor analytic and latent class methods

BACKGROUND: After activation of the Hospital Readmission Reduction Program (HRRP) in 2012, hospitals nationwide experimented broadly with the implementation of Transitional Care (TC) strategies to reduce hospital readmissions. Although numerous evidence-based TC models exist, they are often adapted to local contexts, rendering large-scale evaluation difficult. Little systematic evidence exists about prevailing implementation patterns of TC strategies among hospitals, nor which strategies in which combinations are most effective at improving patient outcomes. We aimed to identify and define combinations of TC strategies, or groups of transitional care activities, implemented among a large and diverse cohort of U.S. hospitals, with the ultimate goal of evaluating their comparative effectiveness. METHODS: We collected implementation data for 13 TC strategies through a nationwide, web-based survey of representatives from short-term acute-care and critical access hospitals (N = 370) and obtained Medicare claims data for patients discharged from participating hospitals. TC strategies were grouped separately through factor analysis and latent class analysis. RESULTS: We observed 348 variations in how hospitals implemented 13 TC strategies, highlighting the diversity of hospitals\u27 TC strategy implementation. Factor analysis resulted in five overlapping groups of TC strategies, including those characterized by 1) medication reconciliation, 2) shared decision making, 3) identifying high risk patients, 4) care plan, and 5) cross-setting information exchange. We determined that the groups suggested by factor analysis results provided a more logical grouping. Further, groups of TC strategies based on factor analysis performed better than the ones based on latent class analysis in detecting differences in 30-day readmission trends. CONCLUSIONS: U.S. hospitals uniquely combine TC strategies in ways that require further evaluation. Factor analysis provides a logical method for grouping such strategies for comparative effectiveness analysis when the groups are dependent. Our findings provide hospitals and health systems 1) information about what groups of TC strategies are commonly being implemented by hospitals, 2) strengths associated with the factor analysis approach for classifying these groups, and ultimately, 3) information upon which comparative effectiveness trials can be designed. Our results further reveal promising targets for comparative effectiveness analyses, including groups incorporating cross-setting information exchange

Improving Evidence-Based Grouping of Transitional Care Strategies in Hospital Implementation Using Statistical Tools and Expert Review

BACKGROUND: As health systems transition to value-based care, improving transitional care (TC) remains a priority. Hospitals implementing evidence-based TC models often adapt them to local contexts. However, limited research has evaluated which groups of TC strategies, or transitional care activities, commonly implemented by hospitals correspond with improved patient outcomes. In order to identify TC strategy groups for evaluation, we applied a data-driven approach informed by literature review and expert opinion. METHODS: Based on a review of evidence-based TC models and the literature, focus groups with patients and family caregivers identifying what matters most to them during care transitions, and expert review, the Project ACHIEVE team identified 22 TC strategies to evaluate. Patient exposure to TC strategies was measured through a hospital survey (N = 42) and prospective survey of patients discharged from those hospitals (N = 8080). To define groups of TC strategies for evaluation, we performed a multistep process including: using ACHIEVE\u27S prior retrospective analysis; performing exploratory factor analysis, latent class analysis, and finite mixture model analysis on hospital and patient survey data; and confirming results through expert review. Machine learning (e.g., random forest) was performed using patient claims data to explore the predictive influence of individual strategies, strategy groups, and key covariates on 30-day hospital readmissions. RESULTS: The methodological approach identified five groups of TC strategies that were commonly delivered as a bundle by hospitals: 1) Patient Communication and Care Management, 2) Hospital-Based Trust, Plain Language, and Coordination, 3) Home-Based Trust, Plain language, and Coordination, 4) Patient/Family Caregiver Assessment and Information Exchange Among Providers, and 5) Assessment and Teach Back. Each TC strategy group comprises three to six, non-mutually exclusive TC strategies (i.e., some strategies are in multiple TC strategy groups). Results from random forest analyses revealed that TC strategies patients reported receiving were more important in predicting readmissions than TC strategies that hospitals reported delivering, and that other key co-variates, such as patient comorbidities, were the most important variables. CONCLUSION: Sophisticated statistical tools can help identify underlying patterns of hospitals\u27 TC efforts. Using such tools, this study identified five groups of TC strategies that have potential to improve patient outcomes

Association Between Chronic Hepatitis C Virus Infection and Myocardial Infarction Among People Living With HIV in the United States.

Hepatitis C virus (HCV) infection is common among people living with human immunodeficiency virus (PLWH). Extrahepatic manifestations of HCV, including myocardial infarction (MI), are a topic of active research. MI is classified into types, predominantly atheroembolic type 1 MI (T1MI) and supply-demand mismatch type 2 MI (T2MI). We examined the association between HCV and MI among patients in the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems, a US multicenter clinical cohort of PLWH. MIs were centrally adjudicated and categorized by type using the Third Universal Definition of Myocardial Infarction. We estimated the association between chronic HCV (RNA+) and time to MI while adjusting for demographic characteristics, cardiovascular risk factors, clinical characteristics, and history of injecting drug use. Among 23,407 PLWH aged ≥18 years, there were 336 T1MIs and 330 T2MIs during a median of 4.7 years of follow-up between 1998 and 2016. HCV was associated with a 46% greater risk of T2MI (adjusted hazard ratio (aHR) = 1.46, 95% confidence interval (CI): 1.09, 1.97) but not T1MI (aHR = 0.87, 95% CI: 0.58, 1.29). In an exploratory cause-specific analysis of T2MI, HCV was associated with a 2-fold greater risk of T2MI attributed to sepsis (aHR = 2.01, 95% CI: 1.25, 3.24). Extrahepatic manifestations of HCV in this high-risk population are an important area for continued research

Development and Psychometric Properties of Surveys to Assess Provider Perspectives on the Barriers and Facilitators of Effective Care Transitions

Background The quality of the discharge process and effective care transitions between settings of care are critical to minimize gaps in patient care and reduce hospital readmissions. Few studies have explored which care transition components and strategies are most valuable to patients and providers. This study describes the development, pilot testing, and psychometric analysis of surveys designed to gain providers’ perspectives on current practices in delivering transitional care services. Methods We underwent a comprehensive process to develop items measuring unique aspects of care transitions from the perspectives of the three types of providers (downstream, ambulatory, and hospital providers). The process involved 1) an environmental scan, 2) provider interviews, 3) survey cognitive testing, 4) pilot testing, 5) a Stakeholder Advisory Group, 6) a Scientific Advisory Council, and 7) a collaborative Project ACHIEVE (Achieving Patient-Centered Care and Optimized Health in Care Transitions by Evaluating the Value of Evidence) research team. Three surveys were developed and fielded to providers affiliated with 43 hospitals participating in Project ACHIEVE. Web-based survey administration resulted in 948 provider respondents. We assessed response variability and response missingness. To evaluate the composites’ psychometric properties, we examined intercorrelations of survey items, item factor loadings, model fit indices, internal consistency reliability, and intercorrelations between the composite measures and overall rating items. Results Results from psychometric analyses of the three surveys provided support for five composite measures: 1) Effort in Coordinating Patient Care, 2) Quality of Patient Information Received, 3) Organizational Support for Transitional Care, 4) Access to Community Resources, and 5) Strength of Relationships Among Community Providers. All factor loadings and reliability estimates were acceptable (loadings ≥ 0.40, α ≥ 0.70), and the fit indices showed a good model fit. All composite measures positively and significantly correlated with the overall ratings (0.13 ≤ r ≤ 0.71). Conclusions We determined that the items and composite measures assessing the barriers and facilitators to care transitions within this survey are reliable and demonstrate satisfactory psychometric properties. The instruments may be useful to healthcare organizations and researchers to assess the quality of care transitions and target areas of improvement across different provider settings

Gut microbiome composition in the Hispanic Community Health Study/Study of Latinos is shaped by geographic relocation, environmental factors, and obesity.

Background: Hispanics living in the USA may have unrecognized potential birthplace and lifestyle influences on the gut microbiome. We report a cross-sectional analysis of 1674 participants from four centers of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), aged 18 to 74 years old at recruitment.Results: Amplicon sequencing of 16S rRNA gene V4 and fungal ITS1 fragments from self-collected stool samples indicate that the host microbiome is determined by sociodemographic and migration-related variables. Those who relocate from Latin America to the USA at an early age have reductions in Prevotella to Bacteroides ratios that persist across the life course. Shannon index of alpha diversity in fungi and bacteria is low in those who relocate to the USA in early life. In contrast, those who relocate to the USA during adulthood, over 45 years old, have high bacterial and fungal diversity and high Prevotella to Bacteroides ratios, compared to USA-born and childhood arrivals. Low bacterial diversity is associated in turn with obesity. Contrasting with prior studies, our study of the Latino population shows increasing Prevotella to Bacteroides ratio with greater obesity. Taxa within Acidaminococcus, Megasphaera, Ruminococcaceae, Coriobacteriaceae, Clostridiales, Christensenellaceae, YS2 (Cyanobacteria), and Victivallaceae are significantly associated with both obesity and earlier exposure to the USA, while Oscillospira and Anaerotruncus show paradoxical associations with both obesity and late-life introduction to the USA.Conclusions: Our analysis of the gut microbiome of Latinos demonstrates unique features that might be responsible for health disparities affecting Hispanics living in the USA

Development and Psychometric Properties of Surveys to Assess Patient and Family Caregiver Experience with Care Transitions

Background The purpose of this study was to develop and administer surveys that assess patient and family caregiver experiences with care transitions and examine the psychometric properties of the surveys. The surveys were designed to ask about 1) the transitional care services that matter most to patients and their caregivers and 2) care outcomes, including the overall quality of transitional care they received, patient self-reported health, and caregiver effort/stress. Methods Survey items were developed based on a review of the literature, existing surveys, focus groups, site visits, stakeholder and expert input, and patient and caregiver cognitive interviews. We administered mail surveys with telephone follow up to patients recently discharged from 43 U.S. hospitals. Patients identified the caregivers who helped them during their hospital stay (Time 1 caregiver) and when they were home (Time 2 caregiver). Time 1 and Time 2 caregivers were surveyed by telephone only. The psychometric properties of the survey items and outcome composite measures were examined for each of the three surveys. Items that performed poorly across multiple analyses, including those with low variability and/or a high missing data, were dropped except when they were conceptually important. Results The analysis datasets included responses from 9282 patients, 1245 Time 1 caregivers and 1749 Time 2 caregivers. The construct validity of the three proposed outcome composite measures—Overall Quality of Transitional Care (patient and caregiver surveys), Patient Overall Health (patient survey) and Caregiver Effort/Stress (caregiver surveys) —was supported by acceptable exploratory factor analysis results and acceptable internal consistency reliability. Site-level reliability was acceptable for the two patient outcome composite measures, but was low for Caregiver Effort/Stress (\u3c 0.70). In all surveys, the Overall Quality of Transitional Care outcome composite measure was significantly correlated with other outcome composite measures and most of the single-item measures. Conclusions Overall, the final patient and caregiver surveys are psychometrically sound and can be used by health systems, hospitals, and researchers to assess the quality of care transitions and related outcomes. Results from these surveys can be used to improve care transitions, focusing on what matters most to patients and their family caregivers

Care Transitions From Patient and Caregiver Perspectives

PURPOSE: Despite concerted actions to streamline care transitions, the journey from hospital to home remains hazardous for patients and caregivers. Remarkably little is known about the patient and caregiver experience during care transitions, the services they need, or the outcomes they value. The aims of this study were to (1) describe patient and caregiver experiences during care transitions and (2) characterize patient and caregiver desired outcomes of care transitions and the health services associated with them. METHODS: We interviewed 138 patients and 110 family caregivers recruited from 6 health networks across the United States. We conducted 34 homogenous focus groups (103 patients, 65 caregivers) and 80 key informant interviews (35 patients, 45 caregivers). Audio recordings were transcribed and analyzed using principles of grounded theory to identify themes and the relationship between them. RESULTS: Patients and caregivers identified 3 desired outcomes of care transition services: (1) to feel cared for and cared about by medical providers, (2) to have unambiguous accountability from the health care system, and (3) to feel prepared and capable of implementing care plans. Five care transition services or provider behaviors were linked to achieving these outcomes: (1) using empathic language and gestures, (2) anticipating the patient\u27s needs to support self-care at home, (3) collaborative discharge planning, (4) providing actionable information, and (5) providing uninterrupted care with minimal handoffs. CONCLUSIONS: Clear accountability, care continuity, and caring attitudes across the care continuum are important outcomes for patients and caregivers. When these outcomes are achieved, care is perceived as excellent and trustworthy. Otherwise, the care transition is experienced as transactional and unsafe, and leaves patients and caregivers feeling abandoned by the health care system

Transcriptional repressor ZEB2 promotes terminal differentiation of CD8⁺ effector and memory T cell populations during infection

ZEB2 is a multi-zinc-finger transcription factor known to play a significant role in early neurogenesis and in epithelial-mesenchymal transition-dependent tumor metastasis. Although the function of ZEB2 in T lymphocytes is unknown, activity of the closely related family member ZEB1 has been implicated in lymphocyte development. Here, we find that ZEB2 expression is up-regulated by activated T cells, specifically in the KLRG1(hi) effector CD8(+) T cell subset. Loss of ZEB2 expression results in a significant loss of antigen-specific CD8(+) T cells after primary and secondary infection with a severe impairment in the generation of the KLRG1(hi) effector memory cell population. We show that ZEB2, which can bind DNA at tandem, consensus E-box sites, regulates gene expression of several E-protein targets and may directly repress Il7r and Il2 in CD8(+) T cells responding to infection. Furthermore, we find that T-bet binds to highly conserved T-box sites in the Zeb2 gene and that T-bet and ZEB2 regulate similar gene expression programs in effector T cells, suggesting that T-bet acts upstream and through regulation of ZEB2. Collectively, we place ZEB2 in a larger transcriptional network that is responsible for the balance between terminal differentiation and formation of memory CD8(+) T cells

Antibiotics and antibiotic resistance in agroecosystems : State of the science

We propose a simple causal model depicting relationships involved in dissemination of antibiotics and antibiotic resistance in agroecosystems and potential effects on human health, functioning of natural ecosystems, and agricultural productivity. Available evidence for each causal link is briefly summarized, and key knowledge gaps are highlighted. A lack of quantitative estimates of human exposure to environmental bacteria, in general, and antibiotic-resistant bacteria, specifically, is a significant data gap hindering the assessment of effects on human health. The contribution of horizontal gene transfer to resistance in the environment and conditions that might foster the horizontal transfer of antibiotic resistance genes into human pathogens also need further research. Existing research has focused heavily on human health effects, with relatively little known about the effects of antibiotics and antibiotic resistance on natural and agricultural ecosystems. The proposed causal model is used to elucidate gaps in knowledge that must be addressed by the research community and may provide a useful starting point for the design and analysis of future research efforts

Author Correction: Gut microbiome composition in the Hispanic Community Health Study/Study of Latinos is shaped by geographic relocation, environmental factors, and obesity.

Following publication of the original paper [1], an error was reported in the third paragraph in the section "Analysis of GMB composition and its correlates" (page 3 of the PDF). The first sentence of the text should refer to Table 2, but mistakenly refers to Table 1