Predictors of Patient Activation at ACS Hospital Discharge and Health Care Utilization in the Subsequent Year

Background. AHA guidelines have been established to reduce Acute Coronary Syndrome (ACS)-related morbidity, mortality and recurrent events post-discharge. These recommendations emphasize the patient as an engaged member of the health care team in secondary prevention efforts. Patients with high levels of activation are more likely to perform activities that will promote their own health and are more likely to have their health care needs met. Despite evidence and strong expert consensus supporting patients as active collaborators in their own ACS care, the complexity and unexpected realities of self-managing one’s care at home are often underestimated. This study seeks to examine the correlates of patient activation at hospital discharge and then identifies activation trajectories in this same cohort in subsequent months. Lastly, this study examines the association between patient activation and health care utilization in the year subsequent to an ACS event. Methods. This study incorporates three aims: Aim 1, identification of the correlates of low patient activation post-discharge; Aim 2, identification of patient activation trajectories among this same cohort in the months following hospitalization; and Aim 3, examination of the association between patient activation and health utilization, post-discharge. Results. Fifty-nine percent of ACS patients identified as being at the lowest two activation stages at the time of hospital discharge. Perceived stress (pidentified post-discharge: low, stable (T1), high, sharp decline (T2), and sharp improvement (T3). The majority of patients (67%) identified as being in T1. Those patients of older age (OR: 2.22; CI 1.4- 3.5), identifying as Black in race (OR: 2.14: CI 1.1- 4.3), and reporting moderate/high perceived stress (OR: 2.54: CI 1.4- 4.5) had increased odds of being in the low, stable trajectory. The bivariate analysis indicated a significant association (P=0.008) between low patient activation and self-reported hospital readmissions in the months following discharge. In the final model, moderate to severe depression (OR: 1.60; CI 1.1- 2.3) was the strongest predictor of readmissions in the 12 months subsequent to discharge. Conclusions: Patients reported low activation at hospital discharge after an ACS event indicated that these patients were not prepared to take an active role in their own care. Correlates of low activation at discharge include moderate to high perceived stress, depression, and low social support. Furthermore, in the months following hospital discharge, the majority of these patients followed either a low/stable or a sharp decline activation trajectory. Hence, these results suggest that over time patients feel less and less confident to take an active role in self-management. Lastly, we found that patient activation may impact healthcare utilization in the year subsequent to hospital discharge, although patient self-reported depression appears to be the strongest predictor of utilization in the subsequent year. Future research is needed to better understand the relationship(s) among patient activation, depression, and health care utilization

Understanding Maternity Care Coordination for Women Veterans Using an Integrated Care Model Approach

BACKGROUND: An increasing number of women veterans are using VA maternity benefits for their pregnancies. However, because the VA does not offer obstetrical care, women must seek maternity care from non-VA providers. The growing number of women using non-VA care has increased the importance of understanding how this care is integrated with ongoing VA medical and mental health services and how perceptions of care integration impact healthcare utilization. Therefore, we sought to understand these relationships among a sample of postpartum veterans utilizing VA maternity benefits. METHODS: We fielded a modified version of the Patient Perceptions of Integrated Care survey among a sample of postpartum veterans who had utilized VA maternity benefits for their pregnancies (n = 276). We assessed relationships between perceptions of six domains of patient-reported integrated care, indicating how well-integrated patients perceived the care received from VA and non-VA clinicians, and utilization of mental healthcare following pregnancy. RESULTS: Domain scores were highest for items focused on VA care, including test result communication and VA provider\u27s knowledge of patient\u27s medical conditions. Scores were lower for obstetrician\u27s knowledge of patient\u27s medical history. Women with depressive symptom scores indicative of depression rated test result communication as highly integrated, while women who received mental healthcare following pregnancy had low integrated care ratings for the Support for Medication and Home Health Management domain, indicating a lack of support for mental health conditions following pregnancy. DISCUSSION: Among a group of postpartum veterans, poor ratings of integrated care across some domains were associated with higher rates of mental healthcare use following pregnancy. Further assessment of integrated care by patients may assist VA providers and policymakers in developing systems to ensure integrated care for veterans who receive care outside the VA

Psychometric Evaluation of the Care Transition Measures in a Sample of ACS Patients: Results from Transitions, Risks, and Actions in Coronary Events – Center for Outcomes Research and Education (TRACE-CORE)

Background: Quality of transitional care is associated with important health outcomes such as rehospitalization and costs. A widely used measure of the construct, the Care Transitions Measure (CTM-15), was developed with classical test theory approach. Its short version (CTM-3) was included in the CAHPS® Hospital Survey. Methods: As part of TRACE-CORE 1545 participants were interviewed during hospitalization for ACS providing information on general health status (SF-36). At 1 month post-discharge, patients completed CTM-15, health utilization and care process questions. We evaluated the psychometric properties of the CTM using classical and item response theory analyses. We compared the measurement precision of CTM-15, CTM-3, and a CTM-IRT based score using relative validity (RV). Results: Participants were 79% non-Hispanic white, 67% male, 27% with a college education or higher (27%) and average age of 62 years. The CTM scale had good internal consistency (Cronbach’s alpha=0.95), but demonstrated strong acquiescence bias (8.7% participants responded “Strongly agree”, 19% “Agree” to all 15 items) and limited score variability. IRT based item parameters were estimated for all items. The CTM-15 differentiated between groups of patients defined by self-reported health status, health care utilization, and care transition process indicators. Differences between groups were small (2-3 points). There was no gain in measurement precision for the scale from IRT scoring. The CTM-3 was not significantly lower for patients reporting rehospitalization or emergency department visits. Conclusion: We identified psychometric challenges of the CTM, which may limit its value in research and practice. The strong acquiescence bias in the measure leads to highly skewed, clustered scores with restricted score variance. In the absence of guidelines on meaningfully important differences, it is hard to determine whether detected statistically significant differences in CTM are important. These results are in line with emerging evidence of gaps in the validity of the measure

Share2Quit: Web-Based Peer-Driven Referrals for Smoking Cessation

BACKGROUND: Smoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions. OBJECTIVE: The objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system. METHODS: We will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population. RESULTS: This protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation. CONCLUSIONS: Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions

Newborn Outcomes Among Veterans Utilizing VHA Maternity Benefits, 2016-2020

INTRODUCTION: Public Law 111-163 Section 206 of the Caregivers and Veteran Omnibus Health Services Act amended the Veterans Health Administration\u27s (VHA) medical benefits package to include 7 days of medical care for newborns delivered by Veterans. We examined the newborn outcomes among a cohort of women Veterans receiving VHA maternity benefits and care coordination. MATERIALS AND METHODS: We conducted a secondary analysis of phone interview data from Veterans enrolled in the COMFORT (Center for Maternal and Infant Outcomes Research in Translation) study 2016-2020. Multivariable regression estimated associations with newborn outcomes (preterm birth; low birthweight). RESULTS: During the study period, 829 infants were born to 811 Veterans. Mothers reported excellent health for 94% of infants. The prevalence of preterm birth was slightly higher in our cohort (11% vs. 10%), as were low birthweight (9%) deliveries, compared to the general population (8.28%). Additionally, 42% of infants in our cohort required follow-up care for non-routine health conditions; 11% were uninsured at 2 months of age. Adverse newborn outcomes were more common for mothers who were older in age, self-identified as non-white in race and/or of Hispanic ethnicity, had a diagnosis of posttraumatic stress disorder, or had gestational comorbidities. CONCLUSIONS: The current VHA maternity coverage appears to be an effective policy for ensuring the well-being and health care coverage for the majority of Veterans and their newborns in the first days of life, thereby reducing the risk of inadequate prenatal and neonatal care. Future research should examine costs associated with extending coverage to 14 days or longer, comparing those to the projected excess costs of neonatal health problems. VHA policy should continue to support expanding care and resources through the Maternity Care Coordinator model

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

BACKGROUND: What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. OBJECTIVE: The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. METHODS: We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. RESULTS: We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. CONCLUSIONS: We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems

Identification of Relationships Between Patients Through Elements in a Data Warehouse Using the Familial, Associational, and Incidental Relationship (FAIR) Initiative: A Pilot Study

BACKGROUND: Over the last several years there has been widespread development of medical data warehouses. Current data warehouses focus on individual cases, but lack the ability to identify family members that could be used for dyadic or familial research. Currently, the patient\u27s family history in the medical record is the only documentation we have to understand the health status and social habits of their family members. Identifying familial linkages in a phenotypic data warehouse can be valuable in cohort identification and in beginning to understand the interactions of diseases among families. OBJECTIVE: The goal of the Familial, Associational, and Incidental Relationships (FAIR) initiative is to identify an index set of patients\u27 relationships through elements in a data warehouse. METHODS: Using a test set of 500 children, we measured the sensitivity and specificity of available linkage algorithm identifiers (eg, insurance identification numbers and phone numbers) and validated this tool/algorithm through a manual chart audit. RESULTS: Of all the children, 52.4% (262/500) were male, and the mean age of the cohort was 8 years old (SD 5). Of the children, 51.6% (258/500) were identified as white in race. The identifiers used for FAIR were available for the majority of patients: insurance number (483/500, 96.6%), phone number (500/500, 100%), and address (497/500, 99.4%). When utilizing the FAIR tool and various combinations of identifiers, sensitivity ranged from 15.5% (62/401) to 83.8% (336/401), and specificity from 72% (71/99) to 100% (99/99). The preferred method was matching patients using insurance or phone number, which had a sensitivity of 72.1% (289/401) and a specificity of 94% (93/99). Using the Informatics for Integrating Biology and the Bedside (i2b2) warehouse infrastructure, we have now developed a Web app that facilitates FAIR for any index population. CONCLUSIONS: FAIR is a valuable research and clinical resource that extends the capabilities of existing data warehouses and lays the groundwork for family-based research. FAIR will expedite studies that would otherwise require registry or manual chart abstraction data sources

Familial, Associational, & Incidental Relationships (FAIR)

Identifying familial linkages in a phenotypic data warehouse can be valuable in cohort identification, and beginning to understand interactions of diseases among families. The goal of the Familial, Associational, & Incidental Relationships (FAIR) system is to identify an index set patients’ relationships through elements in a data warehouse. Using a test set of 500 children, we measured the sensitivity and specificity of available linkage algorithm (e.g.: insurance id and phone numbers) and validated this tool/algorithm through a manual chart audit. Sensitivity varied from 16% to 87%, and specificity from 70% to 100% using various combinations of identifiers. Using the “i2b2” warehouse infrastructure, we have now developed a web app that facilitates FAIR for any index population

Psychometric Evaluation of the Care Transition Measure in TRACE‐CORE: Do We Need a Better Measure?

Background: The quality of transitional care is associated with important health outcomes such as rehospitalization and costs. The widely used Care Transitions Measure (CTM‐15) was developed with a classic test theory approach; its short version (CTM‐3) was included in the CAHPS Hospital Survey. We conducted a psychometric evaluation of both measures and explored whether item response theory (IRT) could produce a more precise measure. Methods and Results: As part of the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education, 1545 participants were interviewed during an acute coronary syndrome hospitalization, providing information on general health status (Short Form‐36), CTM‐15, health utilization, and care process questions at 1 month postdischarge. We used classic and IRT analyses and compared the measurement precision of CTM‐15–, CTM‐3–, and CTM‐IRT–based score using relative validity. Participants were 79% non‐Hispanic white and 67% male, with an average age of 62 years. The CTM‐15 had good internal consistency (Cronbach's α=0.95) but demonstrated acquiescence bias (8.7% participants responded “Strongly agree” and 19% responded “Agree” to all items) and limited score variability. These problems were more pronounced for the CTM‐3. The CTM‐15 differentiated between patient groups defined by self‐reported health status, health care utilization, and care transition process indicators. Differences between groups were small (2 to 3 points). There was no gain in measurement precision from IRT scoring. The CTM‐3 was not significantly lower for patients reporting rehospitalization or emergency department visits. Conclusion: We identified psychometric challenges of the CTM, which may limit its value in research and practice. These results are in line with emerging evidence of gaps in the validity of the measure

Addressing Systemic Factors Related to Racial and Ethnic Disparities among Older Adults in Long-Term Care Facilities

Disparities in older adults’ care and experiences in long-term care facilities (LTCFs) such as nursing homes and assisted living/residential care communities reflect disparities in the broader society. Various policies and institutional practices related to economic opportunity, education, housing, health care, and retirement financing have created and maintain inequitable social structures in the United States. This chapter describes racial and ethnic disparities among older adults in LTCFs in the United States and the systemic factors associated with those disparities. It presents a conceptual framework for understanding the role of structural racism in the racial and ethnic inequities experienced by LTCF residents. In the framework, structural racism directly contributes to racial and ethnic inequities among LTCF residents through LTCF-related policies and practices. Structural racism also indirectly causes disparities among LTCF residents through health and economic disparities. The chapter describes current efforts that address the effects of structural racism within LTCFs and concludes with practice and policy recommendations to redress racial and ethnic disparities among LTCF residents