Screening for Elder Abuse in the Veterans Health Administration: Varied Approaches Across a National Health System.

BACKGROUND: Elder abuse (EA) is common and has devastating health consequences yet is rarely detected by healthcare professionals. While EA screening tools exist, little is known about if and how these tools are implemented in real-world clinical settings. The Veterans Health Administration (VHA) has experience screening for, and resources to respond to, other forms of interpersonal violence and may provide valuable insights into approaches for EA screening. OBJECTIVE: Describe EA screening practices across a national integrated healthcare system serving a large population of older adults at risk for EA. DESIGN: Survey of all 139 VHA medical centers from January to August 2021. PARTICIPANTS: Surveys were completed by the Social Work Chief, or delegate, at each site. MAIN MEASURES: The survey assessed the presence and characteristics of EA-specific screening practices as well as general abuse/neglect screening conducted with patients of all ages, including older adults. Follow-up emails were sent to sites that reported screening requesting additional details not included in the initial survey. KEY RESULTS: Overall, 130 sites (94%) responded. Among respondents, 5 (4%) reported screening older adults for EA using a previously published tool, while 6 (5%) reported screening for EA with an unstudied or locally developed tool. Forty-eight percent reported screening patients of all ages for general abuse/neglect using unstudied questions/tools, and 44% reported no EA screening at their site. Characteristics of screening programs (e.g., frequency, clinical setting, provider type) varied widely between sites, as did respondents' understanding of the definition of screening. CONCLUSIONS: High variability in screening practices for abuse/neglect and lack of EA-specific screening in a system that has successfully deployed other standardized screening approaches present an important opportunity to standardize and improve EA detection practices. Lessons learned in VHA could help advance the evidence base for EA screening more broadly to increase overall detection rates for EA nationally.

Impact of a Dyadic Intervention on Family Supporter Involvement in Helping Adults Manage Type 2 Diabetes.

BACKGROUND: Family support for adults' diabetes care is associated with improved self-management and outcomes, but healthcare providers lack structured ways to engage those supporters. OBJECTIVE: Assess the impact of a patient-supporter diabetes management intervention on supporters' engagement in patients' diabetes care, support techniques, and caregiving experience. DESIGN: Multivariate regression models examined between-group differences in support-related measures observed as part of a larger trial randomizing participants to a dyadic intervention versus usual care. PARTICIPANTS: A total of 239 adults with type 2 diabetes and either A1c >8% or systolic blood pressure >160mmHg enrolled with a family supporter. INTERVENTION: Health coaches provided training on positive support techniques and facilitated self-management information sharing and goal-setting. MAIN MEASURES: Patient and supporter reports at baseline and 12 months of supporter roles in diabetes care and caregiving experience. RESULTS: At 12 months, intervention-assigned patients had higher odds of reporting increased supporter involvement in remembering medical appointments (AOR 2.74, 95% CI 1.44, 5.21), performing home testing (AOR 2.40, 95% CI 1.29, 4.46), accessing online portals (AOR 2.34, 95% CI 1.29, 4.30), deciding when to contact healthcare providers (AOR 2.12, 95% CI 1.15, 3.91), and refilling medications (AOR 2.10, 95% CI 1.14, 3.89), but not with attending medical appointments or with healthy eating and exercise. Intervention-assigned patients reported increased supporter use of autonomy supportive communication (+0.27 points on a 7-point scale, p=0.02) and goal-setting techniques (+0.30 points on a 5-point scale, p=0.01). There were no differences at 12 months in change scores measuring supporter distress about patients' diabetes or caregiving burden. Intervention-assigned supporters had significantly larger increases in satisfaction with health system support for their role (+0.88 points on a 10-point scale, p=0.01). CONCLUSIONS: A dyadic patient-supporter intervention led to increased family supporter involvement in diabetes self-management and increased use of positive support techniques, without increasing caregiver stress.

My Goals Are Not Their Goals: Barriers and Facilitators to Delivery of Patient-Centered Care for Patients with Multimorbidity.

BACKGROUND: Patient-centered care reflecting patient preferences and needs is integral to high-quality care. Individualized care is important for psychosocially complex or high-risk patients with multiple chronic conditions (i.e., multimorbidity), given greater potential risks of interventions and reduced benefits. These patients are increasingly prevalent in primary care. Few studies have examined provision of patient-centered care from the clinician perspective, particularly from primary care physicians serving in integrated, patient-centered medical home settings within the US Veterans Health Administration. OBJECTIVE: We sought to clarify facilitators and barriers perceived by primary care physicians in the Veterans Health Administration to delivering patient-centered care for high-risk or complex patients with multimorbidity. DESIGN: We conducted semi-structured telephone interviews from April to July 2020 among physicians across 20 clinical sites. Findings were analyzed with deductive content analysis based on conceptual models of patient-centeredness and hierarchical factors affecting care delivery. PARTICIPANTS: Of 23 physicians interviewed, most were female (n = 14/23, 61%), serving in hospital-affiliated outpatient clinics (n = 14/23, 61%). Participants had a mean of 21 (SD = 11.3) years of experience. KEY RESULTS: Facilitators included the following: effective physician-patient communication to individualize care, prioritize among multiple needs, and elicit goals to improve patient engagement; access to care, enabled by interdisciplinary teams, and dictating personalized care planning; effortful but worthwhile care coordination and continuity; meeting complex needs through effective teamwork; and integrating medical and non-medical care aspects in recognition of patients' psychosocial contexts. Barriers included the following: intra- and interpersonal (e.g., perceived patient reluctance to engage in care); organizational (e.g., limited encounter time); and community or policy impediments (e.g., state decisional capacity laws) to patient-centered care. CONCLUSIONS: Physicians perceived individual physician-patient interactions were the greatest facilitators or barriers to patient-centered care. Efforts to increase primary care patient-centeredness for complex or high-risk patients with multimorbidity could focus on targeting physician-patient communication and reducing interpersonal conflict.

Association of Integrated Mental Health Services with Physical Health Quality Among VA Primary Care Patients.

BACKGROUND: Integrated care for comorbid depression and chronic medical disease improved physical and mental health outcomes in randomized controlled trials. The Veterans Health Administration (VA) implemented Primary Care-Mental Health Integration (PC-MHI) across all primary care clinics nationally to increase access to mental/behavioral health treatment, alongside physical health management. OBJECTIVE: To examine whether widespread, pragmatic PC-MHI implementation was associated with improved care quality for chronic medical diseases. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included 828,050 primary care patients with at least one quality metric among 396 VA clinics providing PC-MHI services between October 2013 and September 2016. MAIN MEASURE(S): For outcome measures, chart abstractors rated whether diabetes and cardiovascular quality metrics were met for patients at each clinic as part of VA's established quality reporting program. The explanatory variable was the proportion of primary care patients seen by integrated mental health specialists in each clinic annually. Multilevel logistic regression models examined associations between clinic PC-MHI proportion and patient-level quality metrics, adjusting for regional, patient, and time-level effects and clinic and patient characteristics. KEY RESULTS: Median proportion of patients seen in PC-MHI per clinic was 6.4% (IQR=4.7-8.7%). Nineteen percent of patients with diabetes had poor glycemic control (hemoglobin A1c >9%). Five percent had severely elevated blood pressure (>160/100 mmHg). Each two-fold increase in clinic PC-MHI proportion was associated with 2% lower adjusted odds of poor glycemic control (95% CI=0.96-0.99; p=0.046) in diabetes. While there was no association with quality for patients diagnosed with hypertension, patients without diagnosed hypertension had 5% (CI=0.92-0.99; p=0.046) lower adjusted odds of having elevated blood pressures. CONCLUSIONS AND RELEVANCE: Primary care clinics where integrated mental health care reached a greater proportion of patients achieved modest albeit statistically significant gains in key chronic care quality metrics, providing optimism about the expected effects of large-scale PC-MHI implementation on physical health.

Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention.

BACKGROUND: The ability of latent class models to identify clinically distinct groups among high-risk patients has been demonstrated, but it is unclear how healthcare data can inform group-specific intervention design. OBJECTIVE: Examine how utilization patterns across latent groups of high-risk patients provide actionable information to guide group-specific intervention design. DESIGN: Cohort study using data from 2012 to 2015. PATIENTS: Participants were 934,787 patients receiving primary care in the Veterans Health Administration, with predicted probability of 12-month hospitalization in the top 10th percentile during 2014. MAIN MEASURES: Patients were assigned to latent groups via mixture-item response theory models based on 28 chronic conditions. We modeled odds of all-cause mortality, hospitalizations, and 30-day re-hospitalizations by group membership. Detailed outpatient and inpatient utilization patterns were compared between groups. KEY RESULTS: A total of 764,257 (81.8%) of patients were matched with a comorbidity group. Groups were characterized by substance use disorders (14.0% of patients assigned), cardiometabolic conditions (25.7%), mental health conditions (17.6%), pain/arthritis (19.1%), cancer (15.3%), and liver disease (8.3%). One-year mortality ranged from 2.7% in the Mental Health group to 14.9% in the Cancer group, compared to 8.5% overall. In adjusted models, group assignment predicted significantly different odds of each outcome. Groups differed in their utilization of multiple types of care. For example, patients in the Pain group had the highest utilization of in-person primary care, with a mean (SD) of 5.3 (5.0) visits in the year of follow-up, while the Substance Use Disorder group had the lowest, with 3.9 (4.1) visits. The Substance Use Disorder group also had the highest rates of using services for housing instability (25.1%), followed by the Liver group (10.1%). CONCLUSIONS: Latent groups of high-risk patients had distinct hospitalization and utilization profiles, despite having comparable levels of predicted baseline risk. Utilization profiles pointed towards system-specific care needs that could inform tailored interventions.

Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report.

BACKGROUND: Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. OBJECTIVES: To describe population-level and individual change over time in latent comorbidity groups among Veterans at high-risk of hospitalization in the Veterans Health Administration (VA). RESEARCH DESIGN: Using a repeated cross-sectional design, we conducted a latent class analysis of chronic condition diagnoses. We compared latent class composition, patient high-risk status, and patient class assignment in 2018 to 2020. SUBJECTS: Two cohorts of eligible patients were selected: those active in VA primary care and in the top decile of predicted one-year hospitalization risk in 2018 (n = 951,771) or 2020 (n = 978,771). MEASURES: Medical record data were observed from January 2016-December 2020. Latent classes were modeled using indicators for 26 chronic health conditions measured with a 2-year lookback period from study entry. RESULTS: Five groups were identified in both years, labeled based on high prevalence conditions: Cardiometabolic (23% in 2018), Mental Health (18%), Substance Use Disorders (16%), Low Diagnosis (25%), and High Complexity (10%). The remaining 8% of 2018 patients were not assigned to a group due to low predicted probability. Condition prevalence overall and within groups was stable between years. However, among the 563,725 patients identified as high risk in both years, 40.8% (n = 230,185) had a different group assignment in 2018 versus 2020. CONCLUSIONS: In a repeated latent class analysis of nearly 1 million Veterans at high-risk for hospitalization, population-level groups were stable over two years, but individuals often moved between groups. Interventions tailored to latent groups need to account for change in patient status and group assignment over time.

Subgroups of High-Risk Veterans Affairs Patients Based on Social Determinants of Health Predict Risk of Future Hospitalization.

OBJECTIVE: Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. STUDY DESIGN AND SETTING: SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. RESULTS: Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). CONCLUSION: Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.

Diabetes Distress Among Dyads of Patients and Their Health Supporters: Links With Functional Support, Metabolic Outcomes, and Cardiac Risk.

BACKGROUND: Patients with diabetes (PWD) often experience diabetes distress which is associated with worse self-management and glycemic control. In contrast, PWD who receive support from family and friends (supporters) have better diabetes outcomes. PURPOSE: To examine the associations of PWD diabetes distress and supporters' distress about PWDs' diabetes with supporters' roles and PWD cardiometabolic outcomes. METHODS: We used baseline data from 239 adults with Type 2 diabetes and their supporters participating in a longitudinal trial. PWD and supporter diabetes distress (high vs. low) were determined using the Problem Areas in Diabetes Scale-5. Outcomes included PWD-reported help from supporters with self-care activities, supporter-reported strain, PWD metabolic outcomes (glycemic control [HbA1c], systolic blood pressure [SBP], and non-HDL cholesterol) and 5 and 10 year risk of cardiac event (calculated using the United Kingdom Prospective Diabetes Study algorithm). RESULTS: PWDs with high diabetes distress were more likely to report that their supporters helped with taking medications, coordinating medical care, and home glucose testing (p's < .05), but not more likely to report help with diet or exercise. High supporter distress was associated with greater supporter strain (p < .001). High supporter diabetes distress was associated with higher PWD HbA1c (p = .045), non-HDL cholesterol (p = .011), and 5 (p = .002) and 10 year (p = .001) cardiac risk. CONCLUSIONS: Adults with high diabetes distress report more supporter help with medically focused self-management but not with diet and exercise. Supporter distress about PWD diabetes was consistently associated with worse outcomes. PWD diabetes distress had mixed associations with their diabetes outcomes.

Autonomy support from informal health supporters: links with self-care activities, healthcare engagement, metabolic outcomes, and cardiac risk among Veterans with type 2 diabetes.

This study examined the role of autonomy support from adults' informal health supporters (family or friends) in diabetes-specific health behaviors and health outcomes. Using baseline data from 239 Veterans with type 2 diabetes at risk of complications enrolled in behavioral trial, we examined associations between autonomy support from a support person and that support person's co-residence with the participant's diabetes self-care activities, patient activation, cardiometabolic measures, and predicted risk of a cardiac event. Autonomy support from supporters was associated with significantly increased adherence to healthy lifestyle behaviors (diet, p < .001 and exercise, p = .003); higher patient activation (p < .001); greater patient efficacy in interacting with healthcare providers, and lower 5-year (p = .044) and 10-year (p = .027) predicted cardiac risk. Autonomy support was not significantly associated with diabetes-specific behaviors (checking blood glucose, foot care, or medication taking); or hemoglobin A1c, systolic blood pressure, or non-HDL cholesterol. There was a significant interaction of autonomy support and supporter residence in one model such that lack of autonomy support was associated with lower patient activation only among individuals with in-home supporters. No other interactions were significant. Findings suggest that autonomy support from family and friends may play a role in patient self-management, patient activation, and lower cardiac risk.

Association of the Implementation of the Patient-Centered Medical Home with Quality of Life in Patients with Multimorbidity.

BACKGROUND: The patient-centered medical home (PCMH) has clinical benefits for chronic disease care, but the association with patient-reported outcomes such as health-related quality of life (HRQoL) is unexplored in patients with multimorbidity (two or more chronic diseases). OBJECTIVE: To examine if greater clinic-level PCMH implementation was associated with higher HRQoL in multimorbid adults. DESIGN: A retrospective cohort study. PARTICIPANTS: Twenty-two thousand ninety-five multimorbid patients who received primary care at 944 Veterans Health Administration (VHA) clinics. MAIN MEASURES: Our exposure was the Patient Aligned Care Team Implementation Progress Index (PI2) for the clinic in 2012, a previously validated composite measure of PCMH implementation. Higher PI2 scores indicate better performance within eight PCMH domains. Outcomes were patient-reported HRQoL measured by the physical and mental component scores (PCS and MCS) from the Short Form-12 patient experiences survey in 2013-2014. Interaction of the outcomes with total hospitalizations and primary care visit count was also examined. Generalized estimating equations were used for main models after adjusting for patient and clinic characteristics. RESULTS: The cohort average age was 68 years, mostly male (96%), and had an average of 4.4 chronic diagnoses. Compared with patients seen at the lowest scoring clinics for PCMH implementation, care in the highest scoring clinics was associated with a higher adjusted marginal mean PCS (42.3 (95% CI 41.3-43.4) versus 40.3 (95% CI 39.1-41.5), P = 0.01), but a lower MCS (35.2 (95% CI 34.4-36.1) versus 36.0 (95% CI 35.3-36.8), P = 0.17). Patients with prior hospitalizations seen in clinics with higher compared with lower PI2 scores had a 2.7 point greater MCS (95% CI 0.6-4.8; P = 0.01). CONCLUSIONS: Multimorbid patients seen in clinics with greater PCMH implementation reported higher physical HRQoL, but lower mental HRQoL. The association between PCMH implementation and mental HRQoL may depend on complex interactions with disease severity and prior hospitalizations.