Understanding barriers to well-child visit attendance among racial and ethnic minority parents.

OBJECTIVES: To assess racial and ethnic minority parents' perceptions about barriers to well-child visit attendance. METHODS: For this cross-sectional qualitative study, we recruited parents of pediatric primary care patients who were overdue for a well-child visit from the largest safety net healthcare organization in central Massachusetts to participate in semi-structured interviews. The interviews focused on understanding potential knowledge, structural, and experiential barriers for well-child visit attendance. Interview content was inductively coded and directed content analysis was performed to identify themes. RESULTS: Twenty-five racial and ethnic minority parents participated; 17 (68%) of whom identified Spanish as a primary language spoken at home. Nearly all participants identified the purpose, significance, and value of well-child visits. Structural barriers were most cited as challenges to attending well-child visits, including parking, transportation, language, appointment availability, and work/other competing priorities. While language emerged as a distinct barrier, it also exacerbated some of the structural barriers identified. Experiential barriers were cited less commonly than structural barriers and included interactions with office staff, racial/ethnic discrimination, appointment reminders, methods of communication, wait time, and interactions with providers. CONCLUSIONS: Racial and ethnic minority parents recognize the value of well-child visits; however, they commonly encounter structural barriers that limit access to care. Furthermore, a non-English primary language compounds the impact of these structural barriers. Understanding these barriers is important to inform health system policies to enhance access and delivery of pediatric care with a lens toward reducing racial and ethnic-based inequities.

MEASURING PATIENT EXPERIENCE OF ORAL HEALTH CARE: A CALL TO ACTION.

BACKGROUND: Surveys to measure patients' experiences of health care are common practice in general medical care to improve patient centered care. However, such questionnaires are not consistently used to capture the patient's experience of oral health care. Because patient experience is an important component of oral health care, there is an urgent need to measure it in the oral health care setting. PURPOSE: The purpose of this article is to illustrate the need for patient experience measurement in oral health care, highlight the challenges such measurement in this setting faces, and provide a set of next steps to advance care experience measurement for dental patients. BASIC PROCEDURES: We conducted a comprehensive review of the literature examining patient experience measurement in medical and oral health care. This focused on studies aimed at understanding the current measurement landscape and existing measurement tools. We also gathered additional information and perspectives through discussions with key informants and stakeholders. MAIN FINDINGS: There is a critical need for patient experience measurement in oral health care. To develop a program to measure patient experiences of oral care, the following should be done: (1) convene stakeholders and get their buy-in; (2) develop a patient experiences of oral health conceptual framework; (3) develop a survey tool that captures key aspects of patient experiences of oral health; (4) pilot the survey tool; (5) assess the survey tool...s psychometric properties; and (6) refine and finalize the survey tool. PRINCIPLE CONCLUSIONS: To advance the measurement of the quality of oral health care, we outline a stepwise methodology that captures dental patient experiences of oral health care.

Effectiveness of a Self-Management Program to Improve Cognition and Quality of Life in Epilepsy: A Pragmatic, Randomized, Multicenter Trial.

BACKGROUND AND OBJECTIVES: We conducted a multisite, pragmatic replication trial at 4 New England epilepsy centers to determine the effectiveness of Home-Based Self-Management and Cognitive Training Changes Lives (HOBSCOTCH) in a real-world setting and to assess feasibility of a virtual intervention. METHODS: HOBSCOTCH is an 8-session intervention addressing cognitive impairment and quality of life (QoL) for people with epilepsy (PWE). Participants were recruited from epilepsy centers in 4 states and block-randomized into the following groups: in-person HOBSCOTCH (H-IP), virtual HOBSCOTCH (H-V), and waitlist control. Outcome measures were assessed for all groups at baseline, 3 months, and 6 months; intervention groups received long-term follow-up at 9 and 12 months. RESULTS: A total of 108 participants were recruited, of whom 85 were included in this analysis (age at baseline 47.5 ± 11.5 years; 68% female). Participants completing the in-person intervention (H-IP) had a 12.4-point improvement in QoL score compared with controls (p < 0.001). Pairwise comparisons found a 6.2-point treatment effect for subjective cognition in the H-IP group (p < 0.001). There were no meaningful group differences in objective cognition or health care utilization at any time points and the treatment effect for QoL diminished by 6 months. The virtual intervention demonstrated feasibility but did not significantly improve outcomes compared with controls. Within-group analysis found improvements in QoL for both H-V and H-IP. DISCUSSION: This study replicated the effectiveness of the HOBSCOTCH program in improving QoL for PWE. The study was conducted prior to the COVID-19 pandemic, but the distance-delivered intervention may be particularly well-suited for the current environment. Future research will explore modifications designed to improve the efficacy of H-V and the sustainability of HOBSCOTCH's treatment effect. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov (NCT02394509). CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in-person HOBSCOTCH delivery improved subjective measures of cognition in persons with epilepsy.

A pilot study assessing the feasibility and acceptability of Project UPLIFT adapted for patients with psychogenic nonepileptic seizures.

BACKGROUND: People with psychogenic nonepileptic seizures (PNES) are at elevated risk of multiple psychiatric comorbidities. Current treatment gaps highlight the need for time-limited, distance-delivered interventions that can be cost-effectively delivered to patients with PNES. Project UPLIFT is a self-management program addressing mood problems in epilepsy that has not previously been adapted for PNES. The purpose of this study was to assess the feasibility and acceptability of UPLIFT for patients with PNES. METHODS: Project UPLIFT was minimally adapted by a multidisciplinary research group at Dartmouth-Hitchcock Medical Center. Participants were recruited into a nonrandomized pilot study and were assessed at baseline, after completing the intervention, and at 1-month follow-up. RESULTS: The intervention was adapted and delivered without additional modification. A total of eight participants enrolled in the trial, and six participated in the first session. All six participants who started UPLIFT completed the intervention, with a 98% attendance record across the eight sessions. At follow-up, 100% reported that UPLIFT provided useful tools for daily life. All participants continued using UPLIFT after completing the program, and 83% felt it helped with seizure management. Preliminary results provide proof-of-concept for future efficacy trials. CONCLUSION: This study provides initial support for the feasibility and acceptability of Project UPLIFT, minimally adapted for patients with PNES. As a time-limited program that can be distance-delivered to groups of patients, UPLIFT may be well-suited for the healthcare environment brought on by the COVID-19 pandemic.

Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention.

OBJECTIVE: To develop a classifier that predicts reductions in depression severity in people with epilepsy after participation in an epilepsy self-management intervention. METHODS: Ninety-three people with epilepsy from three epilepsy self-management randomized controlled trials from the Managing Epilepsy Well (MWE) Network integrated research database met the inclusion criteria. Supervised machine learning algorithms were utilized to develop prediction models for changes in self-reported depression symptom severity. Features considered by the machine learning classifiers include age, gender, race, ethnicity, education, study type, baseline quality of life, and baseline depression symptom severity. The models were trained and evaluated on their ability to predict clinically meaningful improvement (i.e., a reduction of greater than three points on the nine-item Patient Health Questionnaire (PHQ-9)) between baseline and follow-up (<=12 weeks) depression scores. Models tested were a Multilayer Perceptron (ML), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression with Stochastic Gradient Descent (SGD), K-nearest Neighbors (KNN), and Gradient Boosting (GB). A separate, outside dataset of 41 people with epilepsy was used in a validation exercise to examine the top-performing model's generalizability and performance with external data. RESULTS: All six classifiers performed better than our baseline mode classifier. Support Vector Machine had the best overall performance (average area under the curve [AUC] = 0.754, highest subpopulation AUC = 0.963). Our analysis of the SVM features revealed that higher baseline depression symptom severity, study type (i.e., intervention program goals), higher baseline quality of life, and race had the strongest influence on increasing the likelihood that a subject would experience a clinically meaningful improvement in depression scores. From the validation exercise, our top-performing SVM model performed similarly or better than the average SVM model with the outside dataset (average AUC = 0.887). SIGNIFICANCE: We trained an SVM classifier that offers novel insight into subject-specific features that are important for predicting a clinically meaningful improvement in subjective depression scores after enrollment in a self-management program. We provide evidence for machine learning to select subjects that may benefit most from a self-management program and indicate important factors that self-management programs should collect to develop improved digital tools.

A systematic literature review of recommendations for referral to specialty care for patients with epilepsy.

OBJECTIVE: In epilepsy, patients who receive appropriate care receive treatment that differs substantially from those that do not. Given the need for a more detailed assessment of the role of specialty referral in the care of patients with epilepsy, this systematic literature review identified epilepsy care guidelines and recommendations that specifically address when and why people with epilepsy should be referred to specialty care. METHODS: This study identified recent (in the last 10 years) publications that made best-practice recommendations for referring people with epilepsy to a neurologist or epileptologist. We searched six databases in December 2018: MEDLINE (PubMed), Cochrane Library, ProQuest, Web of Science, CINAHL (Ebsco), Scopus (Elsevier). Search terms included "Epilepsy" OR "Seizures," "Guideline" OR "Practice Parameter," and "Referral." RESULTS: The 15 full-text articles identified included formal guidelines, summaries of these guidelines, or professional commentary that builds upon existing guidelines. Most of these publications came from the U.K and its National Institute for Health and Care Excellence. Overall, the included recommendations for referral varied considerably both for new-onset and refractory epilepsy. Although these recommendations were not consistent, it is reasonable to refer patients following the failure of 2 anti-seizure medication (ASM) trials. SIGNIFICANCE: Guidelines and informal recommendations are not consistent regarding best practices for specialty care referral for patients with epilepsy. These guidelines and recommendations should consider the context of care in real-world settings and suggest pragmatic approaches that optimize seizure control and functioning.

Antiepileptic drug effects on subjective and objective cognition.

RATIONALE: Cognitive impairment is one of the most common complaints for persons with epilepsy (PWE). These impairments are not only associated with seizures, but are also regularly reported as adverse effects of antiepileptic drugs (AEDs). Previous studies have examined cognitive effects of both AED monotherapy and polytherapy, yet there is limited research on these differences with respect to both subjective and objective cognition. The current study uses data from previous research conducted by the Centers for Disease Control and Prevention (CDC)-sponsored Managing Epilepsy Well (MEW) Network collaborative. We used three distinct archival datasets from the following: (1) the HOBSCOTCH efficacy trial at Dartmouth-Hitchcock Medical Center (HOB-1), (2) the multisite replication trial (HOB-2), and (3) epilepsy self-management research conducted at the NYU School of Medicine. METHODS: This retrospective analysis combined baseline data from three datasets to determine how the number of AEDs and the type of AEDs were associated with subjective (patient-reported) and objective (examiner-assessed) cognition. Subjective cognition was captured using the cognitive subscale of the Quality of Life in Epilepsy Inventory (QOLIE-31) in all three datasets (n = 224), while objective cognition was measured using the Repeated Battery for the Assessment of Neuropsychological Status (RBANS) in the HOB-1 dataset (n = 65) and the Brief Test of Adult Cognition by Telephone (BTACT) in the HOB-2 dataset (n = 91). Multivariable linear regression was utilized for our initial assessments, followed by propensity score matching to provide stronger control of covariates. Matching was based on significantly different covariates, such as education, depression, and history of prior epilepsy surgery. Nonparametric statistical tests were utilized to compare these matched subjects. RESULTS: Subjective cognitive impairment was significantly worse among individuals on polytherapy (2 + AEDs) compared with those on monotherapy (1 AED) (adjusted p  =  0.041). These findings were consistent with our propensity score matched comparison of monotherapy and polytherapy, which indicated that polytherapy was associated with worse overall subjective cognition (adjusted p = 0.01), in addition to impairments on the RBANS (Total score p = 0.05) and specific subdomains of the BTACT (Episodic Verbal Memory p < 0.01, Working Memory p < 0.01, Processing Speed p < 0.01). Interestingly, older generation AEDs were associated with better language performance than newer generation and combined generation AED therapy (RBANS Language p = 0.03). These language-specific findings remained significant after controlling for the effects of topiramate and zonisamide (p = 0.04). CONCLUSIONS: A greater number of AEDs is significantly and negatively associated with subjective and objective cognition in PWE, and is in line with previous research. Antiepileptic drug type did not, in itself, appear to be associated with subjective cognition. Our findings suggest that ineffective AEDs should be replaced, rather than introducing additional AEDs to a treatment regimen. Further, while subjective and objective cognition assessments were both sensitive at detecting differences based on AED status, the neuropsychological objective subdomains offer additional and specific insights into how cognition is impaired with AEDs.