Revisiting Chronic Pain Patient Profiling: An Acceptance-based Approach in an Online Sample.

UNLABELLED: Over 116 million Americans experience chronic pain, incurring an annual cost of $635 bn in healthcare and lost work. Acceptance-based therapies have gained increasing recognition for improving functional outcomes. In our online chronic pain sample, we predicted that (1) patients would cluster into low, medium and high groups of chronic pain acceptance and (2) positive affect, negative affect and perceived disability scores would differ overall by cluster, with the most positive outcomes found in the high cluster and the least found in the low cluster. Participants completed the Chronic Pain Acceptance Questionnaire, Positive and Negative Affect Scales and the Pain Disability Index. A k-means cluster analysis was conducted using activity engagement (AE) and pain willingness (PW) totals from the Chronic Pain Acceptance Questionnaire. As predicted, cluster analysis specified three groups: low AE/low PW, high AE/high PW and medium AE/medium PW. Significant multivariate analysis of covariance results were obtained according to Wilks' λ (0.55), F(6,266) = 15.39, p < 0.01, and indicated differences in positive affect, negative affect and perceived disability within each cluster. Follow-up analyses of covariance revealed mean differences in the predicted directions: the high-high group showed the most positive affect and the least negative affect and perceived disability. Conversely, the low-low group displayed the least positive affect (M = 20.28, SD = 7.86), the most negative affect (M = 28.05, SD = 9.33) and perceived disability (M = 49.57, SD = 9.46). The presence of these clusters introduces key questions about the possibility of creating tailored interventions based on cluster profiles. KEY PRACTITIONER MESSAGE: Higher levels of Acceptance are associated with better functional and affective outcomes for chronic pain patients. Lower Acceptance is associated with poorer functional and affective outcomes. Tailoring interventions using Acceptance-based profiling may improve chronic pain therapies.

Insomnia among community members in Florida: Associations with demographics, health conditions, and social support.

Objectives: To identify associations between demographics, social determinants of health, health conditions, and reported history of insomnia. A cross-sectional study including 11,960 adult community members recruited through HealthStreet, a community outreach program at University of Florida. Methods: Health assessments were conducted via interviews. Participants reported their demographic background, level of social support, history of health conditions, and insomnia. Logistic regression was used to understand associations between risk factors and history of insomnia. Results: The prevalence of self-reported insomnia was 27.3%. Adults aged ≥ 65 years (OR = 1.16) and women (OR = 1.18) reported higher rates of insomnia than their counterparts. Black/African American individuals reported lower rates of insomnia (OR = 0.72) than White individuals. Individuals with food insecurity (OR = 1.53), a military history (OR = 1.30), lower social support (OR = 1.24), living alone (OR = 1.14), anxiety (OR = 2.33), cardiometabolic disease (OR = 1.58), and attention-deficit hyperactivity disorder (ADHD) (OR = 1.44) were significantly more likely to endorse insomnia compared with their counterparts. Depression (OR = 2.57) had the strongest association with insomnia. Conclusions: This study provides evidence regarding who is at greater risk for insomnia among a large community-based sample. Our findings highlight the importance of screening for insomnia, particularly among patients who experience food insecurity, are military veterans, have anxiety, depression, ADHD, or cardiometabolic disease, as well as those who live alone or have lower levels of social support. Future public health campaigns should provide education on insomnia symptoms, treatments, and evidenced-based sleep-promotion strategies.

Trajectory of mild cognitive impairment onset.

The objective was to identify the trajectories of onset of memory and other cognitive loss in persons destined to develop mild cognitive impairment (MCI) or dementia. Healthy, community dwelling, cognitively intact elders (n = 156, mean age at entry = 83 years) were examined annually for an average of greater than 7 years. Those who developed at least two consecutive Clinical Dementia Ratings >or= 0.5 were classified as having MCI. Longitudinal mixed effects models with a change point were used to model the aging process in those with and without an MCI diagnosis during follow-up and to model the rate of change relative to the age of onset of MCI. MCI had a preclinical stage of accelerated cognitive loss that was observed 3 to 4 years before the diagnosis of MCI on tests of verbal memory, animal fluency, and visuospatial constructions. Evidence from memory performance before the change point suggests that a slow decline in memory precedes the period of accelerated decline in the development of MCI. Aging transitions leading to MCI and dementia are characterized by unique linear and nonlinear cognitive changes in several domains that precede the diagnosis of MCI and dementia by at least several years.

Outlining the scope of behavioral health practice in integrated primary care: dispelling the myth of the one-trick mental health pony.

Patient presentation in primary care ranges from psychosocial considerations to physical and mental health concerns including serious mental illness. To best prepare for addressing all aspects of health, integrated primary care practices should be equipped with the expertise and resources to appropriately treat the range of presentations. We conducted a literature review of research articles to determine the span of service types provided by behavioral health providers in primary care settings. Among 675 articles retrieved, only 17 addressed health behaviors, 64 examined both health behaviors and mental health, and 160 included only mental health topics. Within these groups, depression was the dominant screening, assessment, and treatment target, and only 42% of all studies included Method and Results sections. Literature supports that integrating behavioral health providers and services into primary care settings benefits patients, primary care providers, and the practice at large, resulting in improved care experiences. However, primary care practices appear to not use the full range of services behavioral health providers can offer. Increased health policy efforts and payment reform are needed to enable a more expansive view of what behavioral health providers could do in a primary care context.

Mental health treatment in the primary care setting: patterns and pathways.

The redesign of primary care through the patient-centered medical home offers an opportunity to assess the role of primary care in treating mental health relative to the rest of the health care system. Better understanding the patterns of care between primary care and mental health providers helps guide necessary policy changes. This article reports the findings from 109,593 respondents to the 2002-2009 Medical Expenditure Panel Surveys (MEPS). We examined the extent to which persons with poor mental health visited primary care providers, and distinguished among 4 patterns of care: (a) mental health only, (b) primary care only, (c) dual care (both mental health and primary care) and (d) other provider combinations. Our findings indicate that poor mental health and specific mental health conditions remain prevalent in primary care. An increased focus on patient-centered care requires greater integration of primary and mental health care to reduce fragmentation of care and disparities in health outcomes.

Primary care, behavioral health, provider colocation, and rurality.

PURPOSE: The purpose of this study was to characterize the proximity of primary care and behavioral health service delivery sites in the United States and factors influencing their colocation. METHODS: We geocoded the practice addresses of primary care and behavioral health providers found in the Centers for Medicare & Medicaid Services' National Plan and Provider Enumeration System Downloadable File to report where colocation is occurring throughout the country. RESULTS: The extent to which primary care physicians are colocated with behavioral health providers is strongly associated with rurality. Specifically, 40.2% of primary care physicians in urban areas are colocated with behavioral health providers compared with 22.8% in isolated rural areas and 26.5% in frontier areas. However, when controlling for number of primary care physicians at a location, the odds of colocation actually are greater for physicians in a frontier area than those in urban areas (odds ratio, 1.289; P < .01). CONCLUSIONS: Our findings offer new insights into the overlap of the behavioral health and primary care workforce, where opportunities for integration may be limited because of practice size and the proximity of providers, and where new possibilities for integration exist.