Food insecurity and loneliness amongst older urban subsidised housing residents: The importance of social connectedness.

Food insecurity and loneliness are shockingly large and growing problems in the older population in the U.S. and globally. Thus, the study's primary aim was to investigate the prevalence as well as the association between these two social determinants of health in the marginalised and often underserved population of older residents of urban subsidised housing (also referred to as social housing or affordable housing). Of particular interest was whether a greater sense of loneliness increases the risk of older residents being food insecure. The study uses survey data gathered through in-person interviews conducted onsite in English or Spanish with 216 of the 300 older adults (72% response rate) living in a subsidised housing community in a central neighbourhood of a U.S. northeastern city. Amongst the 216 participants, 50% identified as Black and 45% identified as Latino. In addition to food insecurity and loneliness, the survey assessed participants' health, food access challenges, and sociodemographic characteristics. Results revealed a 34% food insecurity prevalence rate amongst the participants which is higher than the estimate of slightly less than 10% of the U.S. older population being food insecure. The participants' 34% loneliness rate was also higher than the 19%-29% prevalence range in U.S. older population studies. Bivariate analyses highlighted the interrelatedness or intertwining of food insecurity, loneliness, poor health and food access challenges whilst a series of logistic regressions further explored the significant association of loneliness with food insecurity. Loneliness increased the odds ratio of food insecurity in the binary (unadjusted) regression (p < 0.001, OR = 3.1) and the multivariate (adjusted) regression that included all covariates (p < 0.05, OR = 2.4). Overall, the findings underscore the need for greater awareness of loneliness as a public health threat as well as future research exploring how loneliness may intensify the health consequences of food insecurity in later life.

Religious coping and psychological well-being in Latino Alzheimer's caregivers.

OBJECTIVES: Prior research has demonstrated the benefits of religion on caregiver well-being; however, less research exists on the processes underlying this relationship, particularly in Latino Alzheimer's disease (AD) families. This study's aim was therefore to explore the direct and indirect influences of positive religious coping (RC-P) and negative religious coping (RC-N) on caregivers' experiences of depression and anxiety. METHODS: The data are from the Circulo de Cuidado Study baseline interviews; participant eligibility criteria were that the person identifies as being Latino, providing at least 5 h of care weekly, and their relative has an AD diagnosis and neuropsychiatric symptoms. In person at-home interviews were conducted in Spanish with 67 caregivers. Religious coping was assessed with the Brief RCOPE; depression and anxiety were measured with the Center for Epidemiological Studies Depression Scale-Spanish version and State Anxiety Inventory. Multivariate regression analyses, controlling for caregiving stressors, were used to test our core hypotheses. RESULTS: As hypothesized, RC-N had significant direct effect on depression (p < 0.001) and anxiety (p < 0.001) as well as partially mediated the relationship between caregiver subjective stressor and depression (p < 0.01) and anxiety (p < 0.01). However, contrary to our hypothesis, RC-P did not have a direct or indirect effects on psychological well-being. CONCLUSIONS: Our findings are consistent with other studies linking RC-N to poorer caregiver outcomes. Caregivers who questioned God's power, perceived God punishing them, or felt abandoned by God reported greater levels of depression and anxiety. The results suggest that caregivers experiencing religious distress may feel overwhelmed and view a more hopeless future.

A pathway for multivariate analysis of ecological communities using copulas.

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity-based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over-dispersion, and/or zero-inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation-based filter with exact family-wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity-based settings. Our proposed analysis pathway provides new opportunities to combine model-based approaches with dissimilarity-based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.

Social Worker-Aided Palliative Care Intervention in High-risk Patients With Heart Failure (SWAP-HF): A Pilot Randomized Clinical Trial.

Importance: Palliative care considerations are typically introduced late in the disease trajectory of patients with advanced heart failure (HF), and access to specialty-level palliative care may be limited. Objective: To determine if early initiation of goals of care conversations by a palliative care-trained social worker would improve prognostic understanding, elicit advanced care preferences, and influence care plans for high-risk patients discharged after HF hospitalization. Design, Setting, and Participants: This prospective, randomized clinical trial of a social worker-led palliative care intervention vs usual care analyzed patients recently hospitalized for management of acute HF who had risk factors for poor prognosis. Analyses were conducted by intention to treat. Interventions: Key components of the social worker-led intervention included a structured evaluation of prognostic understanding, end-of-life preferences, symptom burden, and quality of life with routine review by a palliative care physician; communication of this information to treating clinicians; and longitudinal follow-up in the ambulatory setting. Main Outcomes and Measures: Percentage of patients with physician-level documentation of advanced care preferences and the degree of alignment between patient and cardiologist expectations of prognosis at 6 months. Results: The study population (N = 50) had a mean (SD) age of 72 (11) years and had a mean (SD) left ventricular ejection fraction of 0.33 (13). Of 50 patients, 41 (82%) had been hospitalized more than once for HF management within 12 months of enrollment. At enrollment, treating physicians anticipated death within a year for 32 patients (64%), but 42 patients (84%) predicted their life expectancy to be longer than 5 years. At 6 months, more patients in the intervention group than in the control group had physician-level documentation of advanced care preferences in the electronic health record (17 [65%] vs 8 [33%]; χ2 = 5.1; P = .02). Surviving patients allocated to intervention were also more likely to revise their baseline prognostic assessment in a direction consistent with the physician's assessment (15 [94%] vs 4 [26%]; χ2 = 14.7; P < .001). Among the 31 survivors at 6 months, there was no measured difference between groups in depression, anxiety, or quality-of-life scores. Conclusions and Relevance: Patients at high risk for mortality from HF frequently overestimate their life expectancy. Without an adverse impact on quality of life, prognostic understanding and patient-physician communication regarding goals of care may be enhanced by a focused, social worker-led palliative care intervention that begins in the hospital and continues in the outpatient setting. Trial Registration: clinicaltrials.gov Identifier: NCT02805712.

Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study.

OBJECTIVES: The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis. METHODS: The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent. RESULTS: None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects. CONCLUSIONS: Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.