Development and psychometric evaluation of the CanSmart questionnaire to measure chronic disease self-management tasks.

BACKGROUND: Psychometrically sound measures of chronic disease self-management tasks are needed to improve identification of patient needs and to tailor self-management programs. This study aimed to develop and conduct a preliminary psychometric analysis of the CanSMART questionnaire among a diverse, multimorbid Canadian population. METHODS: The data were drawn from a cross-sectional online survey to examine self-management needs and support preferences. Participants were 306 Canadian adults with one or more physical and/or emotional chronic conditions. The questionnaire on frequency of self-management tasks was developed with substantial patient partner input. We conducted Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) of the 11 self-management tasks comprising the scale in two randomly selected subsamples, followed by Rasch analysis. Associations between patient characteristics and the self-management task subscales and individual items were explored. RESULTS: The factor analyses identified two self-management task subscales that were labelled Coping tasks (6 items) and Physical tasks (3 items), with Cronbach's alpha of 0.70 and 0.67, respectively. Rasch analysis suggested that participants had difficulty discriminating between response options "mostly" and "always". In analyses of independent associations with patient characteristics, both Coping and Physical tasks were associated with reporting more than one chronic disease and employment disability. The Coping tasks subscale was associated with female sex. Two items, on medication use and monitoring biological parameters, did not load on either scale. Both were associated with specific diagnoses. CONCLUSIONS: In this preliminary analysis, two self-management tasks subscales exhibit good psychometric properties. Two items that did not load on either scale may represent additional dimensions of self-management. This work provides the basis for further scale development and use in research and clinical practice.

A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states.

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.

Trained lay coaches and self-care cognitive-behavioral tools improve depression outcomes.

OBJECTIVES: Identify the key effective components of a depression self-care intervention. METHODS: Secondary analysis of data from 3 studies that demonstrated effectiveness of a similar depression self-care intervention (n = 275): 2 studies among patients with chronic physical conditions and 1 among cancer survivors. The studies used similar tools, and telephone-based lay coaching. Depression remission and reduction at 6 months were assessed with either PHQ-9 (chronic condition cohorts) or CES-D (cancer survivor cohort). Multiple logistic regression was used to analyze data when the interaction p-value with cohort was < 0.10. RESULTS: The 3 coached cohorts achieved better depression outcomes than usual care. The combination of coaching and joint use of 2 tools based on cognitive-behavioral therapy (CBT) was associated with depression remission and reduction among chronic condition cohorts but not among cancer survivors. Neither the number nor the length of coach calls were associated with outcomes in pooled data. CONCLUSIONS: Trained lay coaching and use of CBT-based self-care tools were associated with improved depression outcomes in patients with chronic conditions but not among cancer survivors. PRACTICE IMPLICATIONS: Trained lay coaching and CBT tools are key components of depression self-care interventions. Further research is needed on the effective components in cancer survivors.

Conditional associations between childhood cat ownership and psychotic experiences in adulthood: A retrospective study.

Ownership of cats in childhood has been inconsistently associated with psychosis in adulthood. Parasitic exposure, the putative mechanism of this association, may be more common with rodent-hunting cats, and its association with psychosis may depend on other environmental exposures. We examined the conditional associations between childhood cat ownership and the frequency of psychotic experiences in adulthood. Adults (n = 2206) were recruited in downtown Montreal to complete a survey about childhood cat ownership (non-hunting or rodent-hunting), winter birth, residential moves in childhood, head trauma history, and tobacco smoking. The frequency of psychotic experiences (PE) was measured with the 15-item positive subscale of the Community Assessment of Psychic Experiences. Associations between exposures and PE were examined in linear regressions adjusted for age and sex. Interactions among variables were explored using a conditional inference tree. Rodent-hunting cat ownership was associated with higher PE scores in male participants (vs. non-hunting or no cat ownership: SMD = 0.57; 95% CI: 0.27, 0.86), but not in female participants (SMD = 0.10; 95% CI: -0.18, 0.38). In the conditional inference tree, the highest mean PE score was in the class comprised of non-smokers with >1 residential move, head trauma history, and rodent-hunting cat ownership (n = 22; mean standard score = 0.96). The interaction between rodent-hunting cat ownership and head trauma history was supported by a post-hoc linear regression model. Our findings suggest childhood cat ownership has conditional associations with psychotic experiences in adulthood.

The PREvention Program for Alzheimer's RElated Delirium (PREPARED) cluster randomized trial: a study protocol.

BACKGROUND: Delirium is a significant cause of morbidity and mortality among older people admitted to both acute and long-term care facilities (LTCFs). Multicomponent interventions have been shown to reduce delirium incidence in the acute care setting (30-73%) by acting on modifiable risk factors. Little work, however, has focused on using this approach to reduce delirium incidence in LTCFs. METHODS: The objective is to assess the effectiveness of the multicomponent PREPARED Trial intervention in reducing the following primary outcomes: incidence, severity, duration, and frequency of delirium episodes in cognitively impaired residents. This 4-year, parallel-design, cluster randomized study will involve nursing staff and residents in 45-50 LTCFs in Montreal, Canada. Participating public and private LTCFs (clusters) that provide 24-h nursing care will be assigned to either the PREPARED Trial intervention or the control (usual care) arm of the study using a covariate constrained randomization procedure. Approximately 400-600 LTC residents aged 65 and older with dementia and/or cognitive impairment will be enrolled in the study and followed for 18 weeks. Residents must be at risk of delirium, delirium-free at baseline and have resided at the facility for at least 2 weeks. Residents who are unable to communicate verbally, have a history of specific psychiatric conditions, or are receiving end-of-life care will be excluded. The PREPARED Trial intervention consists of four main components: a decision tree, an instruction manual, a training package, and a toolkit. Primary study outcomes will be assessed weekly. Functional autonomy and cognitive levels will be assessed at the beginning and end of follow-up, while information pertaining to modifiable delirium risk factors, medical consultations, and facility transfers will be collected retrospectively for the duration of the follow-up period. Primary outcomes will be reported at the level of intervention assignment. All researchers analyzing the data will be blinded to group allocation. DISCUSSION: This large-scale intervention study will contribute significantly to the development of evidence-based clinical guidelines for delirium prevention in this frail elderly population, as it will be the first to evaluate the efficacy of a multicomponent delirium prevention program translated into LTC clinical practice on a large scale. TRIAL REGISTRATION: NCT03718156 , ClinicalTrials.gov .

Front-line emergency department nurses' and physicians' assessments of processes of elder-friendly care for quality improvement.

INTRODUCTION: Geriatric emergency department (ED) care has gained increasing importance and interest due to increasing visits in seniors. AIM: Among ED front-line nurses and physicians, to assess and compare ratings of elder-friendly care process indicators, variability in ratings, and concurrent validity of ratings. METHODS: Four Quebec EDs' full-time registered nurses and physicians rated their geriatric care using 9 subscales. Nurse and physician subscale scores were compared. Inter-rater variability within disciplines and variability between nurses and physicians were measured. Associations between the subscale scores and perceived overall quality of care were tested. RESULTS: 38 nurses and 36 physicians completed the survey (83% of 89 eligible). Scores differed by discipline for 3 of 9 subscales computed; nurses had higher mean scores on Protocols, Family-Centered Discharge, and Staff Education. Very high variation for Staff Education was found within disciplines. Variations for Family-Centered Discharge differed significantly between nurses and physicians. Almost all subscale scores were significantly positively associated with perceived overall quality of care. CONCLUSIONS: ED nurses and physicians rate geriatric care components similarly except for protocols, discharge processes, and continuing education. The subscales have concurrent validity. Results suggest a need for improvement in continuing educational strategies with a particular attention to discharge processes.

CanDirect: Effectiveness of a Telephone-Supported Depression Self-Care Intervention for Cancer Survivors.

PURPOSE: Depression in post-treatment cancer survivors is common and can impair quality of life. CanDirect is a novel, telephone-delivered depression self-care intervention for cancer survivors. We conducted a randomized controlled superiority trial to compare CanDirect with usual care (UC) in this population. METHODS: Participants completing cancer treatment within the past 10 years who had mild-moderate depressive symptoms with or without major depression were recruited from clinical and community settings in Quebec and Ontario. Permuted block random assignment allocated participants to CanDirect plus UC or to UC alone. Assessments of depression severity (Center for Epidemiological Studies-Depression scale [CES-D]; primary outcome) and secondary outcomes health-related quality of life (Short Form Survey-12 mental and physical component summaries), anxiety symptoms (Hospital Anxiety and Depression Scale), activation (Patient Activation Measure), depression diagnosis (Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV), and health services (self-report) were conducted at baseline, as well as 3 and 6 months (primary time point). Analyses of outcomes were adjusted for covariates using linear regression and missing data by inverse probability weighting. RESULTS: Participants recruited between September 2016 and October 2018 were randomly assigned to CanDirect (n = 121) or UC (n = 124). Among 245 participants randomly assigned, 218 (89.0%) completed the primary outcome at 6 months. CanDirect participants reported less severe depressive symptoms on the CES-D than UC participants at 6 months, adjusted effect size (ES) 0.61 (95% CI, 0.33 to 0.88). CanDirect participants also had significantly greater quality of life, lower anxiety, more activation, and lower rates of depression diagnoses, compared with UC. Exploratory analysis suggested that sex was a modifier of the primary outcome (interaction term P value = .03); the intervention was less effective in men (ES, 0.12; 95% CI, -0.45 to 0.69). CONCLUSION: The findings suggest that CanDirect is an effective method of managing mild-moderate depression symptoms in cancer survivors.

The effect of culturally-adapted health education interventions among culturally and linguistically diverse (CALD) patients with a chronic illness: A meta-analysis and descriptive systematic review.

OBJECTIVE: To review the effectiveness of health education interventions adapted for culturally and linguistically diverse (CALD) populations with a chronic illness. METHODS: A systematic review and meta-analysis were conducted. Eligible studies were identified across six databases. Data were extracted and intervention effect was summarized using standardized mean difference. If there were insufficient data for meta-analysis, a descriptive summary was included. Modifying effects of intervention format, length, intensity, provider, self-management skills taught, and behavioral change techniques (BCTs) utilized were examined. RESULTS: 58 studies were reviewed and data were extracted for 36 outcomes. Most interventions used multiple modes of delivery and were facilitated by bilingual health care professionals (HCPs). On average, interventions included 5.19 self-management skills and 4.82 BCTs. Interventions were effective in reducing BMI, cholesterol, triglycerides, blood glucose, HbA1C, and depression, and in increasing knowledge. Effectiveness was influenced partly by provider, with HCPs favored over lay providers or paraprofessionals in increasing knowledge; however, the opposite was noted for HbA1c. CONCLUSIONS: Health education interventions are effective among CALD populations, particularly at improving objective, distal outcomes (e.g., anthropometric measures). These interventions may be equally effective in improving proximal patient-reported outcomes (PROs); however, diversity in PROs limited analyses. PRACTICE IMPLICATIONS: Core outcome sets (COS) are needed to further investigate and compare health education intervention effectiveness on PROs.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

SeSaMe PS Function: Functional Analysis of the Whole Metagenome Sequencing Data of the Arbuscular Mycorrhizal Fungi.

In this study, we introduce a novel bioinformatics program, Spore-associated Symbiotic Microbes Position-specific Function (SeSaMe PS Function), for position-specific functional analysis of short sequences derived from metagenome sequencing data of the arbuscular mycorrhizal fungi. The unique advantage of the program lies in databases created based on genus-specific sequence properties derived from protein secondary structure, namely amino acid usages, codon usages, and codon contexts of 3-codon DNA 9-mers. SeSaMe PS Function searches a query sequence against reference sequence database, identifies 3-codon DNA 9-mers with structural roles, and creates a comparative dataset containing the codon usage biases of the 3-codon DNA 9-mers from 54 bacterial and fungal genera. The program applies correlation principal component analysis in conjunction with K-means clustering method to the comparative dataset. 3-codon DNA 9-mers clustered as a sole member or with only a few members are often structurally and functionally distinctive sites that provide useful insights into important molecular interactions. The program provides a versatile means for studying functions of short sequences from metagenome sequencing and has a wide spectrum of applications. SeSaMe PS Function is freely accessible at www.fungalsesame.org.

SeSaMe: Metagenome Sequence Classification of Arbuscular Mycorrhizal Fungi-associated Microorganisms.

Arbuscular mycorrhizal fungi (AMF) are plant root symbionts that play key roles in plant growth and soil fertility. They are obligate biotrophic fungi that form coenocytic multinucleated hyphae and spores. Numerous studies have shown that diverse microorganisms live on the surface of and inside their mycelia, resulting in a metagenome when whole-genome sequencing (WGS) data are obtained from sequencing AMF cultivated in vivo. The metagenome contains not only the AMF sequences, but also those from associated microorganisms. In this study, we introduce a novel bioinformatics program, Spore-associated Symbiotic Microbes (SeSaMe), designed for taxonomic classification of short sequences obtained by next-generation DNA sequencing. A genus-specific usage bias database was created based on amino acid usage and codon usage of a three consecutive codon DNA 9-mer encoding an amino acid trimer in a protein secondary structure. The program distinguishes between coding sequence (CDS) and non-CDS, and classifies a query sequence into a genus group out of 54 genera used as reference. The mean percentages of correct predictions of the CDS and the non-CDS test sets at the genus level were 71% and 50% for bacteria, 68% and 73% for fungi (excluding AMF), and 49% and 72% for AMF (Rhizophagus irregularis), respectively. SeSaMe provides not only a means for estimating taxonomic diversity and abundance but also the gene reservoir of the reference taxonomic groups associated with AMF. Therefore, it enables users to study the symbiotic roles of associated microorganisms. It can also be applicable to other microorganisms as well as soil metagenomes. SeSaMe is freely available at www.fungalsesame.org.

Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models.

It is challenging to estimate the phenotypic impact of the structural genome changes known as copy-number variations (CNVs), since there are many unique CNVs which are nonrecurrent, and most are too rare to be studied individually. In recent work, we found that CNV-aggregated genomic annotations, that is, specifically the intolerance to mutation as measured by the pLI score (probability of being loss-of-function intolerant), can be strong predictors of intellectual quotient (IQ) loss. However, this aggregation method only estimates the individual CNV effects indirectly. Here, we propose the use of hierarchical Bayesian models to directly estimate individual effects of rare CNVs on measures of intelligence. Annotation information on the impact of major mutations in genomic regions is extracted from genomic databases and used to define prior information for the approach we call HBIQ. We applied HBIQ to the analysis of CNV deletions and duplications from three datasets and identified several genomic regions containing CNVs demonstrating significant deleterious effects on IQ, some of which validate previously known associations. We also show that several CNVs were identified as deleterious by HBIQ even if they have a zero pLI score, and the converse is also true. Furthermore, we show that our new model yields higher out-of-sample concordance (78%) for predicting the consequences of carrying known recurrent CNVs compared with our previous approach.

Lidocaine With Epinephrine Versus Bupivacaine With Epinephrine as Local Anesthetic Agents in Wide-Awake Hand Surgery: A Pilot Outcome Study of Patient's Pain Perception.

Purpose: Wide-awake local anesthesia hand surgery has many advantages over other forms of anesthesia, including faster recovery, lower cost, and improved patient safety; however, few studies compare postoperative pain and analgesic consumption after long- and short-acting anesthetics. This is important because surgeons seek to minimize opioid consumption during the opioid epidemic. Methods: This was a double-blinded, prospective, randomized, parallel design pilot study. We randomized 61 patients to receive carpal tunnel surgery with a short-lasting regional anesthetic (lidocaine, 29 patients) or a long-lasting one (bupivacaine, 32 patients). Primary outcomes were pain levels over the first and second 24 hours. Secondary outcomes were postoperative consumption of acetaminophen and opioids over the first and second 12 hours after surgery. Results: Pain intensity and acetaminophen consumption were significantly less in the bupivacaine group over the first 24 and 12 hours after surgery, respectively. The bupivacaine group consumed less opioids in the first 12 hours and delayed consumption of the first medication after surgery, but these results were not statistically significant. There was no difference in pain intensity or analgesic consumption between 24 and 48 hours after surgery. Conclusions: The use of a long-term anesthetic (bupivacaine) over a short-term one (lidocaine) in awake carpal tunnel release surgery decreases postoperative pain over the initial 12 hours after surgery and delays the initiation of analgesic consumption; however, this difference is small. The amount of opioid consumption was not significantly different between groups, but both groups consumed less than 10% of the prescribed opioids. It is important to reevaluate the need for opioids in minor hand surgery and favor the use of alternatives such as nonsteroidal anti-inflammatory drugs and acetaminophen. Type of study/level of evidence: Therapeutic I.

Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

Background: A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reason for admission, indication for delivery, optimal steroid use, gestational age, and other potential prognostic factors. Using stepwise multivariable analysis, the only two variables independently associated with serious neonatal morbidity were gestational age and the presence of suspected intrauterine growth restriction as a reason for admission. This finding was surprising given the beneficial effects of antenatal steroids and hazards associated with some causes of preterm birth. Multivariable logistic regression techniques have limitations. Without testing for multiple interactions, linear regression will identify only individual factors with the strongest independent relationship to the outcome for the entire study group. There may not be a single "best set" of risk factors or one set that applies equally well to all subgroups. In contrast, machine learning techniques find the most predictive groupings of factors based on their frequency and strength of association, with no attempt to identify independence and no assumptions about linear relationships.Objective: To determine if machine learning techniques would identify specific clusters of conditions with different probability estimates for severe neonatal morbidity and to compare these findings to those based on the original multivariable analysis.Materials and methods: This was a secondary analysis of data collected in a multicenter, prospective study on all admissions to the neonatal intensive care unit between 2013 and 2015 in 10 hospitals. We included all patients with a singleton, stillborn, or live newborns, with a gestational age between 23 0/7 and 31 6/7 week. The composite endpoint, severe neonatal morbidity, defined by the presence of any of five outcomes: death, grade 3 or 4 intraventricular hemorrhage (IVH), and ≥28 days on ventilator, periventricular leukomalacia (PVL), or stage III necrotizing enterocolitis (NEC), was present in 238 of the 1039 study patients. We studied five explanatory variables: maternal age, parity, gestational age, admission reason, and status with respect to antenatal steroid administration. We concentrated on Classification and Regression Trees because the resulting structure defines clusters of risk factors that often bear resemblance to clinical reasoning. Model performance was measured using area under the receiver-operator characteristic curves (AUC) based on 10 repetitions of 10-fold cross-validation.Results: A hybrid technique using a combination of logistic regression and Classification and Regression Trees had a mean cross-validated AUC of 0.853. A selected point on its receiver-operator characteristic (ROC) curve corresponding to a sensitivity of 81% was associated with a specificity of 76%. Rather than a single curve representing the general relationship between gestational age and severe morbidity, this technique found seven clusters with distinct curves. Abnormal fetal testing as a reason for admission with or without growth restriction and incomplete steroid administration would place a 20-year-old patient on the highest risk curve.Conclusions: Using a relatively small database and a few simple factors known before birth it is possible to produce a more tailored estimate of the risk for severe neonatal morbidity on which clinicians can superimpose their medical judgment, experience, and intuition.

Self-management across chronic diseases: Targeting education and support needs.

OBJECTIVES: Among Canadian adults with chronic disease: 1) to identify groups that differ in self-management task frequency and self-efficacy; 2) to compare group characteristics and preferences for self-management support. METHODS: Using data from an online survey, cluster analysis was used to identify groups that differed in self-management task frequency and self-efficacy. Multivariable regression was used to explore relationships with patient characteristics and preferences. RESULTS: Cluster analysis (n = 247) revealed three groups:Vulnerable Self-Managers (n = 55), with the highest task frequency and lowest self-efficacy; Confident Self-Managers (n = 73), with the lowest task frequency and highest self-efficacy; and Moderate Needs Self-Managers (n = 119), with intermediate task frequency and self-efficacy. Vulnerable Self-Managers, when compared with the Confident group, were more often: on illness-related employment disability or unemployed; less well educated; diagnosed with emotional problems or hypertension, and had greater multimorbidity. They participated less often in self-management programs, and differed in support preferences. CONCLUSIONS: Knowing the characteristics of vulnerable self-managers can help in targeting those in greater need for self-management support that matches their preferences. PRACTICE IMPLICATIONS: Different approaches are needed to support self-management in the vulnerable population.

Constrained instruments and their application to Mendelian randomization with pleiotropy.

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer's disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55-66) to disentangle causal relationships of several biomarkers with AD progression.

Latent class analysis of the multivariate Delirium Index in long-term care settings.

ABSTRACTBackground:A few studies examine the time evolution of delirium in long-term care (LTC) settings. In this work, we analyze the multivariate Delirium Index (DI) time evolution in LTC settings. METHODS: The multivariate DI was measured weekly for six months in seven LTC facilities, located in Montreal and Quebec City. Data were analyzed using a hidden Markov chain/latent class model (HMC/LC). RESULTS: The analysis sample included 276 LTC residents. Four ordered latent classes were identified: fairly healthy (low "disorientation" and "memory impairment," negligible other DI symptoms), moderately ill (low "inattention" and "disorientation," medium "memory impairment"), clearly sick (low "disorganized thinking" and "altered level of consciousness," medium "inattention," "disorientation," "memory impairment" and "hypoactivity"), and very sick (low "hypoactivity," medium "altered level of consciousness," high "inattention," "disorganized thinking," "disorientation" and "memory impairment"). Four course types were also identified: stable, improvement, worsening, and non-monotone. Class order was associated with increasing cognitive impairment, frequency of both prevalent/incident delirium and dementia, mortality rate, and decreasing performance in ADL. CONCLUSION: Four ordered latent classes and four course types were found in LTC residents. These results are similar to those reported previously in acute care (AC); however, the proportion of very sick residents at enrolment was larger in LTC residents than in AC patients. In clinical settings, these findings could help identify participants with a chronic clinical disorder. Our HMC/LC approach may help understand coexistent disorders, e.g. delirium and dementia.

Self-management support in primary care is associated with improvement in patient activation.

OBJECTIVES: To examine: 1) whether patient characteristics predict perceived self-management support (usefulness of information and collaborative care planning) by primary care providers after six months, and 2) the association between perceived self-management support and patient activation at 6 months METHODS: We conducted a secondary analysis among 120 primary care patients aged 40 and over with a chronic physical condition and comorbid depressive symptoms who participated in a randomized controlled trial of a coaching intervention for depression self-management. Activation was measured at baseline (T0) and 6 months (T1). Self-management support was captured at T1 for physical and mood problems. RESULTS: The sample of analysis was 120 patients who completed all relevant measures. At T1, the perceived usefulness of information for mood self-management was independently associated with activation. More severe depressive symptoms at T0 predicted lower perceived usefulness of chronic condition self-management information at T1. Lower T0 mental health-related quality of life predicted lower perceived usefulness of mood self-management information at T1. CONCLUSIONS: Perceived informational support for mood self-management may contribute to increased activation. Patients with more severe mental health symptoms or impairment perceive that they receive less useful self-management information from their care team. PRACTICE IMPLICATIONS: Care teams should determine whether patients with mood problems need greater self-management support.

Shedding light on the link between early life sun exposure and risk of multiple sclerosis: results from the EnvIMS Study.

BACKGROUND: Lower levels of sun exposure in childhood have been suggested to be associated with increased risk of multiple sclerosis (MS). In this paper we extend previous work, using two novel analytical strategies. METHODS: Data collected in the Environmental risk factors In MS (EnvIMS) study, a case-control study with MS cases and population-based controls from Canada, Italy and Norway, were used. Participants reported on sun exposure behaviours for 5-year age intervals from birth; we focused on the first three age intervals (≤15 years). We compared two life course epidemiology conceptual models, the critical period and the accumulation model. We also used latent class analysis to estimate MS risk for different latent sun exposure behaviour groups. RESULTS: The analyses included 2251 cases and 4028 controls. The accumulation model was found to be the best model, which demonstrated a nearly 50% increased risk of MS comparing lowest reported summer sun exposure with highest [risk ratio (RR) = 1.47 (1.24, 1.74)]. The latent sun exposure behaviour group, characterized by low sun exposure during summer and winter and high sun protection use, had the highest risk of MS; a 76% increased risk as compared with the group with high sun exposure and low sun protection use [RR = 1.76 (1.27, 2.46)]. CONCLUSIONS: Our analyses provide novel insights into the link between sun exposure and MS. We demonstrate that more time indoors during childhood and early adolescence is linked with MS risk, and that sun protection behaviours in those who spend most time indoors may play a key role in increasing risk.