Latent Class Mixture Models Research Articles

Longitudinal Examination of Aggression and Study Skills From Middle to High School: Implications for Dropout Prevention.

High school completion provides health and economic benefits. The purpose of this study is to describe dropout rates based on longitudinal trajectories of aggression and study skills using teacher ratings. The sample consisted of 620 randomly selected sixth graders. Every year from Grade 6 to 12, a teacher completed a nationally normed behavioral rating scale. We used latent class mixture modeling to identify the trajectories. Participants followed 3 trajectories of aggression (Low, Medium Desisting, and High Desisting) and 5 trajectories of study skills (Low, Average-Low, Decreasing, Increasing, and High). Over three-quarters of the sample were in stable trajectories of study skills over time. Most students in the High Desisting Aggression group were in the Low Study Skills group, and all students in the High Study Skills group were in the Low Aggression group. The overall dropout rate was 17%, but varied dramatically across combined aggression and study skills groups, ranging from 2% to 50%. The results highlight the importance of early prevention that combines academic enhancement and behavioral management for reducing school dropout.

Predictors and outcomes of somatization in bipolar I disorder: A latent class mixture modeling approach

BackgroundMood disorders are often associated with somatic symptoms. The role of somatic symptoms on disease progression in unipolar depression is substantially better characterized than that role in bipolar disorder. Moreover, the contribution of comorbid anxiety disorders and medical illness is not well understood. MethodWe investigated 527 patients with bipolar I disorder clustered within 102 families using a latent class approach. Predictors were added stepwise into the model. Anxiety and commonly associated medical illnesses were added as covariates. ResultsThe rate of somatic symptoms in this sample was 73% (mean 1.7 symptoms), and 27.3% had a comorbid anxiety disorder. A two-class model, with a subgroup at high-risk for somatization, gave the best fit to the data. Multilevel mixture modeling accounted for family clusters. Somatic symptoms were independently associated with disease severity, defined as earlier age of first seeking psychiatric help (x = 21.7 vs x = 24.7, p = 0.005) and first psychiatric hospitalization (x = 25.7 vs x = 28.2, p = 0.03), greater probability of attempting suicide (x = 0.41 vs x = 0.32, p = 0.047), and rapid-cycling disease course (x = 0.57 vs x = 0.36, p < 0.001). Persons with few or no somatic symptoms were more likely to be hospitalized for severe mania (x = 0.63 vs x = 0.51; p = 0.013), but did not significantly differ in hospitalization for severe depression. LimitationsThe study is correlational. Information on pharmacologic interventions and comorbid diseases was limited. ConclusionsSomatic symptoms in bipolar disorder could be an independent indicator for disease severity, suicidality, and rapid-cycling disease course. In severe mental illness, somatic and psychological symptoms must be jointly addressed.

Patterns of self-care and clinical events in a cohort of adults with heart failure: 1 year follow-up

BackgroundHeart failure (HF) self-care is important in reducing clinical events (all-cause mortality, emergency room visits and hospitalizations). HF self-care behaviors are multidimensional and include maintenance (i.e. daily adherence behaviors), management (i.e. symptom response behaviors) and consulting behaviors (i.e. contacting a provider when appropriate). Across these dimensions, patterns of successful patient engagement in self-care have been observed (e.g. successful in one dimension but not in others), but no previous studies have linked patterns of HF self-care to clinical events. ObjectivesTo identify patterns of self-care behaviors in HF patients and their association with clinical events. MethodsThis was a prospective, non-experimental, cohort study. Community-dwelling HF patients (n = 459) were enrolled across Italy, and clinical events were collected one year after enrollment. We measured dimensions of self-care behavior with the Self-Care of HF Index (maintenance, management, and confidence) and the European HF Self-care Behavior Scale (consulting behaviors). We used latent class mixture modeling to identify patterns of HF self-care across dimensions, and Cox proportional hazards modeling to quantify event-free survival over 12 months of follow-up. ResultsPatients (mean age 71.8 ± 12.1 years) were mostly males (54.9%). Three patterns of self-care behavior were identified; we labeled each by their most prominent dimensional characteristic: poor symptom response, good symptom response, and maintenance-focused behaviors. Patients with good symptom response behaviors had fewer clinical events compared with those who had poor symptom response behaviors (adjusted hazard ratio = 0.66 [0.46–0.96], p = 0.03). Patients with poor symptom response behaviors had the most frequent clinical events. Patients with poor symptom response and those with maintenance-focused behaviors had a similar frequency of clinical events. ConclusionsSelf-care is significantly associated with clinical events. Routine assessment, mitigation of barriers, and interventions targeting self-care are needed to reduce clinical events in HF patients.

Domain-specific market segmentation using a latent class mixture modelling approach and wine-related lifestyle (WRL) algorithm

PurposeSince the publication of Van Raaij and Verhallen’s seminal work in European Journal of Marketing in 1994, identifying the domain-specific market segmentation approach as one of the most feasible for segmenting markets, there has been surprisingly limited development in this field, with the food domain as the only exception. This study aims to develop a methodological approach using latent class mixture modelling as contribution in the domain-specific market segmentation field.Design/methodology/approachThis study captures the AIO lifestyle perspective using a domain-specific 80-item algorithm which has the wine (product) domain as its focus. A sample size of 811 consumers is used from data collected by means of the CATI approach.FindingsThe authors use four criteria for model selection: comparison of the Bayesian information criterion (BIC) statistic, comparison of classification error, verification of the interpretation of the derived segments and, finally, use of the conditional bootstrap procedure to test whether the selected model provides a significant improvement over the previous model. The five-segment model option yields a minimum BIC, the classification error measure is minimal and is easier to interpret than the other models. Segment descriptions for the five identified lifestyle-based segments are developed.Research limitations/implicationsSegmentation by traditional k-means clustering has proven to be less useful than the more innovative alternative of mixture regression modelling; therefore, the authors identify segments in the market on the basis of individuals’ domain-specific lifestyle characteristics using a latent class mixture modelling approach.Practical implicationsFollowing the attainment of a clear and robust market segmentation structure, the simultaneous analysis of the lifestyles, demographics and behaviours of consumers as nexus of the domain-specific segmentation approach, provides rich and valid information accurately informing the market segment descriptions.Originality/valueThe authors make a substantive contribution by developing a methodological approach using latent class mixture modelling; the first of its kind in the area of domain-specific segmentation. Next, they use the discriminant and/or predictive validity of the 80-scale items to predict cluster membership using the WRL algorithm. Finally, the authors describe the identified market segments in detail and outline the practical implications.

Latent Class Mixture Models of Treatment Effect Heterogeneity

We provide a general Bayesian framework for modeling treatment effect heterogeneity in experiments with non-categorical outcomes. Our modeling approach incorporates latent class mixture components to capture discrete heterogeneity and regression interaction terms to capture continuous heterogeneity. Flexible error distributions allow robust posterior inference on parameters of interest. Hierarchical shrinkage priors on relevant parameters address multiple comparisons concerns. Leave-one-out cross validation estimates of expected posterior predictive density obtained through importance sampling, together with posterior predictive checks, provide a convenient method for model selection and evaluation. We apply our approach to a clinical trial comparing two HIV treatments and to an instrumental variable analysis of a natural experiment on the effect of Medicaid enrollment on emergency department utilization.

An approach to understanding sleep and depressed mood in adolescents: person-centred sleep classification.

Depressive mood in youth has been associated with distinct sleep dimensions, such as timing, duration and quality. To identify discrete sleep phenotypes, we applied person-centred analysis (latent class mixture models) based on self-reported sleep patterns and quality, and examined associations between phenotypes and mood in high-school seniors. Students (n=1451; mean age=18.4±0.3years; 648M) completed a survey near the end of high-school. Indicators used for classification included school night bed- and rise-times, differences between non-school night and school night bed- and rise-times, sleep-onset latency, number of awakenings, naps, and sleep quality and disturbance. Mood was measured using the total score on the Center for Epidemiologic Studies-Depression Scale. One-way anova tested differences between phenotype for mood. Fit indexes were split between 3-, 4- and 5-phenotype solutions. For all solutions, between phenotype differences were shown for all indicators: bedtime showed the largest difference; thus, classes were labelled from earliest to latest bedtime as 'A' (n=751), 'B' (n=428) and 'C' (n=272) in the 3-class solution. Class B showed the lowest sleep disturbances and remained stable, whereas classes C and A each split in the 4- and 5-class solutions, respectively. Associations with mood were consistent, albeit small, with class B showing the lowest scores. Person-centred analysis identified sleep phenotypes that differed in mood, such that those with the fewest depressive symptoms had moderate sleep timing, shorter sleep-onset latencies and fewer arousals. Sleep characteristics in these groups may add to our understanding of how sleep and depressed mood associate in teens.

Trajectories of Functional Change After Inpatient Rehabilitation for Traumatic Brain Injury

ObjectiveTo examine trajectories of functional recovery after rehabilitation for traumatic brain injury (TBI). DesignProspective study. SettingInpatient rehabilitation hospitals in the Uniform Data System for Medical Rehabilitation. ParticipantsA subset of individuals receiving inpatient rehabilitation services for TBI from 2002 to 2010 who also had postdischarge measurement of functional independence (N=16,583). InterventionsInpatient rehabilitation. Main Outcomes MeasuresAdmission, discharge, and follow-up data were obtained from the Uniform Data System for Medical Rehabilitation. We used latent class mixture models to examine recovery trajectories for both cognitive and motor functioning as measured by the FIM instrument. ResultsLatent class models identified 3 trajectories (low, medium, high) for both cognitive and motor FIM subscales. Factors associated with membership in the low cognition trajectory group included younger age, male sex, racial/ethnic minority, Medicare or Medicaid (vs commercial or other insurance), comorbid conditions, and greater duration from injury date to rehabilitation admission date. Factors associated with membership in the low motor trajectory group included older age, racial/ethnic minority, Medicare or Medicaid coverage, comorbid conditions, open head injury, and greater duration to admission. ConclusionsStandard approaches to assessing recovery patterns after TBI obscure differences between subgroups with trajectories that differ from the overall mean. Select demographic and clinical characteristics can help classify patients with TBI into distinct functional recovery trajectories, which can enhance both patient-centered care and quality improvement efforts.

Patterns, relevance and predictors of heart failure dyadic symptom appraisal.

Caregivers are thought to play a major role in helping patients first appraise and then respond to heart failure (HF) symptoms. The aims of this study were to: (a) characterise distinct patterns of HF patient-caregiver dyads with respect to symptom appraisal; and (b) link dyadic symptom appraisal to contributions to self-care and caregiver strain. A cross-sectional dyadic descriptive design was used to capture patient and caregiver appraisal of patient HF symptoms (i.e. dyspnoea, fatigue, pain and anxiety). Contributions to self-care were measured using patient and caregiver versions of the Self-Care of Heart Failure Index and the European Heart Failure Self-care Behaviour Scale. Caregiver strain was measured using the Multidimensional Caregiver Strain Index. Multilevel and latent class mixture modelling (LCMM) were used to examine distinct patterns of symptom appraisal. Two patterns of dyadic symptom appraisal were identified: one pattern ( n = 24; 38.7%) wherein caregivers appraised patients' symptoms as being significantly worse than did the patient (labelled as 'Caregiver > Patient'); and a second pattern ( n = 38; 61.3%) wherein patients appraised their symptoms similar to or worse than that as perceived by their caregiver (labelled as 'Patient ⩾ Caregiver'). Dyads in the Caregiver > Patient pattern of symptom appraisal reported much better contributions to self-care (symptom response behaviours only), but also greater caregiver strain, compared with dyads in the Patient ⩾ Caregiver pattern. Greater patient depression and older caregiver age were significant determinants of fitting the Patient ⩾ Caregiver pattern. Differences in how HF patients and caregivers appraise symptoms together must be taken into consideration when examining contributions to HF care and caregiver outcomes.

An Empirical Model of Behavioral Heterogeneity

In this paper we propose a new latent class/mixture model (LCM) to determine whether firms behave like profit maximizers or just cost minimizers when there is no additional sample separation information. Since some firms might be maximizing profit while others might minimize cost, the LCM with behavioral heterogeneity can be quite useful. Estimation of the LCM amounts to mixing a cost minimization and a profit maximization model. Using the U.S. airlines data we find that after deregulation about 15% of the airlines are found to be consistent with profit maximizing behavior.

Hashtags and handshakes: consumer motives and platform use in brand-consumer interactions

Purpose – The current increase in social media activity related to brand–consumer interactions is progressively influencing the manner in which brands and their customers communicate. Whereas this attention to social media is warranted, researchers and brand managers must also recognize that consumers connect and engage with brands across other communication platforms as well. Accordingly, this study aims to examine brand–consumer interactions taking place across social, online and physical platforms, as well as consumer motives for initiating these brand interactions across various platforms. Design/methodology/approach – A mixed-method approach integrating quantitative and qualitative data was used. We administered a written diary to 102 individuals over a two-month period, in which study participants recorded their motivations and platform use in their interactions with a brand. We evaluated latent-class mixture models for complex data and multi-level latent-class mixture models to identify classes of interactions based on participants’ motivations and platform use as well as customer segments based on the identified motives-by-platform classes. Findings – The findings reveal ten categories of motives for interacting with brands, including promotions and incentives, timely information, product information, engagement, browsing, purchase, customer service, branded content, entertainment, and personalization/exclusivity. Furthermore, six motives-by-platform interaction classes are identified. The findings suggest three consumer segments differentiated by their motives-by-platform profiles. Research limitations/implications – This study adds to past research investigating the motives behind brand–consumer interactions in social media by investigating both social media and non-social media-related interactions, and offering a typology of interaction profiles that considers interaction motives and platform preferences. Practical implications – This study illustrates that consumers are driven to interact with brands based upon the ten motive categories. These motives, in turn, are associated with different platform uses. Thus, it is important for brands to adopt ambidexterity across multiple communication platforms. Originality/value – This research adds to the understanding of brand–consumer interactions conducted on online and offline communication platforms.

HIGH AND LOW THRESHOLD FOR STARTLE REACTIVITY ASSOCIATED WITH PTSD SYMPTOMS BUT NOT PTSD RISK: EVIDENCE FROM A PROSPECTIVE STUDY OF ACTIVE DUTY MARINES.

Heightened startle response is a symptom of PTSD, but evidence for exaggerated startle in PTSD is inconsistent. This prospective study aimed to clarify whether altered startle reactivity represents a trait risk-factor for developing PTSD or a marker of current PTSD symptoms. Marines and Navy Corpsmen were assessed before (n = 2,571) and after (n = 1,632) deployments to Iraq or Afghanistan with the Clinician-Administered PTSD Scale (CAPS). A predeployment startle-threshold task was completed with startle probes presented over 80-114 dB[A] levels. Latent class mixture modeling identified three growth classes of startle performance: "high," "low," and "moderate" threshold classes. Zero-inflated negative binomial regression was used to assess relationships between predeployment startle threshold and pre- and postdeployment psychiatric symptoms. At predeployment, the low-threshold class had higher PTSD symptom scores. Relative to the moderate-threshold class, low-threshold class membership was associated with decreased likelihood of being symptom-free at predeployment, based on CAPS, with particular associations with numbing and hyperarousal subscales, whereas high-threshold class membership was associated with more severe predeployment PTSD symptoms, in particular avoidance. Associations between low-threshold membership and CAPS symptoms were independent from measures of trauma burden, whereas associations between high-threshold membership and CAPS were not. Predeployment startle threshold did not predict postdeployment symptoms. This study found that both low startle threshold (heightened reactivity) and high startle threshold (blunted reactivity) were associated with greater current PTSD symptomatology, suggesting that startle reactivity is associated with current PTSD rather than a risk marker for developing PTSD.

A joint latent class model for classifying severely hemorrhaging trauma patients.

BackgroundIn trauma research, “massive transfusion” (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a “gold standard” for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to classify bleeding trauma patients.MethodsUsing the data from a retrospective trauma transfusion study, we applied a latent-class (LC) mixture model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain model parameters. Estimated posterior probabilities were used for patients’ classification and compared with the MT rule. To evaluate predictive performance of the LC-based classification, we examined the role of six clinical variables as predictors using two separate logistic regression models.ResultsOut of 471 trauma patients, 211 (45 %) were MT, while our latent SH classifier identified only 127 (27 %) of patients as SH. The agreement between the two classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol.ConclusionsThe traditional MT classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint latent class modeling could be used to correct for potential bias caused by misclassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event data from prospective studies.

An Indirect Comparison of Everolimus Versus Axitinib in US Patients With Advanced Renal Cell Carcinoma in Whom Prior Sunitinib Therapy Failed

PurposeThe purpose of this study was to perform a weight-adjusted indirect comparison to approximate the relative efficacy of everolimus versus axitinib among patients with second-line metastatic renal cell carcinoma in whom sunitinib therapy previously failed. MethodsIndividual patient data from the RECORD-1 (Renal Cell Cancer Treatment With Oral RAD001 Given Daily) Phase III clinical trial provided information for patients taking everolimus. Summary baseline clinical and demographic characteristics and progression-free survival (PFS) outcomes were available for patients taking axitinib who were included in the AXIS (axitinib versus sorafenib) Phase III clinical trial. A Bayesian latent class mixture model differentiating responders and nonresponders and with imbedded Weibull regression on PFS was used to identify sex, Memorial Sloan-Kettering Cancer Center risk score, and time receiving prior sunitinib therapy as prognostic factors for PFS based on posterior probability >95%. Patients taking everolimus were weighted up or down based on their combination of prognostic variables. Weights were calculated by dividing the proportion of patients observed in AXIS for a given characteristic by the proportion observed in RECORD-1 and taking the product of the values derived for all three weighting variables considered. Weighted PFS distributions were derived with bootstrapped 95% CIs and compared with those reported for the AXIS trial. FindingsAfter weighting, distributions of the 3 key baseline characteristics were more closely aligned between the 2 studies; however, some differences remained. A slightly lower rate of poor-risk patients was evident in RECORD-1 (30%) versus AXIS (36%), and a 9% lower proportion of males was observed in the everolimus group compared with the axitinib group. Distributions of time receiving prior sunitinib therapy were almost equivalent between the treatment arms. A median PFS of 4.7 months (95% CI, 3.5–10.6 months) was observed for patients in the weighted everolimus group compared with 4.8 months (95% CI, 4.5–6.4 months) in the AXIS trial. ImplicationsSimilar median PFS point estimates and overlapping CIs suggest that everolimus and axitinib have similar efficacy. Although these results do not negate the need for direct comparison, this study may be used to inform clinical and reimbursement decisions until such evidence is available.

Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test.

Ignoring the fact that the reference test used to establish the discriminative properties of a combination of diagnostic biomarkers is imperfect can lead to a biased estimate of the diagnostic accuracy of the combination. In this paper, we propose a Bayesian latent-class mixture model to select a combination of biomarkers that maximizes the area under the ROC curve (AUC), while taking into account the imperfect nature of the reference test. In particular, a method for specification of the prior for the mixture component parameters is developed that allows controlling the amount of prior information provided for the AUC. The properties of the model are evaluated by using a simulation study and an application to real data from Alzheimer's disease research. In the simulation study, 100 data sets are simulated for sample sizes ranging from 100 to 600 observations, with a varying correlation between biomarkers. The inclusion of an informative as well as a flat prior for the diagnostic accuracy of the reference test is investigated. In the real-data application, the proposed model was compared with the generally used logistic-regression model that ignores the imperfectness of the reference test. Conditional on the selected sample size and prior distributions, the simulation study results indicate satisfactory performance of the model-based estimates. In particular, the obtained average estimates for all parameters are close to the true values. For the real-data application, AUC estimates for the proposed model are substantially higher than those from the 'traditional' logistic-regression model.

Symptom-Hemodynamic Mismatch and Heart Failure Event Risk.

Heart failure (HF) is a heterogeneous condition of both symptoms and hemodynamics. The goals of this study were to identify distinct profiles among integrated data on physical and psychological symptoms and hemodynamics and quantify differences in 180-day event risk among observed profiles. A secondary analysis of data collected during 2 prospective cohort studies by a single group of investigators was performed. Latent class mixture modeling was used to identify distinct symptom-hemodynamic profiles. Cox proportional hazards modeling was used to quantify difference in event risk (HF emergency visit, hospitalization, or death) among profiles. The mean age (n = 291) was 57 ± 13 years, 38% were female, and 61% had class III/IV HF. Three distinct symptom-hemodynamic profiles were identified: 17.9% of patients had concordant symptoms and hemodynamics (ie, moderate physical and psychological symptoms matched the comparatively good hemodynamic profile), 17.9% had severe symptoms and average hemodynamics, and 64.2% had poor hemodynamics and mild symptoms. Compared with those in the concordant profile, both profiles of symptom-hemodynamic mismatch were associated with a markedly increased event risk (severe symptoms hazards ratio, 3.38; P = .033; poor hemodynamics hazards ratio, 3.48; P = .016). A minority of adults with HF have concordant symptoms and hemodynamics. Either profile of symptom-hemodynamic mismatch in HF is associated with a greater risk of healthcare utilization for HF or death.

Stability in Cognitive Function Over 18 Years: Prevalence and Predictors among Older Mexican Americans.

Numerous studies have examined the association of physical, behavioral and social factors with cognitive decline in older adults. Less attention has been placed on factors associated with long-term maintenance of intact cognition even into very old age. A greater understanding of those factors can inform the development of activities for maintaining cognitive strength. Using a sample from the Hispanic Established Populations for Epidemiologic Study of the Elderly, a population-based study of non-institutionalized Mexican Americans aged 65 and older from five Southwestern states (N = 2767), latent class mixture models were developed to identify subgroups of cognitive change over time. Three distinct trajectories of cognitive change were identified and characterized as stable, slow decline and rapid decline. Compared to the rapid decline group, a higher proportion of the stable cognition group were women, had high school education, were married and attended church one or more times per week. Regular church attendance had a significant positive impact in the stable group (β = 0.64, p <0.01), the slow decline group (β = 0.84, p <0.001) and the rapid decline group (β = 2.50, p <0.001). Activity limitations had a consistently negative association with cognition in the stable, slow decline and rapid decline groups (β = -0.37, p <0.001; β = -0.85, p <0.001; and β = -1.58, p <0.001 respectively). Substantial heterogeneity exists in rates of cognitive decline among older Mexican Americans. Interventions targeting cognitive maintenance may benefit from increased focus on factors associated with continued social engagement.

Comorbid substance use disorders in schizophrenia: A latent class approach

Schizophrenia is a complex psychiatric disorder with a characteristic disease course and heterogeneous etiology. While substance use disorders and a family history of psychosis have individually been identified as risk factors for schizophrenia, it is less well understood if and how these factors are related. To address this deficiency, we examined the relationship between substance use disorders and family history of psychosis in a sample of 1219 unrelated patients with schizophrenia. The lifetime rate of substance use disorders in this sample was 50%, and 30% had a family history of psychosis. Latent class mixture modeling identified three distinct patient subgroups: (1) individuals with low probability of substance use disorders; (2) patients with drug and alcohol abuse, but no symptoms of dependence; and (3) patients with substance dependence. Substance use was related to being male, to a more severe disease course, and more acute symptoms at assessment, but not to an earlier age of onset of schizophrenia or a specific pattern of positive and negative symptoms. Furthermore, substance use in schizophrenia was not related to a family history of psychosis. The results suggest that substance use in schizophrenia is an independent risk factor for disease severity and onset.

Physical and Psychological Symptom Profiling and Event-Free Survival in Adults With Moderate to Advanced Heart Failure

: Heart failure (HF) is a heterogeneous symptomatic disorder. The goal of this study was to identify and link common profiles of physical and psychological symptoms to 1-year event-free survival in adults with moderate to advanced HF. Multiple valid, reliable, and domain-specific measures were used to assess physical and psychological symptoms. Latent class mixture modeling was used to identify distinct symptom profiles. Associations between observed symptom profiles and 1-year event-free survival were quantified using Cox proportional hazards modeling. The mean age of the participants (n = 202) was 57 ± 13 years, 50% were men, and 60% had class III/IV HF. Three distinct profiles, mild (41.7%), moderate (30.2%), and severe (28.1%), that captured a gradient of both physical and psychological symptom burden were identified (P < .001 for all comparisons). Controlling for the Seattle HF Score, adults with the moderate symptom profile were 82% more likely (hazard ratio, 1.82; 95% confidence interval, 1.07-3.11; P = .028) and adults with the severe symptom profile were more than twice as likely (hazard ratio, 2.06; 95% confidence interval, 1.21-3.52; P = .001) to have a clinical event within 1 year than patients with the mild symptom profile. Profiling patterns among physical and psychological symptoms identifies HF patient subgroups with significantly worse 1-year event-free survival independent of prognostication based on objective clinical HF data.

Coxiella burnetii(Q-Fever) Seroprevalence in Prey and Predators in the United Kingdom: Evaluation of Infection in Wild Rodents, Foxes and Domestic Cats Using a Modified ELISA

Coxiella burnetii, the agent of Q-fever, is recognized as a worldwide zoonosis with a wide host range and potentially complex reservoir systems. Infected ruminants are the main source of infection for humans, but cats and other mammals, including wild rodents, also represent potential sources of infection. There has been a recent upsurge of reported cases in humans, domestic ruminants and wildlife in many parts of the world, and studies have indicated that wild brown rats may act as true reservoirs for C.burnetii and be implicated in outbreaks in livestock and humans. However, investigation of reservoir systems is limited by lack of validated serological tests for wildlife or other non-target species. In this study, serum samples from 796 wild rodents (180 bank voles, 309 field voles, 307 wood mice) 102 wild foxes and 26 domestic cats from three study areas in the UK were tested for the presence of antibodies to C.burnetii using a commercial indirect ELISA kit modified for use in multiple wildlife species. Test thresholds were determined for each species in the absence of species-specific reference sera using a bi-modal latent class mixture model to discriminate between positive from negative results. Based on the thresholds determined, seroprevalence in the wild rodents ranged from 15.6% to 19.1% depending on species (overall 17.3%) and was significantly higher in both foxes (41.2%) and cats (61.5%) than in rodents. This is the first report to quantify seroprevalence to C.burnetii in bank voles, field voles, wood mice, foxes and cats in the UK and provides evidence that predator species could act as indicators for the presence of C.burnetii in rodents. The study demonstrates that wildlife species could be significant reservoirs of infection for both livestock and humans, and the high seroprevalence in domestic cats highlights the potential zoonotic risk from this species.

Depressive Symptom Trajectories and Associated Risks among Korean Elderly

Background Recent studies conducting changes in depressive symptoms among the elderly reported mixed results. The present study sought to determine if subgroups of elderly Koreans follow distinctive depressive symptom trajectories and the characteristics associated with the depressive symptom trajectories. METHODS Subjects were those who had participated in a longitudinal study of quality of life in older adults. A latent class mixture model was examined to identify the trajectories of depressive symptom changes with time. RESULTS We found four depressive symptom trajectories. Poorer health status, poor economic status, and less social support were risk factors in the high depression group. CONCLUSIONS Early intervention to help elderly individuals manage their health, economic concerns, and social relationships may decrease the risk of high level depression. KEYWORDS: Elderly, Depression, Demographic factor. Language: ko