Distinguishing phenotypes of childhood wheeze and cough using latent class analysis

Airway disease in childhood comprises a heterogeneous group of disorders. Attempts to distinguish different phenotypes have generally considered few disease dimensions. The present study examines phenotypes of childhood wheeze and chronic cough, by fitting a statistical model to data representing multiple disease dimensions. From a population-based, longitudinal cohort study of 1,650 preschool children, 319 with parent-reported wheeze or chronic cough were included. Phenotypes were identified by latent class analysis using data on symptoms, skin-prick tests, lung function and airway responsiveness from two preschool surveys. These phenotypes were then compared with respect to outcome at school age. The model distinguished three phenotypes of wheeze and two phenotypes of chronic cough. Subsequent wheeze, chronic cough and inhaler use at school age differed clearly between the five phenotypes. The wheeze phenotypes shared features with previously described entities and partly reconciled discrepancies between existing sets of phenotype labels. This novel, multidimensional approach has the potential to identify clinically relevant phenotypes, not only in paediatric disorders but also in adult obstructive airway diseases, where phenotype definition is an equally important issue.

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

Novel screening tools for latent tuberculosis: time to leave an old friend?

PURPOSE OF REVIEW: Therapeutic inhibition of tumour necrosis factor-alpha strongly increases the risk of reactivation in latent tuberculosis infection. Recent blood tests based on antigen-specific T cell response and measuring production of interferon-gamma, so called interferon-gamma release assays (IGRAs), are promising novel tools to identify infected patients. The performance of diagnostic testing for latent tuberculosis infection in patients with rheumatic diseases will be discussed. RECENT FINDINGS: In patients with rheumatoid arthritis, IGRAs are more sensitive and more specific than traditional tuberculin skin testing. They are unaffected by Bacillus-Calmette-Guérin vaccination and most nontuberculous mycobacteria. Most comparative studies show a better performance of the IGRAs than tuberculin skin testing in terms of a higher specificity. The rate of indeterminate results may be affected by glucocorticoids and the underlying disease but appears independent of disease-modifying antirheumatic drugs. Despite using identical Mycobacterium tuberculosis antigens, the two commercially available tests show differences in clinical performance. SUMMARY: The current information about the performance of the tuberculin skin testing and the IGRAs in the detection of latent tuberculosis infection in patients with rheumatic diseases strongly suggest a clinically relevant advantage of the IGRAs. Their use will help to reduce overuse and underuse of preventive treatment in tumour necrosis factor inhibition.

Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

Tracks of immigrant political incorporation

Models of Immigrant Political Incorporation brings together a multidisciplinary group of scholars to consider pathways by which immigrants may be incorporated into the political processes of western democracies. It builds on a rich tradition of studying immigrant incorporation, but each chapter innovates by moving beyond singular accounts of particular groups and locations toward a general causal model with the scope and breadth to apply across groups, places, and time. Models of Immigrant Political Incorporation addresses three key analytic questions: what, if anything, are the distinctive features of immigrants or immigrant groups? How broadly should one define and study politics? What are the initial premises for analyzing pathways toward incorporation; does one learn more by starting from an assumption of racialization and exclusion or from an assumption of engagement and inclusion? While all models engage with all three key analytic questions, chapters vary in their relative focus on one or another, and in the answers they provide. Most include graphical illustrations of the model, as well as extended examples applying the model to one or more immigrant populations. At a time when research on immigrant political incorporation is rapidly accumulating - and when immigrants are increasingly significant political actors in many democratic polities — this volume makes a timely and valuable intervention by pushing researchers to articulate causal dynamics, provide clear definitions and measurable concepts, and develop testable hypotheses. Furthermore, the wide array of frameworks examining how immigrants become part of a polity or are shunted aside ensure that activists and analysts alike will find useful insights. By including historians, sociologists, and political scientists, by ranging across North America and Western Europe, by addressing successful and failed incorporative efforts, this handbook offers guides for anyone seeking to develop a dynamic, unified, and supple model of immigrant political incorporation.

Social Meets Molecular: Combining Phylogenetic and Latent Class Analyses to Understand HIV-1 Transmission in Switzerland

Switzerland has a complex human immunodeficiency virus (HIV) epidemic involving several populations. We examined transmission of HIV type 1 (HIV-1) in a national cohort study. Latent class analysis was used to identify socioeconomic and behavioral groups among 6,027 patients enrolled in the Swiss HIV Cohort Study between 2000 and 2011. Phylogenetic analysis of sequence data, available for 4,013 patients, was used to identify transmission clusters. Concordance between sociobehavioral groups and transmission clusters was assessed in correlation and multiple correspondence analyses. A total of 2,696 patients were infected with subtype B, 203 with subtype C, 196 with subtype A, and 733 with recombinant subtypes (mainly CRF02_AG and CRF01_AE). Latent class analysis identified 8 patient groups. Most transmission clusters of subtype B were shared between groups of gay men (groups 1-3) or between the heterosexual groups "heterosexual people of lower socioeconomic position" (group 4) and "injection drug users" (group 8). Clusters linking homosexual and heterosexual groups were associated with "older heterosexual and gay people on welfare" (group 5). "Migrant women in heterosexual partnerships" (group 6) and "heterosexual migrants on welfare" (group 7) shared non-B clusters with groups 4 and 5. Combining approaches from social and molecular epidemiology can provide insights into HIV-1 transmission and inform the design of prevention strategies.