Deciding on the Starting Number of Classes of a Latent Class Tree

A National Study of Healthcare Service Patterns at the End of Life Among Children With Cardiac Disease

BACKGROUND

Heart diseases are one of the leading causes of health-related deaths among children. Concurrent hospice care offers hospice and nonhospice healthcare services simultaneously, but the use of these services by children with cardiac disease has been rarely investigated.

OBJECTIVE

The aims of this study were to identify patterns of nonhospice healthcare services used in concurrent hospice care and describe the profile of children with cardiac disease in these clusters.

METHODS

This study was a retrospective cohort analysis of Medicaid claims data collected between 2011 and 2013 from 1635 pediatric cardiac patients. The analysis included descriptive statistics and latent class analysis.

RESULTS

Children in the sample used more than 314 000 nonhospice healthcare services. The most common services were inpatient hospital procedures, durable medical equipment, and home health. Latent class analysis clustered children into "moderate intensity" (60.0%) and "high intensity" classes (40.0%). Children in "moderate intensity" had dysrhythmias (31.7%), comorbidities (85.0%), mental/behavioral health conditions (55%), and technology dependence (71%). They commonly resided in urban areas (60.1%) in the Northeast (44.4%). The health profile of children in the "high intensity" class included dysrhythmias (39.4%), comorbidities (97.6%), mental/behavioral health conditions (71.5%), and technology dependence (85.8%). These children resided in rural communities (50.7%) in the South (53.1%).

CONCLUSIONS

Two patterns of use of nonhospice healthcare services were identified in this study. This information may be used by nurses and other healthcare professionals working in concurrent hospice care to assess the healthcare service needs of children with cardiac conditions at the end of life.

Do peers share the same criteria for assessing grant applications?

This study examines a basic assumption of peer review, namely, the idea that there is a consensus on evaluation criteria among peers, which is a necessary condition for the reliability of peer judgements. Empirical evidence indicating that there is no consensus or more than one consensus would offer an explanation for the disagreement effect, the low inter-rater reliability consistently observed in peer review. To investigate this basic assumption, we have surveyed all humanities scholars in Switzerland on 23 grant review criteria. We have employed latent class tree modelling to identify subgroups in which scholars rated criteria similarly (i.e. latent classes) and to explore covariates predicting class membership. We have identified two consensus classes, two consensus-close classes, and a consensus-far class. The consensus classes contain a core consensus (10 criteria related to knowledge gaps; feasibility; rigour; comprehensibility and argumentation; academic relevance; competence and experience of the applicant) and a broad consensus that include the core consensus plus eight contribution-related criteria, such as originality. These results provide a possible explanation for the disagreement effect. Moreover, the results are consistent with the notion of conservatism, which holds that original research is undervalued in peer review, while other aspects, such as methodology and feasibility, are overweighted. The covariate analysis indicated that age and having tenure increase from the consensus far to the consensus close to the consensus classes. This suggests that the more academic experience scholars accumulate, the more their understanding of review criteria conforms to the social norm.

Pharmacists opioid risk and safety counseling practices: A latent class analysis approach

BACKGROUND

The opioid crisis is a global public health issue, especially present in the United States. Limited research addresses pharmacists' opioid medication counseling practices particularly their risk and safety counseling practices.

OBJECTIVES

The objective of this paper is to categorize pharmacists based on their opioid risk and safety counseling practices to inform future interventions and research to improve practice. The percent of pharmacists falling into each of these underlying, unobservable subgroups is identified using latent class analysis.

METHODS

This study was conducted as a statewide survey of pharmacists using the modified Dilman technique. The survey consisted of ten items about pharmacists' opioid risk and safety practices when dispensing an opioid medication. Descriptive statistics were conducted followed by latent class analysis. This approach categorized pharmacists based on their responses to the survey items.

RESULTS

Responses from 216 pharmacists were used in this study. In the three-class model which was deemed the best fit, the first class shows a profile of pharmacists who counsel on almost all opioid risk and safety topics and composed 16.75% of the total respondent population. The second class shows a profile of pharmacists who hardly counsel on any opioid risks and safety topics and comprised 39.80% of the respondent population. The third class shows a profile of pharmacists counseling on opioid risk and safety topics mostly for new or long-term prescriptions, but not for refill or short-term prescriptions. This group constituted 43.45% of the respondent population.

CONCLUSION

This study identifies distinct classes of pharmacists in terms of the frequency with which their opioid counseling does or does not include key elements of risk and safety topics. A small minority usually include the risk and safety issues. Training and resource interventions targeting pharmacists who do not counsel patients about opioid risks are important to help them become more comfortable and adept as opioid risk and safety educators.

Latent class analysis for exploring distribution patterns of primary superficial venous insufficiency

BACKGROUND

This study aims to identify specific segmental distribution patterns of lower extremity chronic venous disease based on latent class analysis of Doppler mapping results.

METHODS

A total of 1,871 lower extremities of 1,218 treatment-naïve patients (536 males, 682 females; mean age 45.4 years; range, 21 to 87 years) with chronic venous disease referred for Doppler examination between September 2009 and August 2018 were included. Refluxing superficial venous segments of the lower extremities were mapped and recorded in database in 10 distinct anatomic locations as follows: saphenofemoral junction and proximal greater saphenous vein, mid and distal thigh greater saphenous vein, anterior and posterior accessory saphenous veins, proximal and distal calf greater saphenous vein, saphenopopliteal junction and proximal lesser saphenous vein, distal lesser saphenous vein, and intersaphenous veins including Giacomini"s vein. Repeated examinations were excluded. The latent class analysis was applied to identify any possible anatomic distribution patterns of chronic venous disease.

RESULTS

Bayesian information criteria revealed three latent class models fit for refluxing segment distribution as follows: 58.2% (n=1,089) were above-the-knee greater saphenous vein segments including saphenofemoral junction (pattern 1); 29.3% (n=548) were below-theknee greater saphenous vein segments (pattern 2); and 12.5% (n=234) were lesser saphenous vein segments and intersaphenous veins including Giacomini"s vein (pattern 3). There was no age- or sex-specific differences in the chronic venous disease distribution patterns.

CONCLUSION

The latent class analysis, by identifying previously unseen subgroups within the sampled population, provides a new approach to classification of reflux patterns in chronic venous disease. Identification of latent classes may provide understanding of different pathophysiological bases of venous reflux and more optimal planning for interventions.

Health-Related Quality of Life Among Young Children With Cochlear Implants and Developmental Disabilities

Supplemental Digital Content is available in the text.

Objectives:

We developed a framework for objectively comparing hearing aids, independent of brand, type, or product family. This was done using a large dataset of commercially available hearing aids. To achieve this, we investigated which hearing aid features are suitable for comparison, and are also relevant for the rehabilitation of hearing impairment. To compare hearing aids objectively, we distinguished populations of hearing aids based on a set of key hearing aid features. Finally, we describe these hearing aid subpopulations so that these could potentially be used as a supporting tool for the selection of an appropriate hearing aid.

Design:

In this study, we used technical (meta-)data from 3911 hearing aids (available on the Dutch market in March 2018). The dataset contained about 50 of the most important characteristics of a hearing aid. After cleaning and handling the data via a well-defined knowledge discovery in database procedure, a total 3083 hearing aids were included. Subsequently, a set of well-defined key hearing aid features were used as input for further analysis. The data were split into an in-the-ear style hearing aid subset and a behind-the-ear style subset, for separate analyses. The knowledge discovery in databases procedure was also used as an objective guiding tool for applying an exploratory cluster analysis to expose subpopulations of hearing aids within the dataset. The latter was done using Latent Class Tree Analysis, which is an extension to the better-known Latent Class Analysis clustering method: with the important addition of a hierarchical structure.

Results:

A total of 10 hearing aid features were identified as relevant for audiological rehabilitation: compression, sound processing, noise reduction (NR), expansion, wind NR, impulse (noise) reduction, active feedback management, directionality, NR environments, and ear-to-ear communication. These features had the greatest impact on results yielded by the Latent Class Tree cluster analysis. At the first level in the hierarchical cluster model, the two subpopulations of hearing aids could be divided into 3 main branches, mainly distinguishable by the overall availability or technology level of hearing aid features. Higher-level results of the cluster analysis yielded a set of mutually exclusive hearing aid populations, called modalities. In total, nine behind-the-ear and seven in-the-ear modalities were found. These modalities were characterized by particular profiles of (complex) interplay between the selected key features. A technical comparison of features (e.g., implementation) is beyond the scope of this research.

Conclusions:

Combining a large dataset of hearing aids with a probabilistic hierarchical clustering method enables analysis of hearing aid characteristics which extends beyond product families and manufacturers. Furthermore, this study found that the resulting hearing aid modalities can be thought of as a generic alternative to the manufacturer-dependent proprietary “concepts,” and could potentially aid the selection of an appropriate hearing aid for technical rehabilitation. This study is in line with a growing need for justification of hearing aid selection and the increasing demand for evidence-based practice.