Andy H. Lee

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which render the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation.

Determinants of maternity length of stay: a Gamma mixture risk-adjusted model.

With obstetrical delivery being the most frequent cause for hospital admissions, it is important to determine health- and patient-related characteristics affecting maternity length of stay (LOS). Although the average inpatient LOS has decreased steadily over the years, the issue of the appropriate LOS after delivery is complex and hotly debated, especially since the introduction of the mandatory minimum-stay legislation in the USA. The purpose of this paper is to identity factors associated with maternity LOS and to model variations in LOS. A Gamma mixture risk-adjusted model is proposed in order to analyze heterogeneity of maternity LOS within obstetrical Diagnosis Related Groups (DRGs). The determination of pertinent factors would benefit hospital administrators and clinicians to manage LOS and expenditures efficiently.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Two-component Poisson mixture regression modelling of count data with bivariate random effects

Two-component Poisson mixture regression is typically used to model heterogeneous count outcomes that arise from two underlying sub-populations. Furthermore, a random component can be incorporated into the linear predictor to account for the clustering data structure. However, when including random effects in both components of the mixture model, the two random effects are often assumed to be independent for simplicity. A two-component Poisson mixture regression model with bivariate random effects is proposed to deal with the correlated situation. A restricted maximum quasi-likelihood estimation procedure is provided to obtain the parameter estimates of the model. A simulation study shows both fixed effects and variance component estimates perform well under different conditions. An application to childhood gastroenteritis data demonstrates the usefulness of the proposed methodology, and suggests that neglecting the inherent correlation between random effects may lead to incorrect inferences concerning the count outcomes.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accomodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration.

Public versus private hospital maternity length of stay: a gamma mixture modelling approach:

Application of a gamma mixture model to obstetrical diagnosis-related groups (DRGs) revealed heterogeneity of maternity length of stay (LOS). The proportion of long-stay subgroups identified, which can account for 30% of admissions, varied between DRGs. The burden of long-stay patients borne was estimated to be much higher in private hospitals than public hospitals for normal delivery, but vice versa for Caesarean section. Such differences highlights the impact of DRG-based casemix funding on inpatient LOS and have significant implications for health insurance companies to integrate casemix funding across the public and private sectors. The analysis also benefits hospital administrators and managers to budget expenditures accordingly.

Educational technology in hospitality management programs: adoption and expectations.

ABSTRACT Educational technology has been widely adopted to support students’ learning. However, existing literature provides fractional pictures of the current adoption and expectations of educational technology in hospitality programs in higher education. This study examines the perspectives of hospitality management students on educational technology adoption, its advantages, their concerns and expectations regarding the integration of educational technology. The findings reveal flexibility as the main advantage of educational technology, while the need for stronger self-motivation is the main concern. Students expect more adoption of multimedia in education, while the current adoption of social software suffices their expectation. Also, different technology adoption is recommended to satisfy lower and upper classmen respectively.

Reflective Practice in Food and Beverage Education

Food and beverage (F&B) management education is essential to hospitality, and arguably tourism and event, management students. Higher educators are challenged in resourcing various approaches. As many students have experiences as F&B workers and/or consumers, reflective assessment leveraging these experiences may be an effective learning tool. Using student reflective journals from an Australian institution’s undergraduate F&B management cohort, this article reports the process, effectiveness, and challenges associated with reflective learning. Using Nvivo®, analysis identified three key themes: students demonstrated comprehension for a theoretical topic by effectively interpreting a past experience; they applied this greater level of theoretical comprehension to further evaluate that past experience and challenge assumptions; and they leveraged increased theoretical comprehension and the application and evaluative processes of past experiences to effect reflective thinking. Findings suggest students from Confucian heritage backgrounds require additional tutelage in attempting reflective tasks. Implications for educators are discussed.

Tourists’ emotional wellness and hotel room colour

In response to the growing wellness trend, the tourism and hospitality industry has offered various wellness services and facilities. Despite the significance of emotion in wellness, research on emotional wellness is surprisingly scant. The present empirical study examined the underlying dimensions of emotional wellness and the influence of guest room colour on emotional wellness via hypotheses testing. Results indicated that calmness is the most dominant dimension of emotional wellness. Results also suggest that a cool colour-themed guest room, particularly green, is preferable. Theoretical and managerial implications are discussed, and future research suggestions are provided.