Narrating Sustainability in Canadian Health Care Reform Discourse

In Canada, as in other industrialized welfare states, definitions of health system sustainability reflect different goals and correspondingly diverse ways of understanding exactly what is to be sustained and how. In this article, we report on results from a survey of documents and groups involved in and/or concerned about sustainability of the healthcare system. We identify four broad narratives of sustainability in Canadian healthcare reform discourse, and explore what these different sustainability narratives tell us about the character and contours of these often‐fractious debates. We argue that sustainability itself may not be a paradigm shift, as has been suggested, but a plot device that helps to steer the story in a particular direction, ‘black‐boxing’ certain aspects of context and emphasizing others.     

Communication in Statistics-Theory and methods improved GQL estimation method for the generalised BINMA(1) model

In a recent research, the quasi-likelihood estimation methodology was developed to estimate the regression effects in the Generalized BINMA(1) (GBINMA(1)) process. The method provides consistent parameter estimates but, in the intermediate computations, moment estimating equations were used to estimate the serial- and cross-correlation parameters. This procedure may not result optimal parameter estimates, in particular, for the regression effects. This paper provides an alternative simpler GBINMA(1) process based on multivariate thinning properties where the main effects are estimated via a robust generalized quasi-likelihood (GQL) estimation approach. The two techniques are compared through some simulation experiments. A real-life data application is studied.     

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.     

A BINAR(1) time-series model with cross-correlated COM–Poisson innovations

This article proposes a bivariate integer-valued autoregressive time-series model of order 1 (BINAR(1) with COM–Poisson marginals to analyze a pair of non stationary time series of counts. The interrelation between the series is induced by the correlated innovations, while the non stationarity is captured through a common set of time-dependent covariates that influence the count responses. The regression and dependence effects are estimated using generalized quasi-likelihood (GQL) approach. Simulation experiments are performed to assess the performance of the estimation algorithms. The proposed BINAR(1) process is applied to analyze a real-life series of day and night accidents in Mauritius.     

A GQL estimation approach for analysing non-stationary over-dispersed BINAR(1) time series

This paper proposes a generalized quasi-likelihood (GQL) function for estimating the vector of regression and over-dispersion effects for the respective series in the bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with Negative Binomial (NB) marginals. The auto-covariance function in the proposed GQL is computed using some ‘robust’ working structures. As for the BINAR(1) process, the inter-relation between the series is induced mainly by the correlated NB innovations that are subject to different levels of over-dispersion. The performance of the GQL approach is tested via some Monte-Carlo simulations under different combination of over-dispersion together with low and high serial- and cross-correlation parameters. The model is also applied to analyse a real-life series of day and night accidents in Mauritius.     

Estimating the parameters of a BINMA Poisson model for a non-stationary bivariate time series

This article proposes a novel non-stationary BINMA time series model by extending two INMA processes where their innovation series follow the bivariate Poisson under time-varying moment assumptions. This article also demonstrates, through simulation studies, the use and superiority of the generalized quasi-likelihood (GQL) approach to estimate the regression effects, which is computationally less complicated as compared to conditional maximum likelihood estimation (CMLE) and the feasible generalized least squares (FGLS). The serial and bivariate dependence correlations are estimated by a robust method of moments.     

Placement of children in out-of-home care in Québec,Canada: When and for whom initial out-of-home placement is most likely to occur

This study contributes to the growing child protection placement literature by providing the first Canadian provincial longitudinal study examining when and for whom initial out-of-home placement is most likely to occur. Anonymized clinical-administrative child protection data were merged with the 2006 Canadian Census data for the province of Québec, and the final dataset included 127,181 children investigated for maltreatment for the first time between April 1, 2002 and March 31, 2010. Cox proportional hazard results indicate that the vast majority of investigated children do not experience a placement, but for the others, placement tends to occur immediately following the maltreatment investigation with only a slight increase in risk over time. The increased risk of placement for younger children aged 0 to 9 years was statistically explained by a combination of male gender, behavioral problems, parents' high risk lifestyles, hospital referral, the number of investigations and neighborhood area socioeconomic disadvantages. The increased risk of placement for older children aged 10 to 17 years was statistically explained by a combination of behavioral problems, police reporting, the number of investigations and neighborhood area socioeconomic disadvantages. Neighborhood area socioeconomic disadvantages significantly contributed to the increased risk of out-of-home placement for all children, but this factor is most influential when it comes to younger children.     

Comparing Joint GQL Estimation and GMM Adaptive Estimation in COM-Poisson Longitudinal Regression Model

It is of scientific interest to study the application of COM-Poisson model to the case of longitudinal response data, the analysis of which is quite challenging due to the fact that longitudinal responses of a subject are correlated and the correlation pattern is usually unknown. In this article, we extend the COM-Poisson GLM to the generalized linear longitudinal model. We also develop a joint generalized quasi-likelihood estimating equation approach based on a stationary autocorrelation structure for the repeated count data. We further compare the performance of this estimation method with that of Generalized Method of Moments through a simulation study.     

Substantiating neglect of first nations and non-aboriginal children

First Nations (Native American) children are greatly overrepresented in the Canadian child welfare system, and disproportionality in the substantiation of maltreatment contributes to this overrepresentation. This study explores the factors driving disproportionality in the substantiation of maltreatment and, more specifically, neglect. Data from the Canadian Incidence Study of Reported Child Abuse and Neglect (2008) are used in multivariate analyses which examine the relationship between the substantiation of maltreatment/neglect and worker assessments of case, child, household, and caregiver characteristics. These case factors fully explain disproportionality in maltreatment substantiation for First Nations and non-Aboriginal children; the disproportionality reflects underlying differences in the case, child, household and caregiver characteristics identified in First Nations and non-Aboriginal investigations. However, case factors do not fully explain disproportionality in substantiation of neglect-only investigations. Further analysis indicates that the weight that workers assigned to caregiver substance abuse, housing problems, and presence of a lone caregiver when substantiating neglect also differed for First Nations and non-Aboriginal children. Discussion of these findings explores possible explanations for these differences, and links to broader discussions around definitions of neglect and the role of substantiation in child welfare decision making processes.     

A case study of MCB and SBMH stock transaction using a novel BINMA(1) with non-stationary NB correlated innovations

This paper focuses on the modeling of the intra-day transactions at the Stock Exchange Mauritius (SEM) of the two major banking companies: Mauritius Commercial Bank Group Limited (MCB) and State Bank of Mauritius Holdings Ltd (SBMH) in Mauritius using a flexible non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) process with negative binomial (NB) innovations that may cater for different levels of over-dispersion. The generalized quasi-likelihood (GQL) approach is used to estimate the regression, dependence and over-dispersion effects. However, for the over-dispersion parameters, the auto-covariance structure in the GQL is constructed using some higher order moments. This new model is tested over some Monte-Carlo experiments and is applied to analyze the inter-related intra-day series of volume of stocks for the two banking institutions using data collected from 3 August to 16 October 2015 in the presence of some time-varying covariates such as the news effect, Friday effect and time of the day effect.