In this paper, we extend the linear M-quantile random intercept model (MQRE) to discrete data and use the proposed model to evaluate the effect of selected covariates on two count responses: the number of generic medical examinations and the number of specialised examinations for health districts in three regions of central Italy. The new approach represents an outlier-robust alternative to the generalised linear mixed model with Gaussian random effects and it allows estimating the effect of the covariates at various quantiles of the conditional distribution of the target variable. Results from a simulation experiment, as well as from real data, confirm that the method proposed here presents good robustness properties and can be in certain cases more efficient than other approaches.
View Article and Find Full Text PDFJ Epidemiol Community Health
November 2014
Background: Community-based rehabilitation (CBR) programmes have been described as highly effective means of promoting the rights and opportunities of persons with disabilities (PwD). Although CBR is often the main way in which PwD in low-income and middle-income countries access rehabilitation services, there is little literature providing rigorous evaluation of their impact on people's well-being.
Methods: Data were collected in the Mandya and Ramanagar districts (Karnataka state, India), between December 2009 and May 2010.
Purpose: In this paper, we measure the effectiveness of Community-Based Rehabilitation (CBR) programmes in promoting the well-being of people with disabilities and removing the barriers to their participation in family and community decision-making processes.
Method: To evaluate the impact of the CBR programme, we use data from a large-scale control study in Karnataka, India. Propensity score matching is used to evaluate the impacts on disabled persons after 2, 4 and 7 years of entering the CBR.