Population Forecasts for Bangladesh, Using a Bayesian Methodology

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible

Geographically Dependent Individual-level Models for Infectious Disease Transmission

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems, however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically-dependent ILMs (GD-ILMs), to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models are investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data (2009). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo (MCMC) methods. We also developed the continuous-time GD-ILMs, allowing infection times and infectious periods to be treated as latent variables that are estimated using data-augmented Markov Chain Monte Carlo (MCMC) techniques within a Bayesian framework. This approach results in a flexible infectious disease modeling framework for formulating etiological hypotheses and identifying unusually high-risk geographical regions to develop preventive action. We evaluate the performance of these proposed models on a combination of simulated epidemic data and seasonal influenza data in Alberta in 2009. Finally, we proposed a special case of the GD-ILMs, termed as {\it small-area restricted} GD-ILMs for infectious disease modelling. The reliability of these models are investigated through simulation studies based on disease spread through the Canadian city of Calgary, Alberta

Population Forecasts for Bangladesh, Using a Bayesian Methodology

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible

Impact of Oncology Drug Review Times on Public Funding Recommendations

New oncology drugs undergo detailed review prior to public funding in a single-payer healthcare system. The aim of this study was to assess how cancer drug review times impact funding recommendations. Drugs reviewed by the pan-Canadian Oncology Drug Review (pCODR) between the years 2012 and 2020 were included. Data were collected including Health Canada approval dates, initial and final funding recommendations, treatment intent, drug class, clinical indications, and incremental cost-effectiveness ratios (ICER). Univariable and multivariable analyses were used to determine the association between funding recommendations and review times. Of the 164 applications submitted, 130 received a positive final recommendation. Median time from Health Canada (HC) approval to final recommendation was longer for drugs indicated for the treatment of gastrointestinal (GI) and lung cancer compared to breast, genitourinary (GU), and other tumours (205 vs. 198 vs. 111 vs. 129 vs. 181 days, respectively; Kruskal–Wallis p = 0.0312). Drugs with longer review times were more likely to receive a negative pCODR recommendation, even when adjusting for tumour type, drug class, and intent of therapy (157 vs. 298 days; Wilcoxon p = 0.0003, OR 1.002 95% CI [1.000–1.004].). There was no association between funding recommendation and tumour type or class of drug. The exploration of factors associated with variance in review times will be important in ensuring timely patient access to cancer drugs