Syed Shahadat Hossain

Quantitative assessment of medical waste generation in the capital city of Bangladesh

There is a concern that mismanagement of medical waste in developing countries may be a significant risk factor for disease transmission. Quantitative estimation of medical waste generation is needed to estimate the potential risk and as a basis for any waste management plan. Dhaka City, the capital of Bangladesh, is an example of a major city in a developing country where there has been no rigorous estimation of medical waste generation based upon a thorough scientific study. These estimates were obtained by stringent weighing of waste in a carefully chosen, representative, sample of HCEs, including non-residential diagnostic centres. This study used a statistically designed sampling of waste generation in a broad range of Health Care Establishments (HCEs) to indicate that the amount of waste produced in Dhaka can be estimated to be 37+/-5 ton per day. The proportion of this waste that would be classified as hazardous waste by World Health Organisation (WHO) guidelines was found to be approximately 21%. The amount of waste, and the proportion of hazardous waste, was found to vary significantly with the size and type of HCE.

Predicting arsenic concentration in groundwater of Bangladesh using Bayesian geostatistical model

The pattern of the spatial variation in arsenic concentration in groundwater of Bangladesh is usually needed for the planning of safe drinking water. Often a model-based prediction is required for this purpose. In this paper, we fit a Bayesian hierarchical geostatistical model by utilizing data from the project, ‘Groundwater studies of arsenic concentration in Bangladesh’ conducted by the British Geological Survey and the Department of Public Health Engineering of Bangladesh. We also develop a predictive model for arsenic concentration at different levels of well-depth using the same approach. The resulting predictive model has been cross-validated by appropriate statistical tools. Finally, we obtained reliable spatially continuous predictive maps and predictive probability maps showing the areas with high probability of arsenic concentration for different levels of well-depth. Results indicate that our model fits the data well and captures a substantial amount of spatial variation. Moreover, well-depth is found to have a significant contribution in explaining the observed variation in arsenic concentration. The predictive maps that have been produced are observed to be different for various levels of well-depths and are expected to be helpful to the policy makers in preparing proper regional planning for safe drinking water.

Non‐Parametric Selected Ranked Set Sampling

A nonparametric selected ranked set sampling is suggested. The estimator of population mean based on the new approach is compared with that using the simple random sampling (SRS), the ranked set sampling (RSS) and the median ranked set sampling (MRSS) methods. The estimator of population mean using the new approach is found to be more efficient than its counter-parts for almost all the cases considered.

Determinants of education quality: what makes students’ perception different?

ABSTRACT In recent decades, the commercialization of education has become more apparent and the need for using marketing tools is greater than before. This study aims to identify the demographic and background information of students that differentiate their perception about quality of higher education. A sample of 432 students was taken from five top private universities of Bangladesh to evaluate their perception toward dimensions of higher education. Multinomial regression analysis was conducted to identify the characteristics of students which make their perception about quality of higher education dissimilar. The findings show that status of students for scholarship, extracurricular activities, parents’ education, age, previous result, and university they study in have a significant influence on perception about quality of higher education. Part-time job status has moderate influence on the students’ perception. This research carries value to education policy-makers and university authorities. They can use these findings to formulate regulations, and target specific groups of students to ensure favorable academic environment and increase the brand image of their institutions.

Maximum likelihood estimation of variance components of heteroscedastic random anova model

the estimation of variance components of heteroscedastic random model is discussed in this paper. Maximum Likelihood (ML) is described for one-way heteroscedastic random models. The proportionality condition that cell variance is proportional to the cell sample size, is used to eliminate the efffect of heteroscedasticity. The algebraic expressions of the estimators are obtained for the model. It is seen that the algebraic expressions of the estimators depend mainly on the inverse of the variance-covariance matrix of the observation vector. So, the variance-covariance matrix is obtained and the formulae for the inversions are given. A Monte Carlo study is conducted. Five different variance patterns with different numbers of cells are considered in this study. For each variance pattern, 1000 Monte Carlo samples are drawn. Then the Monte Carlo biases and Monte Carlo MSE’s of the estimators of variance components are calculated. In respect of both bias and MSE, the Maximum Likelihood (ML) estimators of varian...

Efficient estimation in two-stage randomized clinical trials using ranked sets.

ABSTRACT Clinical trials designed for survival probability estimation of different treatment policies for chronic diseases like cancer, leukemia, and schizophrenia usually need randomization of treatments in two stages. Since complete remission is rare for these diseases, initially an induction therapy is given for patient’s remission. Further treatment, which is often an expensive maintenance therapy, is administered only for the patients with remission. If the maintenance therapy is so expensive that the cost of the trial inflates, only a simple random sample of patients will be treated with the expensive maintenance due to budget constraint. In this article, we have implemented a design using ranked sets instead of simple randomization in the second stage and obtained an unbiased estimator of the overall survival distribution for a particular treatment combination. Through simulation studies under different conditions, we have found that the design we developed based on ranked sets gives an unbiased estimate of the population survival probability which is more efficient than the estimate obtained by the usual design.

Migration pattern in Bangladesh: a covariate-dependent Markov model

Internal migration is one of the major components of rapid and unplanned growth of towns and cities especially in the developing countries. This paper describes the transition pattern of internal out migration in Bangladesh and some sociodemographic factors influencing such migration in the country using a covariate-dependent Markov model. Four types of migration behavior namely, rural to rural, rural to urban, urban to rural and urban to urban are under consideration of this paper. Defining two discrete states, urban and rural, each of such transition can be characterized by a stochastic process; hence we use a two-state Markov chain for this purpose. We find that age, sex, division and reason of migration are significantly associated with internal migration in Bangladesh. The major findings include that any type of migration, rural to rural, rural to urban, urban to rural and urban to urban, mostly take place at the ages of 15–30 as well as at the ages of 0–15; females have higher odds than males to make a migration; Dhaka, Rajshahi and Chittagong divisions have remarkably higher migration rate as compared to Barisal and Sylhet division; and the professional reason is the main reason for rural to urban migration.

Sensitivity analysis of structural equation model with non-normal observed variables

Abstract Structural equation modeling (SEM) relies on normal theory methods – such as maximum likelihood method (ML) and generalized least squares (GLS) when estimating model and testing model goodness of fit. This limits its applicability since most of the observed variables in social sciences are almost never normally distributed. In this paper, we relax this restriction and conduct a simulation experiment with the simplest observed variable SEM model to see the extent and nature of effects of nonnormality on SEM. We incorporate that the distribution of error of the model belongs to g -and- k family of distributions.