Mahmoud Torabi

Mahmoud Torabi
University of Manitoba | UMN · Department of Community Health Sciences

PhD

About

81
Publications
8,187
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696
Citations
Citations since 2017
40 Research Items
468 Citations
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
Introduction
My areas of research expertise are spatial statistics and small area estimation. I develop novel biostatistics methods for big data and apply the methods to improve the health and well-being of individuals, communities, and diverse populations. The team I lead is developing new models and techniques in the context of population health research to effectively integrate the knowledge we generate into healthcare practice which is fundamental to the health and well-being of the population.
Additional affiliations
July 2010 - present
University of Manitoba
January 2007 - present
University of Alberta

Publications

Publications (81)
Article
In recent years, small area estimation has played an important role in statistics as it deals with the problem of obtaining reliable estimates for parameters of interest in areas with small or even zero sample sizes corresponding to population sizes. Nested error linear regression models are often used in small area estimation assuming that the cov...
Article
Full-text available
Background Our objectives were to describe both the development, and content, of a charitable food dataset that includes geographic information for food pantries in 12 American states. Methods Food pantries were identified from the foodpantries.org website for 12 states, which were linked to state-, county-, and census-level demographic informatio...
Article
Conventional sampling theory is widely used in environmental and health hazard assessment. However, spatial sampling techniques are among the most efficient methods when sampling units are spatially correlated. Spatial sampling has been introduced and used for population mean estimation. In addition, few works have also been focused for the populat...
Article
In many longitudinal studies, the number and timing of measurements differ across study subjects. Statistical analysis of such data requires accounting for both the unbalanced study design and the unequal spacing of repeated measurements. This paper proposes a time‐heterogeneous D‐vine copula model that allows for time adjustment in the dependence...
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Introduction The aim was to study any spatial and/or temporal patterns of ischemic heart disease (IHD) prevalence and measure the effects of selected social determinants on these spatial and space-time patterns. Methods Data were obtained from the Population Research Data Repository housed at the Manitoba Centre for Health Policy to identify perso...
Article
Linear regression models which account for skewed error distributions with fat tails have been previously studied. These two important features, skewness, and fat tails, are often observed in real data analyses. Covariates measured with an error also happen frequently in the observational data set-up. As a motivating example, wind speed as a covari...
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Objectives: We investigated the spatial disparities and factors associated with gastric cancer (GC) Incidence in Manitoba. Methods: We combined information from Manitoba Cancer registry and Census data to obtain an age-sex adjusted relative risk (IRR) of GC incidence. We geocoded the IRR to the 96 regional health authority districts (RHADs) usin...
Article
We develop a method originally proposed by R. A. Fisher into a general procedure, called tailoring, for deriving goodness-of-fit tests that are guaranteed to have a \(\chi ^{2}\) asymptotic null distribution. The method has a robustness feature that it works correctly in testing a certain aspect of the model while some other aspect of the model may...
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Geographically dependent individual level models (GD‐ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete‐time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptibl...
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Background: Colorectal cancer (CRC) is one of the main causes of mortality and morbidity worldwide. Socio-economic status is one of the most important related factors with CRC. Objectives: In this study, we used the human development index (HDI) as one of the common measures of socio-economic status to predict the incidence rate of CRC in the count...
Article
en Unit‐level regression models are commonly used in small area estimation (SAE) to obtain an empirical best linear unbiased prediction of small area characteristics. The underlying assumptions of these models, however, may be unrealistic in some applications. Previous work developed a copula‐based SAE model where the empirical Kendall's tau was us...
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Background: Mental health outcomes vary widely among high-income countries, although mental health problems represent an increasing proportion of the burden of disease for all countries. This has led to increased demand for healthcare services, but mental health outcomes may also be particularly sensitive to the availability of social services. Th...
Article
Policy decisions regarding allocation of resources to subgroups in a population, called small areas, are based on reliable predictors of their underlying parameters. However, the information is collected at a different scale than these subgroups. Hence, we need to predict characteristics of the subgroups based on the coarser scale data. In view of...
Article
Mixed models are commonly used to analyze spatial data which frequently occur in practice such as in health sciences and life studies. It is customary to incorporate spatial random effects into the model to account for spatial variation of the data. In particular, Poisson mixed models are used to analyze the spatial count data. It is often assumed...
Article
We propose a simple, unified, Monte‐Carlo‐assisted approach (called ‘Sumca’) to second‐order unbiased estimation of the mean‐squared prediction error (MSPE) of a small area predictor. The MSPE estimator proposed is easy to derive, has a simple expression and applies to a broad range of predictors that include the traditional empirical best linear u...
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Full-text available
Objectives The objectives of this study were to: (1) examine whether the smoking status of the Canadian population is associated with a reduction in health-related quality of life (HRQoL); (2) calculate the overall economic burden of loss in HRQoL using a commonly accepted $100,000 willingness-to-pay (WTP) threshold to gain one quality-adjusted lif...
Article
Background We investigated temporal trends, geographical variation, and geographical risk factors for incidence of inflammatory bowel disease (IBD). Methods We used the University of Manitoba IBD Epidemiology Database to identify incident IBD cases diagnosed between 1990 and 2012, which were then geocoded to 296 small geographic areas (SGAs). Soci...
Article
In survey sampling, policy decisions regarding the allocation of resources to sub‐groups of a population depend on reliable predictors of their underlying parameters. However, in some sub‐groups, called small areas due to small sample sizes relative to the population, the information needed for reliable estimation is typically not available. Conseq...
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Background: To improve public access to oral health care, dental hygienists have been identified for practice expansion, and, therefore, they must demonstrate decision-making capacity. This study aimed to identify and test potentially influential factors in dental hygiene decision making. Organizational and gender factors were hypothesized to be m...
Article
Spatial models have been widely used in the public health setup. In the case of continuous outcomes, the traditional approaches to model spatial data are based on the Gaussian distribution. This assumption might be overly restrictive to represent the data. The real data could be highly non‐Gaussian and may show features like heavy tails and/or skew...
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The level of spatial aggregation is a major concern in cluster investigations. Combining regions to protect privacy may result in a loss of power and thus, can limit the information researchers can obtain. The impact of spatial aggregation on the ability to detect clusters is examined in this study, which shows the importance of choosing the correc...
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Objectives To investigate the price and income elasticities of adolescent smoking initiation and intensity to determine the extent to which increased pocket money leads to greater smoking among youth, and whether higher taxes can mitigate this effect. Methods We used the 2012/2013 Canadian Youth Smoking Survey including students in grades 7–12. Th...
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Introduction Large population-based data sets present similar analytic issues across such fields as: population health, clinical epidemiology, education, justice, and children’s services. Step-wise approaches and generalized tools can bring together several pillars: big (typically administrative) data, programming, and study design/analysis. How ca...
Article
Atrial fibrillation (AF) is associated with stroke and mortality. The arrhythmia can be sustained or intermittent. Previous studies that have used fixed covariates and short-time horizons to examine the relation between the pattern of AF and the occurrence of events have produced conflicting results. The Manitoba Follow-Up Study includes 3,983 orig...
Article
Longitudinal data occur frequently in practice such as medical studies and life sciences. Generalized linear mixed models (GLMMs) are commonly used to analyze such data. It is typically assumed that the random effects covariance matrix is constant among subjects in these models. In many situations, however, the correlation structure may differ amon...
Article
In survey sampling, policymaking regarding the allocation of resources to subgroups (called small areas) or the determination of subgroups with specific properties in a population should be based on reliable estimates. Information, however, is often collected at a different scale than that of these subgroups; hence, the estimation can only be obtai...
Article
Small area estimation has become a very active area of research in statistics. Many models studied in small area estimation focus on one or more variables of interest from a single survey without paying close attention to the nature of the covariates. It is useful to utilize the idea of borrowing strength from covariates to build a model which comb...
Article
p> OBJECTIVES: Although individuals living in areas with lower household income have been shown to have higher rates of mortality from colorectal cancer (CRC), findings on the effect of income on CRC incidence in countries with universal health care have been inconsistent. There are limited data from Canada. We investigated the geographic variation...
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Background Screening decreases non-small cell lung cancer (NSCLC) deaths and is recommended by the Canadian Task Force on Preventive Health Care. We investigated risk factor prevalence and NSCLC incidence at a small region level to inform resource allocation for lung cancer screening. Methods NSCLC diagnoses were obtained from the Canadian Cancer...
Article
Mixed models are widely used to analyze longitudinal data, especially in health and medical research. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are condition...
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Objectives To test for time and spatial trends in lymphoid malignancies, including lymphoid leukemia (LL), Hodgkin lymphoma (HL), and non-Hodgkin lymphoma (NHL), in children and adolescents in the province of Manitoba, Canada. Methods Incident cases diagnosed between 1984 and 2013 were identified from the Manitoba Cancer Registry. We assessed time...
Data
Observed and relative survival by sex in children and adolescents with lymphoid leukemia or lymphoma in Manitoba, Canada, 1984–2013. (DOCX)
Data
Most likely clusters for lymphoid leukemia (LL), Hodgkin lymphoma (HL), and non-Hodgkin lymphoma (NHL) incidence in children and adolescents in Manitoba, Canada: 1984–2013. (DOCX)
Data
Hazard ratios (HRs) and 95% confidence intervals (CIs) from Cox regression model. (DOCX)
Article
In this paper, our aim is to analyze geographical and temporal variability of disease incidence when spatio-temporal count data have excess zeros. To that end, we consider random effects in zero-inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio-temporal models that employ conditionally autoregres...
Data
Trace plots of sociodemographic factors, additional choropleth maps, and regression analysis.
Article
Disease mapping of a single disease has been widely studied in the public health setup. Simultaneous modeling of related diseases can also be a valuable tool both from the epidemiological and from the statistical point of view. In particular, when we have several measurements recorded at each spatial location, we need to consider multivariate model...
Article
In survey sampling, policy decisions regarding allocation of resources to subgroups, called small areas, or determination of subgroups with specific properties in a population are based on reliable estimates of small area parameters. However, the information is often collected at a different scale than these subgroups. Hence, we need to estimate ch...
Article
Nerve growth factor (NGF) expression is augmented during neuroinflammation. However, its function in the dorsal root ganglia (DRG) and spinal cord (SC) during experimental autoimmune encephalomyelitis (EAE), the inflammatory model of Multiple Sclerosis, is indistinct. Thus, the role of antigenically induced NGF in Lewis rats under a state of EAE wa...
Article
Policy decisions regarding allocation of resources to subgroups in a population, called small areas, are based on reliable predictors of their underlying parameters. However, in sample surveys, the information to estimate reliable predictors is often insufficient at the level of the small areas. Hence, parameters of the subgroups are often predicte...
Article
Background Identification of geographical areas and ecological factors associated with higher incidence of childhood leukaemias can direct further study for preventable factors and location of health services to manage such individuals.AimThe aim of this study was to describe the geographical variation and the socio-demographic factors associated w...
Article
Small area estimation plays an important role in making reliable inference for subpopulations (areas) for which relatively small samples or no samples are available. In model-based small area estimation studies, linear and generalized linear mixed models have been used extensively assuming that covariates are not subjected to measurement errors. Re...
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Full-text available
Background Hospital readmission is costly and potentially avoidable. The concept of virtual wards as a new model of care is intended to reduce hospital readmissions by providing short-term transitional care to high-risk and complex patients in the community. In order to provide information regarding the development of virtual wards in the Winnipeg...
Article
Mixed models are commonly used for the analysis of small area estimation. In particular, small area estimation has been extensively studied under linear mixed models. Recently, small area estimation under the linear mixed model with penalized spline (P-spline) regression model, for fixed part of the model, has been proposed. However, in practice th...
Article
Spatial modeling is widely used in environmental sciences, biology, and epidemiology. Generalized linear mixed models are employed to account for spatial variations of point-referenced data called spatial generalized linear mixed models (SGLMMs). Frequentist analysis of these type of data is computationally difficult. On the other hand, the advent...
Article
Disease mapping studies have been widely performed with considering only one disease in the estimated models. Simultaneous modeling of different diseases can also be a valuable tool both from the epidemiological and also from the statistical point of view. In particular, when we have several measurements recorded at each spatial location, we need t...
Article
Background: Macroscopic geographic variation in the incidence and prevalence of MS is well-recognized. Microscopic geographic variation in the distribution of MS is also recognized, but less well-studied. Most studies have focused on prevalent cases of MS, although studies of variation in disease incidence are more relevant for developing etiologi...
Article
Background Neuropathic pain (NPP) is a chronic syndrome suffered by patients with multiple sclerosis (MS), for which there is no cure. Underlying cellular mechanisms involved in its pathogenesis are multifaceted, presenting significant challenges in its management.MethodsA randomized, double-blind, placebo-controlled study involving 15 relapsing-re...
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Full-text available
We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Bayesian disease mapping (...
Article
The analysis of geographical and temporal variability of binomial data, using generalized additive mixed models, are considered. In this class of models, spatially correlated random effects and temporal components are adopted. The frequentist analysis of these complex models is computationally difficult. Recently developed method of data cloning ha...
Article
We propose an extension of the well-known Fay and Herriot (1979) area level model to sub-area level. Not only this model may be used to estimate small area means by borrowing strength from related areas, but also by borrowing strength from sub-areas to obtain more efficient sub-area estimators. Model-based empirical best linear unbiased prediction...
Article
Full-text available
To investigate the geographical variation and small geographical area level factors associated with colorectal cancer (CRC) mortality. Information regarding CRC mortality was obtained from the population-based Manitoba Cancer Registry, population counts were obtained from Manitoba's universal health care plan Registry and characteristics of the are...
Article
Full-text available
A significant number of deaths attributed to colorectal cancer (CRC) can be prevented if the cancer is detected at an early, curable stage. Although decreasing the incidence of CRC is important, the focus of CRC screening and surveillance has ultimately been to decrease CRC-related mortality. Given that universal health systems are expected to prov...
Article
Full-text available
Bowel disorders have destructive impacts on the patients social and mental aspects of life and cancause emotional distress. The risk of developing bowel incontinence also increases with age. Therate of incidence of inflammatory bowel disease in Manitoba, Canada, has been unusually raised.Therefore, it is important to identify trends in the incidenc...
Article
In spatial epidemiology, detecting areas with high ratio of disease is important as it may lead to identifying risk factors associated with disease. This in turn may lead to further epidemiological investigations into the nature of disease. Disease mapping studies have been widely performed with considering only one disease in the estimated models....
Article
Most of the research on small area estimation has focused on unconditional mean squared error (MSE) estimation under an assumed small area model. Datta et al. (2011) [3] studied conditional MSE estimation of a small area mean under a basic area-level model, conditional on the area-specific direct estimator. In this paper, estimation of a small area...
Article
In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of disease ratios. In this class of models, spatio–temporal models that use conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are considered. The frequentist...
Article
The generalized linear mixed models (GLMMs) for clustered data are studied when covariates are measured with error. The most conventional measurement error models are based on either linear mixed models (LMMs) or GLMMs. Even without the measurement error, the frequentist analysis of LMM, and particularly of GLMM, is computationally difficult. On th...
Article
Bayesian methods have been extensively used in small area estimation. A linear model incorporating autocorrelated random effects and sampling errors was previously proposed in small area estimation using both cross-sectional and time-series data in the Bayesian paradigm. There are, however, many situations that we have time-related counts or propor...
Article
This paper studies generalized linear mixed models (GLMMs) with two components of dispersion. The frequentist analysis of linear mixed model (LMM), and particularly of GLMM, is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of LMM and GLMM computationally convenient....
Article
Full-text available
In this article, a generalized linear mixed model (GLMM) based on a frequentist approach is employed to examine spatial trend of asthma data. However, the frequentist analysis of GLMM is computationally difficult. On the other hand, the Bayesian analysis of GLMM has been computationally convenient due to the advent of Markov chain Monte Carlo algor...
Article
Using both time-series and cross-sectional data, a linear model incorporating autocorrelated random effects and sampling errors was previously proposed in small area estimation. However, in practice there are many situations that we have time-related counts or proportions in small area estimation; for example a monthly dataset on the number of inci...
Article
Previously, the nested error linear regression models using survey weights have been studied in small area estimation to obtain efficient model-based and design-consistent estimators of small area means. In particular, the pseudo-empirical Bayes (PEB) using survey weights has received a lot of attention and is being used in statistical agencies. Th...
Article
In this article, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal models that use intrinsic conditionally autoregressive smoothing across the spatial dimension...
Article
The U.S. Bureau of Labour Statistics publishes monthly unemployment rate estimates for its 50 states, the District of Columbia, and all counties, under Current Population Survey. However, the unemployment rate estimates for some states are unreliable due to low sample sizes in these states. Datta et al. (1999) proposed a hierarchical Bayes (HB) met...
Article
Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. The most popular methods for detecting spatial focused clusters are circular spa...
Article
Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study five popular methods for detecting spatial clusters. The...
Article
To examine childhood cancer diagnoses in the province of Alberta, Canada during 1983-2004, we construct a generalized additive mixed model for the analysis of geographic and temporal variability of cancer ratios. In this model, spatially correlated random effects and temporal components are adopted. The interaction between space and time is also ac...
Article
Nested error linear regression models using survey weights have been studied in small area estimation to obtain efficient model-based and design-consistent estimators of small area means. The covariates in these nested error linear regression models are not subject to measurement errors. In practical applications, however, there are many situations...
Article
Using survey weights, You & Rao [You and Rao, The Canadian Journal of Statistics 2002; 30, 431-439] proposed a pseudo-empirical best linear unbiased prediction (pseudo-EBLUP) estimator of a small area mean under a nested error linear regression model. This estimator borrows strength across areas through a linking model, and makes use of survey weig...
Article
This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates This class of models adopts spatially correlated random effects and ran dom temporal components Spatio temporal models that use conditional autoregressive smoothing across the spatial dimension and autoregressive smoot...
Article
Small area estimation is studied under a nested error linear regression model with area level covariate subject to measurement error. Ghosh and Sinha (2007) obtained a pseudo-Bayes (PB) predictor of a small area mean and a corresponding pseudo-empirical Bayes (PEB) predictor, using the sample means of the observed covariate values to estimate the t...
Article
Previously, small area estimation under a nested error linear regression model was studied with area level covariates subject to measurement error. However, the information on observed covariates was not used in finding the Bayes predictor of a small area mean. In this paper, we first derive the fully efficient Bayes predictor by utilizing all the...
Article
Full-text available
In geographic surveillance of disease, areas with large numbers of disease cases are to be identified so that investigations of the causes of high disease rates can be pursued. Areas with high rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by ch...
Data
The result of clustering for each HA with gender and age year as strata based on the compound Poisson and normal distributions. This is an excel file. For each HA in the compound Poisson approach, the number of neighbours (wi) to determine the cluster size (kiw) is presented along with the test statistics (li), the number of visits (vi:l) and the p...
Article
Full-text available
Lehtonen and Veijanen (1999) proposed a new model-assisted generalized regression (GREG) estimator of a small area mean under a two-level model. They have shown that the proposed estimator performs better than the customary GREG estimator in terms of average absolute relative bias and average median absolute relative error. We derive the mean squar...

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