Browsing by Author "Jafari, Behnaz"

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    Bias and Bias-Correction for Individual-Level Models of Infectious Disease
    (2020-01-30) Jafari, Behnaz; Deardon, Rob; Chekouo, Thierry T.; Kopciuk, Karen Arlene
    Accurate infectious disease models can help scientists understand how an ongoing disease epidemic spreads and help forecast the course of epidemics more effectively (e.g. O'Neill, 2010; Jewell et al., 2009; Deardon et al., 2010). The main purpose of infectious disease modeling is to capture the main risk factors that affect the spread of a disease and make a prediction based on these factors. In real life, we do not generally have homogeneous and homogeneously mixing populations and various factors affect the spread of a disease (e.g. geographical, social, domestic, and employment networks, genetics factors). Using individual-level-models (ILMs) (Deardon et al., 2010) can help researchers to incorporate population heterogeneity. In these models inferences are made within a Bayesian Markov chain Monte Carlo (MCMC) framework (e.g. Gamerman and Lopes, 2006), obtaining posterior estimates of model parameters. However, parameter estimation and bias of estimates go hand in hand. The issue of bias of parameter estimates, and methods for bias correction, have been widely studied in the context of many of the most established and commonly used statistical models, and associated methods of parameter estimation. However, these methods are not directly applicable to individual-level infections disease data. The focus of this thesis is to investigate circumstances in which ILM parameter estimates may be biased in some simple disease system scenarios. Further, we aim to find bias-corrected estimates of ILM parameters using simulation and compare them with the posterior estimates of the model parameter. We also discuss the factors that affect performance of these estimators.
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    Correction: Prevalence and factors associated with polypharmacy: a systematic review and meta-analysis
    (2022-09-12) Delara, Mahin; Murray, Lauren; Jafari, Behnaz; Bahji, Anees; Goodarzi, Zahra; Kirkham, Julia; Chowdhury, Mohammad; Seitz, Dallas P.
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    Prevalence and factors associated with polypharmacy: a systematic review and Meta-analysis
    (2022-07-19) Delara, Mahin; Murray, Lauren; Jafari, Behnaz; Bahji, Anees; Goodarzi, Zahra; Kirkham, Julia; Chowdhury, Zia; Seitz, Dallas P.
    Abstract Introduction Polypharmacy is commonly associated with adverse health outcomes. There are currently no meta-analyses of the prevalence of polypharmacy or factors associated with polypharmacy. We aimed to estimate the pooled prevalence of polypharmacy and factors associated with polypharmacy in a systematic review and meta-analysis. Methods MEDLINE, EMBASE, and Cochrane databases were searched for studies with no restrictions on date. We included observational studies that reported on the prevalence of polypharmacy among individuals over age 19. Two reviewers extracted study characteristics including polypharmacy definitions, study design, setting, geography, and participant demographics. The risk of bias was assessed using the Newcastle-Ottawa Scales. The main outcome was the prevalence of polypharmacy and factors associated with polypharmacy prevalence. The pooled prevalence estimates of polypharmacy with 95% confidence intervals were determined using random effects meta-analysis. Subgroup analyses were undertaken to evaluate factors associated with polypharmacy such as polypharmacy definitions, study setting, study design and geography. Meta-regression was conducted to assess the associations between polypharmacy prevalence and study year. Results 106 full-text articles were identified. The pooled estimated prevalence of polypharmacy in the 54 studies reporting on polypharmacy in all medication classes was 37% (95% CI: 31-43%). Differences in polypharmacy prevalence were reported for studies using different numerical thresholds, study setting, and publication year. Sex, study geography, study design and geographical location were not associated with differences in polypharmacy prevalence. Discussion Our review highlights that polypharmacy is common particularly among older adults and those in inpatient settings. Clinicians should be aware of populations who have an increased likelihood of experiencing polypharmacy and efforts should be made to review the appropriateness of prescribed medications and occurrence of adverse effects potentially associated with polypharmacy. Conclusions and implications Clinicians should be aware of the common occurrence of polypharmacy and undertake efforts to minimize inappropriate polypharmacy whenever possible.