Browsing by Author "Abedin, Tasnima"

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    Barriers to cervical cancer screening faced by immigrant women in Canada: a systematic scoping review
    (2018-10-11) Ferdous, Mahzabin; Lee, Sonya; Goopy, Suzanne; Yang, Huiming; Rumana, Nahid; Abedin, Tasnima; Turin, Tanvir C
    Abstract Background The objective of this scoping study is to review the published literature and summarize findings related to barriers experienced by immigrant women in Canada while accessing cervical cancer screening. Methods Electronic databases of peer-reviewed articles and grey literature were searched using comprehensive sets of keywords, without restricting the time period or language. Articles were selected based on the following criteria: (a) the study population consisted of Canadian immigrant women and healthcare providers and other stakeholders serving immigrant women, (b) the research focused on the barriers to accessing cervical cancer screening, and (c) the study was conducted in Canada. Results Extracted data were grouped and analyzed, resulting in barriers comprised of six themes: economic barriers, cultural barriers, language barriers, healthcare system-related barriers, knowledge-related barriers, and individual-level barriers. Lack of education, low income, preference for a female physician, lack of knowledge, lack of effective communication, and embarrassment were some of the most common barriers mentioned. Conclusions Immigrant access to health services, including cervical cancer screening, is a complex issue concerning a wide range of barriers. Our findings offer insights into barriers to cervical cancer screening in immigrant communities in Canada that can be used to assist policymakers, healthcare providers, and researchers enhance the health and well-being of these populations by mitigating barriers and improving screening.
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    Inferences for Two-Component Mixture Models with Stochastic Dominance
    (2018-01-18) Abedin, Tasnima; Wu, Jingjing; Lu, Xuewen; Leon, Alexander de; Liao, Wenyuan; Nettleton, Dan
    In this thesis, we studied a two-component nonparametric mixture model with a stochastic dominance constraint, which is a model that arises naturally from genetic studies. For this model, we proposed and studied nonparametric estimation based on cumulative distribution functions (c.d.f.s) and maximum likelihood estimation (MLE) through multinomial approximation. In order to incorporate the stochastic dominance constraint, we introduced a semiparametric model structure for which we proposed and investigated both MLE and minimum Hellinger distance estimation (MHDE). We also proposed a hypothesis testing to test the validity of the semiparametric model. For the proposed methods, we investigated their asymptotic properties such as consistency and asymptotic normality theoretically and through simulation studies. Our numerical studies demonstrated that (1) all the proposed estimation methods work well; (2) the semiparametric model structure incorporates nicely the stochastic dominance constraint and thus the MLE and MHDE based on it are superior in terms of efficiency than the two estimation techniques that do not use this model structure; (3) the MHDE is much more robust than the MLE. To demonstrate the use of these methods, we applied them to several real data including publicly available grain data (Smith et al., 1986) and malaria data (Vonatsou et al., 1998).