Rinku Sutradhar Page 1

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1 Background With 1 in 152 women expected to develop cervical cancer during her lifetime, screening for cervix cancer has become an incredibly valuable tool for detecting early stage cancer and reducing cervix cancer mortality 1. Canadian cancer screening guidelines recommend cervix cancer screening every three years for average-risk women aged Despite the importance of cancer screening and despite the suggested national guidelines, there remain various subpopulations of women in Ontario that are not adherent to screening. Based on prior work using predominantly cross-sectional analyses, under-screened populations in Canada include new immigrants, women living in poverty, and women without a family physician 2-3. Nonadherence to regular cervix cancer screening increases the risk of a delay in cervix cancer diagnosis, which may ultimately affect treatment options and outcomes. Objectives and motivation of proposed research program The specific research objective of this program is to understand the association between individual- and physician-level characteristics and adherence to cervix cancer screening among women age years old in Ontario from The important association between adherence, or being continually up-to-date, and the risk of delay in a diagnosis of cervix cancer is the motivation for our proposal. Unfortunately prior work that has examined adherence to cervix screening has been methodologically limited: crude summary measures are often used to capture adherence and the analyses do not make optimal use of the available data. For example, when examining adherence to cervix cancer screening, some studies simply calculate the proportion of individuals who are up-to-date with screening at a specific point in time 4-6, and others simply categorize individuals into adherent or non-adherent groups based on their screening recommendations 7. We argue that a wide range of screening patterns exist among women who are non-adherent. Some of these women could spend a small portion of time being not up-to-date with cervix screening, whereas others could spend a significant amount of time being not up-to-date; and thus categorizing both groups as non-adherent is oversimplifying the issue. Rinku Sutradhar Page 1

2 During the internship, the successful candidate will learn theory and implementation of Markov multistate modeling methods to examine disparities in adherence to cervix cancer screening among women age years old in Ontario from Specifically the candidate will learn how to: 1- Set-up the data structure to perform a Markov multistate model analysis 2- Implement a relative rate regression model under a multistate framework, using the statistical software R, to examine patient-level and physician-level characteristics associated with adherence to cervix cancer screening among women in Ontario. 3- Determine if the associations vary based on immigration status and socioeconomic status 4- Effectively communicate all methods and results to the study team 5- Draft a manuscript, tables, and figures in preparation for submission to a peer-reviewed journal ***Please note the proposed research is in line with the objectives of the Health Services Research (HSR) program, a joint effort between the Ontario Institute for Cancer Research (OICR) and Cancer Care Ontario (CCO), which aims to enhance Ontario s capacity to study how the organization and delivery of cancer services affects the quality and outcomes of cancer care.*** Methodology We will use multistate modeling methods to longitudinally study the patterns of being adherent with cervix cancer screening over time for women age in Ontario. The analysis will be conducted at the individual level. At any given time, a woman s status will be classified into one of three states: State 1- not adherent with cervix cancer screening, State 2- adherent with cervix cancer screening, or State 3- cervix cancer or dead. At any point in time, a woman will be considered adherent if she had a cervix screen in the prior 3 years, as recommended by the Ontario cancer screening guidelines. Otherwise the woman will be considered non-adherent. A woman can make transitions back and forth between being non-adherent and adherent. These transitions can occur until cervix cancer or death is experienced, which is the absorbing state in our multistate model. Rinku Sutradhar Page 2

3 Multistate models are governed by transition intensity functions, which represent the instantaneous incidence rate of moving from one state to another at a specific point in time For context, a transition intensity function is equivalent to a hazard function in a survival model (a survival model is simply a unidirectional 2-state model). In this proposal, we are specifically interested in the duration of time spent in the not adherent state and in the rapidness of transition from the not adherent state to the adherent state, which can occur numerous times during a woman s observation period. The multistate methodological framework, under time nonhomogenous Markov assumptions, will be used to address the research questions. We will set up the data based on a counting process structure this will allow us to readily handle rightcensoring, time-varying covariates, and delayed entry. Age will be used as the time scale in the analysis. Knowledge dissemination and translation Our research will improve the quality of evidence regarding disparities among women in being adherent with cervix cancer screening over time. We will engage national and international research leaders through traditional mechanisms including conference participation and publication in peer review journals targeting the cancer screening community. Close associations of our study team with provincial and national stakeholder groups will assist knowledge dissemination. Our findings will be presented to the Canadian Partnership Against Cancer Corporation and the territorial screening programs in Ontario; these programs can then target the appropriate populations of women (based on our findings) to minimize the amount of time individuals spend non-adherent to cervix cancer screening. Our work will also facilitate future research - the proposed methodology serves as a foundation for understanding patterns of being adherent with other types of screening for example our approach can be translated to future studies examining adherence to colorectal cancer screening in Ontario. Rinku Sutradhar Page 3

4 Required administrative information Proposed Primary Supervisor: Dr. Rinku Sutradhar (Senior Scientist, Institute for Clinical Evaluative Sciences; Associate Professor of Biostatistics, Dalla Lana School of Public Health, University of Toronto) Phone Number: Address: Co-supervisors: Dr. Lawrence Paszat (Clinician-Scientist, Sunnybrook Hospital; Associate Professor, IHMPE, University of Toronto) Location of internship: Institute for Clinical Evaluative Sciences (ICES) References 1. Canadian Cancer Society's Advisory Committee on Cancer Statistics. Canadian Cancer Statistics Toronto, ON: Canadian Cancer Society; Government of Canada. Statistics Canada. 2013; 7. Accessed Aug 13, Bleyer A, Barr R. Cancer in young adults: 20 to 39 years of age: Overview. Semin Oncol. 2009;36(3): Borkhoff CM, Saskin R, Rabeneck L, et al. Disparities in receipt of screening tests for cancer, diabetes and high cholesterol in Ontario, Canada: A population-based study using area-based methods. Can J Public Health 2013; 104(4):e284-e Kiran T, Wilton AS, Moineddin R, et al. Effect of payment incentives on cancer screening in Ontario Primary Care. Ann Fam Med 2014; Shields M, Wilkins K. An update on mammography use in Canada. Health Reports Statistics Canada (Catalogue no XPE). 7. Friedman EB, Chun J, Schnabel F, et al. Screening prior to breast cancer diagnosis: the more things change, the more they stay the same. Int J of Breast Cancer. Rinku Sutradhar Page 4

5 8. Andersen PK, Keiding N. Multi-state models for event history analysis. Statistical Methods in Medical Research 2002; 11:91-115, 9. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Statistics in Medicine 2007; 26: Rinku Sutradhar Page 5