THE EFFECT OF THE COLON CANCER CHECK … · ii Thesis Abstract Thesis Title: The Effect of the...

THE EFFECT OF THE COLON CANCER CHECK PROGRAM ON COLORECTAL CANCER SCREENING

IN ONTARIO

by

Gladys Honein

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy in Health Services Research

Graduate Institute of Health Policy Management and Evaluation University of Toronto

© Copyright by Gladys Honein, 2012

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Thesis Abstract Thesis Title: The Effect of the Colon Cancer Check Program on Colorectal Cancer Screening in Ontario Student: Gladys Honein Degree: Doctor of Philosophy in Health Services Research Graduate Department: Institute of Health Policy Management and Evaluation University: University of Toronto Year of convocation: 2012

Background: This thesis is composed of three studies testing the effect of the Colon Cancer

Check (CCC) program, the organized screening program for colorectal cancer in Ontario, on

screening participation. In the first paper, we described the trends of participation to Fecal Occult

Blood Test (FOBT) and endoscopy, and the trend of ‘up-to-date’ consistent with guidelines,

overall and stratified by demographic characteristics between 2005 and 2011. In the second

paper, we tested the effect of physician’s recommendation on FOBT participation and disparities

in participation. In the third paper, we measured the effect of the CCC program on FOBT

participation using an interrupted time series.

Methods: We identified six annual cohorts of individuals eligible for CRC screening in Ontario

between 2005 and 2011 by linking the Registered Persons Database to Ontario Health Insurance

Plan and 2006 Census from Statistics Canada. We used descriptive statistics to describe the

trends of participation. The effect of physician’s recommendation on screening participation was

tested using multiple logistic regression analysis. The effect of the CCC program on FOBT

participation was tested using segmented regression analysis.

Results: An increasing trend in FOBT participation and ‘up-to-date’ status was observed across

all demographic characteristics. The disparity gaps persisted over time by gender, income, recent

registrant and age. The rural/urban gap was removed. Physician’s recommendation tripled the

likelihood of FOBT participation (prevalence rate ratio=3.23, CI= 3.22-3.24) and mitigated

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disparities. The CCC led to a temporary increase in level (8.2‰ person-month) in FOBT

participation followed by a decline in trend and then a plateau. The increase in level was

significant across all population sub-groups.

Conclusions: We found that CRC screening has increased in Ontario across all subgroups of the

population but remained suboptimal. Disparities in screening participation were identified.

Proposed strategies to improve performance include interventions to increase the rate of

physician’s recommendation at the practice level, tailored interventions to motivate under-users

and public media campaigns.

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Acknowledgments

There are a number of people I would like to thank whose contributions made this thesis

possible:

First, I would like to thank Arlene Bierman, my supervisor and mentor during the PhD. Her

insightful comments and advice kept me on track and encouraged me to go forward.

To my Thesis Committee (Rahim Moineddin, David Urbach, and Arlene Bierman) for their input

and guidance at every stage of this thesis.

To my advisory committee who kept me on my toes with their pragmatic and insightful

comments: Nancy Baxter, Linda Rabeneck, Lawrence Paszat, and Jill Tinmouth.

To the Institute for Clinical Evaluative Sciences (ICES) for providing access to data and facilities

to conduct the analysis.

To ICES personnel who were supportive at every step of the analysis: Refik Saskin, Pam

Slaughter, Lucy Gerry and Park Jin.

To the Canadian Institutes of Health Research, Institute for Gender and Health for supporting me

over the last three years of my thesis.

To the Institute of Health Policy Management and Evaluation (Rhonda Cockerill, Whitney Berta,

Christina Lopez, Mariana Vardaei) for their administrative support and guidance for all thesis

related matters.

To my family members for their understanding and encouragement all along.

And most important to my wonderful loving husband, Mounir AbouHaidar who provided advice,

support and unequivocal support at every stage of this arduous journey.

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Table of Contents

TableofContents

Acknowledgments ................................................................................................................. iv

TableofContents.................................................................................................................... v

ListofTables .......................................................................................................................... ix

ListofFigures.......................................................................................................................... x

ListofAppendices .................................................................................................................. xi

CHAPTER1:BACKGROUND .....................................................................................................1

Colorectalcancerepidemiology...........................................................................................................................................1Riskfactorsforcolorectalcancer........................................................................................................................................2Screeningforcolorectalcancer............................................................................................................................................3ScreeningtestinginCanada...................................................................................................................................................4ColonCancerCheckprogram:anorganizedscreeningprogram..........................................................................5TheColonCancerCheckprograminOntario.................................................................................................................6

Thesisoverview..........................................................................................................................................................................8Specificobjectives......................................................................................................................................................................9Significanceofthisthesis........................................................................................................................................................9Conceptualframework .........................................................................................................................................................10Aliteraturemap.......................................................................................................................................................................13Intrapersonallevel .................................................................................................................................................................. 13Interpersonallevel .................................................................................................................................................................. 15Sociallevel .................................................................................................................................................................................. 17Politicallevel.............................................................................................................................................................................. 17

Ethicsstatement ......................................................................................................................................................................18Figureforbackgroundchapter .........................................................................................................................................19

CHAPTER2:COHORTIDENTIFICATIONANDMETHODS.......................................................... 20

Studydesign ..............................................................................................................................................................................20Datasources ..............................................................................................................................................................................21

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RegisteredPersonsDatabase(RPDB) ............................................................................................................................ 21OntarioHealthInsurancePlan(OHIP) .......................................................................................................................... 21TheCanadianInstituteofHealthInformationDischargeAbstractsDatabase(CIHIDAD)................. 21TheOntarioCancerRegistry(OCR)................................................................................................................................. 222006CensusData .................................................................................................................................................................... 22

Method.........................................................................................................................................................................................23Datalinkage............................................................................................................................................................................... 23Studycohorts ............................................................................................................................................................................. 24Definitionsofvariables ......................................................................................................................................................... 26Analyses ....................................................................................................................................................................................... 29Tracingeligibilityandcalculatingpersonmonthineachquarter ................................................................... 31

CHAPTER3:TRENDSANDDISPARITIESINCOLORECTALCANCERSCREENINGTESTS

PARTICIPATIONINONTARIO,2005‐2011............................................................................... 33

ABSTRACT..................................................................................................................................................................................33BACKGROUND ..........................................................................................................................................................................35METHOD......................................................................................................................................................................................36DataSources .............................................................................................................................................................................. 36CohortIdentification.............................................................................................................................................................. 36

Measures.....................................................................................................................................................................................38Demographicvariables ......................................................................................................................................................... 39StatisticalAnalyses ................................................................................................................................................................. 39

RESULTS......................................................................................................................................................................................40TrendsinFOBTparticipation ............................................................................................................................................ 40Trendsinendoscopyparticipation .................................................................................................................................. 41Trendsin‘uptodate’status............................................................................................................................................... 41

DISCUSSION...............................................................................................................................................................................42LIMITATIONS............................................................................................................................................................................45CONCLUSION.............................................................................................................................................................................46Figuresforchapter3 ............................................................................................................................................................. 48Tablesforchapter3 ............................................................................................................................................................... 50

CHAPTER4:THEINFLUENCEOFPHYSICIANRECOMMENDATIONONPARTICIPATIONINFECAL

OCCULTBLOODSCREENINGTESTINONTARIOUSINGPOPULATIONBASEDDATA................. 61

ABSTRACT..................................................................................................................................................................................61

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INTRODUCTION.......................................................................................................................................................................62METHOD......................................................................................................................................................................................63DataSources .............................................................................................................................................................................. 63CohortIdentification.............................................................................................................................................................. 64StatisticalAnalyses ................................................................................................................................................................. 66

RESULTS......................................................................................................................................................................................66Distributionofcontactwithphysicianandphysician’srecommendationbydemographic

characteristics........................................................................................................................................................................... 67UnadjustedandmultivariateregressionadjustedprevalencerateratioofFOBTparticipation ........ 67

DISCUSSION...............................................................................................................................................................................68LIMITATIONS............................................................................................................................................................................71CONCLUSION.............................................................................................................................................................................72Figuresforchapter4 ............................................................................................................................................................. 73Tablesforchapter4 ............................................................................................................................................................... 75

CHAPTER5:THEEFFECTOFTHECOLONCANCERCHECKPROGRAMONFECALOCCULTBLOOD

TESTPARTICIPATIONINONTARIO:ANINTERRUPTEDTIMESERIESUSINGSEGMENTED

REGRESSIONANALYSIS ......................................................................................................... 82

ABSTRACT..................................................................................................................................................................................82BACKGROUND ..........................................................................................................................................................................84METHOD......................................................................................................................................................................................85DataSources .............................................................................................................................................................................. 85Measures...................................................................................................................................................................................... 87Studydesign ............................................................................................................................................................................... 88

RESULTS......................................................................................................................................................................................90DISCUSSION...............................................................................................................................................................................91STRENGTHS&LIMITATIONS ............................................................................................................................................94CONCLUSION.............................................................................................................................................................................96Figuresforchapter5 ............................................................................................................................................................. 97Tablesforchapter5 ............................................................................................................................................................... 98

CHAPTER6:DISCUSSIONANDCONCLUSION....................................................................... 102

ThesisSummary ................................................................................................................................................................... 102ImplicationsandRecommendations ........................................................................................................................... 103Practice ......................................................................................................................................................................................103

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Healthpolicy ............................................................................................................................................................................104Research ....................................................................................................................................................................................105

ThesisLimitations................................................................................................................................................................ 106Futurestudies........................................................................................................................................................................ 108Conclusion............................................................................................................................................................................... 110

Appendices ......................................................................................................................... 111

Appendix1:CharacteristicsofcolorectalcancerscreeningtestsusedinOntario.................................. 111Appendix2:ColonCancerCheckphysicianincentives........................................................................................ 112Appendix3:Datalinkageflowchart............................................................................................................................ 113Appendix4:Definitionofdemographicvariables ................................................................................................. 116Appendix6:Definitionofexplanatoryandoutcomevariables........................................................................ 118Appendix7:Segmentedregressionstatisticalanalysis....................................................................................... 120Appendix8:FOBTparticipationbyquarterper1000person‐monthsbydemographic

characteristics,Ontario,2005‐2010 ............................................................................................................................ 122

References .......................................................................................................................... 124

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List of Tables

Table 3.1: Population due for colorectal cancer screening by demographic characteristics, Ontario, 2005-2011

Table 3.2: Age standardized percent of Fecal Occult Blood Test participation by demographic characteristics, Ontario, 2005-2011

Table 3.3: Percent of Fecal Occult Blood Test participation by age group, Ontario, 2005-2011

Table 3.4: Age standardized percent of endoscopy participation by demographic characteristics, Ontario, 2005-2011

Table 3.5: Percent of endoscopy participation by age group, Ontario, 2005-2011

Table 3.6: Age standardized percent of ‘up-to-date’ status by demographic characteristics, Ontario, 2005-2011

Table 3.7: Percent of ‘up-to-date’ status by age group, Ontario, 2005-2011

Table 4.1: Population eligible for colorectal cancer screening by demographic characteristics, Ontario, 2008-2010

Table 4.2: Contact with physician by demographic characteristics and recommendation, Ontario, 2008- 2010

Table 4.3: Physician recommendation by demographic characteristics, Ontario, 2008-2010

Table 4.4: Unadjusted Prevalence Rate Ratio (PRR) of FOBT participation by demographic characteristics, Ontario, 2008-2010

Table 4.6: Multiple regression adjusted prevalence rate ratio of FOBT participation by demographic characteristics, Ontario, 2008-2010


Table 5.2: Parameter estimates from the segmented regression analysis for the effect of the Colon Cancer Check on FOBT participation per 1000 person-months with and without adjustment for autocorrelation

Table 5.3: Parameter estimates from the segmented regression analysis for the effect of the Colon Cancer Check program on FOBT participation per 1000 person-months by demographic characteristics

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List of Figures

Figure 1.1: Thesis conceptual framework Figure 3.1. Age standardized percent of FOBT participation by demographic characteristics, Ontario, 2005-2011

Figure 3.2. Percent of FOBT participation by age, Ontario, 2005-2011

Figure4.1.Percentofphysicianrecommendationbydemographiccharacteristics,Ontario,2008‐2010

Figure 4.2: Percent of FOBT participation by demographic characteristics, Ontario, 2008-2010

Figure 5.1: Quarterly rates of Fecal Occult Blood Test (FOBT) participation per 1000 person-months, Ontario, 2005-2010

Figures in appendix 8: Figures 5.2-5.6: Quarterly rates of FOBT participation per 1000 person-months by demographic characteristics, Ontario, 2005-2010

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List of Appendices

Appendix 1: Characteristics of colorectal cancer screening tests used in Ontario

Appendix 2: Colon Cancer Check physician incentives

Appendix 3: Data linkage flow chart

Appendix 4: Definition of demographic variables

Appendix 5: Person-month calculation flowchart

Appendix 6: Definitions of outcome measures

Appendix 7: Segmented regression statistical analyses

Appendix 8: Quarterly rates of FOBT participation per 1000 person-months by demographic characteristics, Ontario, 2005-2010

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CHAPTER 1: BACKGROUND

Colorectal cancer epidemiology

Colorectal cancer (CRC) is the most common form of gastrointestinal cancer affecting the colon

and the rectum. CRC almost always starts as a benign adenoma polyp on the inner wall of the

colon or rectum. The cell linings of the colon or rectum become abnormal and divide rapidly

forming a polyp. Certain kind of polyps called adenomas progress slowly towards a carcinoma

(Bond, 2003; Kelloff, et al., 2004). Adenomas are very common, they occur in one third to one

half of all individuals, but only 10% progress towards a carcinoma (Society, 2011). Carcinoma

takes on average 10-15 years to progress from a polyp to a carcinoma (Bond, 2003). At first,

cancer cells are contained on the surface of the polyp but with time they spread to the wall of the

colon or rectum and then to blood and lymph nodes (Canada, 2011). If these polyps are detected

at an early stage we can, not only detect cancer at an early stage, but also eliminate the disease

at a benign stage.

Colorectal cancer is a global burden. Worldwide, CRC is the third most common cancer in men

(10% of total cancers) and the second in women (9.4%) (GLOBOCAN, 2008; I. A. f. R. o. C.

IARC, 2011). Sixty percent of cases occur in developed countries. The higher incidence in

developed countries may be due to disparate set of risk factors and diagnostic practices (Jemal,

et al., 2011). Worldwide, CRC accounts for 8% of all cancer deaths, or an estimated 608,000

men and women die from the disease every year, making it the fourth most common cause of

cancer deaths (GLOBOCAN, 2008).

In 2011, 22,200 Canadians were estimated to be diagnosed with colorectal cancer and 8,900 to

die from the disease, making it the second leading cause of cancer deaths in Canada (Canadian

Cancer Soceity CCS, 2011). The east-west gradient in incidence and mortality rates is striking.

The highest rates are in Atlantic Provinces and lowest in British Columbia and Alberta. The

differences are attributed to variations in risk factors including genetic factors (Green, et al.,

2007), lifestyle factors or screening practices (Canadian Cancer Statistics CCS, 2011). The

incidence and mortality rates are higher in males than in females. One in 14 men is expected to

develop colorectal cancer in a lifetime and one in 27 to die from the disease. One in 15 women

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is expected to develop colorectal cancer in a lifetime and 31 to die from it (Canadian Cancer

Statistics CCS, 2011).

In Ontario, where this thesis is conducted, colorectal cancer is the second leading cause of

cancer deaths (Care). In 2010, an estimated total of 8,200 of new CRC cases were diagnosed

and there were 3,400 deaths from the disease. Colorectal cancer is the second most frequently

diagnosed cancer for men and the third most frequent for women (13% and 12% respectively)

(Canadian Cancer Statistics CCS, 2011; CSQI, 2011). The incidence has been decreasing

overtime. In 2010, the age standardized incidence rates for men and women were estimated at

59.4 and 38.3 per 100,000 respectively, down from 62.8 and 42.6 in 1986. Mortality rates have

decreased for both males (35 in 1986 to 25 per 100,000 in 2010) and females (22 in 1986 to 15

in 2010). The 5-year survival from CRC improved, from 57% in 2003 to 62% in 2007. The

decrease in CRC mortality reflects improvement in prognosis due to screening, treatment or

both (CSQI, 2011).

The incidence of colorectal cancer is disparately distributed in Ontario. CRC incidence is 16-

17% higher in rural areas. The difference in incidence is attributed to differences in risk factors

and screening for CRC. Individuals living in rural areas are more likely to smoke and less likely

to consume the daily recommended servings of fruits and vegetables. Neighborhoods with

higher percentage of immigrants have lower incidence rates, 22% compared to 24% in

neighborhood with lower percentage of immigrants. The difference may reflect a lower risk of

colon and rectum cancer in the countries of birth (CSQI, 2011)

Risk factors for colorectal cancer

The identifiable risk factors for colorectal cancer include hereditary factors, personal or family

history of CRC, inflammatory bowel disease, racial and ethnic background, age, behavioral and

social factors. These factors form the basis for stratifying individuals into high and average risk

groups.

Individuals at high risk of developing colorectal cancer are those who have hereditary, personal

or family history of CRC, and those with history of inflammatory bowel disease. The presence

of these risk factors necessitates screening on a more frequent basis and at an earlier age. About

5% of CRC cases are associated with inherited genetic factors. The most common genetic

diseases are the Familial Adenomatous Polyposis (AFP) and hereditary non-polyposis colorectal

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cancer (HNPCC). About 20-30% of CRC cases are associated with previous medical history

and family history of the disease (Grady, 2003). First degree relatives of individuals with colon

cancer have a two- fold probability of developing the disease compared to the general

population (Grady, 2003). The risk increases when the disease occurs at a young age (CCAC,

2011). Ulcerative colitis and Crohn’s disease are the most common inflammatory bowel

diseases increasing the lifetime risk for developing colorectal by 2% (Eaden, Abrams, &

Mayberry, 2001), this risk increases with an early onset of the disease (Ekbom, Helmick, Zack,

& Adami, 1990).

Individuals at average risk of developing the disease are those who are asymptomatic, aged 50

and above with no biologic or hereditary history of the disease. The onset of CRC increases with

age. The incidence rate rises from 36.7 per 100,000 for (45-49) age group to 64.13 for (50-54)

age group (P. H. A. o. Canada) and increases gradually with each decade (CCAC, 2011).

Dietary and lifestyle factors are associated with increased risk of developing the disease

including high fat diet, physical inactivity, heavy alcohol consumption, and smoking (Care)

(Huxley, et al., 2009). Occupational exposures to environmental carcinogens can also increase

the risk for colon cancer (Bonner, et al., 2007). Obesity and diabetes increase the risk of

developing CRC (Harriss, et al., 2009) (Moghaddam, Woodward, & Huxley, 2007) (Seow,

Yuan, Koh, Lee, & Yu, 2006). Prevention of CRC for average risk individuals includes lifestyle

behavior modifications and screening.

Screening for colorectal cancer

The purpose of screening for CRC is not only to early detect established cancerous cells but also

to detect precancerous cells. Because it takes 10-15 years for the disease to progress from a

benign polyp to a carcinoma (Bond, 2003), CRC screening tests are ideal not only to improve

the prognosis but also to abort cancer while in its precancerous stage (D. Leddin, et al., 2004;

Vinden & McAlister, 2005).

Multiple screening options are available for colorectal cancer. The risk category determines the

ideal type of screening. For average risk individuals, the most recent Canadian Association of

Gastroenterology position statement (D. J. Leddin, et al., 2010) recommends two tests to be

used in a population-based program: a stool test (high sensitivity- Guaiac Fecal Occult Blood

Test (gFOBT) or preferably Fecal Immunochemical Test (FIT)) at least every two years and a

4

flexible simgoidoscopy every 10 years (previously was 5 years) (D. Leddin, et al., 2004). A

colonoscopy can be used in an opportunistic screening and for follow-up of positive stool tests

(D. J. Leddin, et al., 2010). Other screening tests for CRC include: air-contrast barium enema,

Computerized Tomography Colonography (CTC), and altered DNA in stool (sDNA).

The different screening options for CRC vary in their ability to detect cancerous or pre-

cancerous cells and in their ability to interrupt the adenoma-cancer sequence by removing

polyps. A gFOBT, for example, is meant to detect small amount of blood in the stool resulting

from bleeding vessels at the surface of polyps or adenomas, regardless of the site of bleeding. It

is estimated that gFOBT can detect from 9-64% of polyps (CCAC, 2011). Colonoscopy, on the

other hand, allows the doctor to see the inside of the rectum or colon and permits to remove the

polyp during the procedure. Colonoscopy can detect 74% to 95% of cancer cells depending on

the site and size, of adenoma cells (Baxter & Rabeneck, 2010; Rex, et al., 2009). Screening tests

for CRC are also effective in reducing mortality from the disease. A regular screening using

FOBT followed-up by colonoscopy for positive cases reduces mortality from CRC by 15%

(Hardcastle, et al., 1996; Hewitson, Glasziou, Irwig, Towler, & Watson, 2007).

(Appendix 1: summary characteristics for the most commonly used colorectal cancer screening

tests in Ontario).

Screening testing in Canada

A few population-based studies done in provinces and sub-provincial regions in Canada

revealed that since the introduction of the national screening recommendations a decade ago

colorectal cancer screening participation was on the rise but remained suboptimal (McGregor,

Hilsden, Li, Bryant, & Murray, 2007; Rabeneck & Paszat, 2004; Sewitch, Fournier, Ciampi, &

Dyachenko, 2007; Wilkins, 2009; Zarychanski, Chen, Bernstein, & Hebert, 2007). In 2003, self-

reported adherence to guidelines (i.e. FOBT every two years, colonoscopy/simgoidoscopy in

past 10 years) in four provinces in Canada (Ontario, Newfoundland, Saskatchewan and British

Columbia) was 30% (Sewitch, et al., 2007). In 2008, 40% of Canadians aged 50-74 self-

reported having had an FOBT in the past two years or endoscopy (both colonoscopy and

flexible simgoidoscopy) in the past five years (Wilkins, 2009).

Geographic differences in CRC testing were systematically reported. In general the likelihood of

CRC adherence to guidelines is less in Atlantic Provinces, Quebec and territories than the rest of

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provinces. Moreover, geographic differences in the type of test were also reported. Atlantic

provinces and Quebec were less likely to have an FOBT and more likely to have endoscopies

than western provinces (Sewitch, et al., 2007; Wilkins, 2009). FOBT participation ranged from

10% in Quebec to 42% in Manitoba, while endoscopy participation varied from 11% in Yukon,

23% in Saskatchewan, to 30% in Ontario (Wilkins, 2009). Comparison over time revealed that

the increase in trend was significant for all tests and in all provinces (Wilkins, 2009)

The likelihood of participation in screening tests varied by socio-demographic characteristics.

Individuals 65 or older, living in large metropolitan areas, Canadian born individuals,

acculturated immigrants (20 years or more) and higher income were associated with increased

participation(Wilkins, 2009). Gender association with CRC testing was inconsistent.

The patterns of CRC screening in Ontario mirrors the overall pattern in Canada. Prior to 2004, a

population-based study revealed that 20% of screen eligible individuals received CRC testing

timyears period was 20% and in 2007-2008, rates rose to 30%. Colonoscopy rates increase was

more modest but significant over time. A recent study showed that between 1996 and 2005,

there was a four-fold increase in colonoscopy. The rates increased from 1.55 to 4.7% (Jacob, et

al., 2011).

The increase in FOBT participation was significant across all geographic regions of Ontario. In

2001/2002 rates for colonoscopy were highest in the north and central east as well as the

Toronto regions in Ontario and lowest in the east (Schultz, Vinden, & Rabeneck, 2007). In 2007

and 2008, FOBT participation varied from 22% in Muskoka to 36% in Champlain (CCC, 2010).

The likelihood of participation in screening tests varied by socio-demographic characteristics.

Females were more likely to have an FOBT than males, and older adults (70-74) more than

younger adults (50-55) (Ontario., 2010). Higher income were more likely to have an FOBT than

lower income individuals (Krzyzanowska MK, 2009; Ramji, Cotterchio, Manno, Rabeneck, &

Gallinger, 2005).

Colon Cancer Check program: an organized screening program

The high incidence rates of CRC and the suboptimal rates of screening in Ontario led to the

establishment of the Colon Cancer Check program, the first organized province-wide screening

program for colorectal cancer in Canada.

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First, a brief background on opportunistic screening and organized screening. Opportunistic

screening is a medical practice model targeting individuals (Rabeneck, 2007). Testing is

embedded in routine primary care and occurs when the physician captures the opportunity to

recommend and deliver the test and occasionally on individuals to request the test (Senore,

Malila, Minozzi, & Armaroli, 2010). Opportunistic screening is the dominant approach in the

United States. In contrast, an organized screening program is a public health model targeting the

population at large (Rabeneck, 2007). Certain features characterize organized screening

programs including a targeted population for screening, a specific test for screening and

recommended interval for repeat testing, a management team for the implementation of the

program, a health care team for the delivery of services, a standardized quality assurance

program for the laboratory tests, and continuous performance measurement and

monitoring(IARC, 2005a). The benefits of organized screening programs include reaching out

to a large population, reduction of over use, higher quality of services, and a better follow-up

process (Levin, et al., 2011). Organized screening programs for colorectal cancer are presently

implemented through an integrated health care system in twelve Member States of European

Union (I. A. f. R. o. C. IARC, 2011), in the US Veteran’s Administration and in Kaiser

Permanente Northern California (Levin, et al., 2011), in Israel, Japan and the Republic of Korea

(ICSN, 2011). In Canada, the first organized screening program was launched in Ontario in

2008. In 2010, Manitoba and Nova Scotia provinces phased in their programs to cover more

than fifty percent of their communities. Other western provinces (British Columbia, Alberta and

Saskatchewan) have expanded their program to cover between 10 and 50% of their

communities. Eastern provinces (New Foundland, New Brunswick, Prince Edward Island and

Quebec) were still at an evaluation and planning stage. No screening programs were yet

organized for the Northern Territories (CCAC, 2011).

The Colon Cancer Check program in Ontario

Multiple efforts paved the way to the establishment of the organized screening program for

colorectal cancer in Ontario. In 1999, Cancer Care Ontario set up an expert panel to develop

recommendations for a population based colorectal cancer screening. The panel recommended

an FOBT based program for CRC screening for average risk individuals between the ages of 50

and 75 (Screening, April 1999). From 2003 to 2005, the Ontario Ministry of Health and Long

Term Care (MOHTLC) in collaboration with Cancer Care Ontario funded a pilot study to

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identify effective methods for promoting and increasing screening in the community. In 2006,

the pilot study report recommended the establishment of a primary care provider-led, organized

screening program for CRC (Ontario, 2006). In January 2007, the government of Ontario

officially announced the launch of the Colon Cancer Check program. From January 2007 to

April 2008, the preparations for the public launch of the program were underway. On April 1st

2008, the CCC program was launched to the public. An intense media campaign accompanied

the public launch. The dual goals of the program are to reduce the mortality from colorectal

cancer and increase the capacity of primary care providers to participate in an organized

screening program(CCC, 2010). The CCC is not a mail outreach program. The CCC is a

provider-led program. Primary care physicians play a central role in the CCC program. They are

responsible for counseling and dispensing FOBT kits to all eligible patients in their practices

and for patients without primary care physicians (unattached patients), FOBT kits are made

available through pharmacies or through Tele-Health Ontario and once they complete the test,

they are attached to a primary care physician through the CCC.

The CCC program incorporates most elements of an organized screening program features

(Rabeneck, 2007). The CCC identifies average risk individuals aged 50-74 as the target

population eligible for screening in Ontario, adopts the biennial Fecal Occult Blood Test as a

primary test for screening, and colonoscopy to follow-up for positive cases. A capable

management team leads the day-to-day activities of the program. Using an evaluation

framework and indicators, the team is responsible for monitoring the uptake, and for measuring

and reporting the performance. In addition, a province-wide primary care strategy is set to

engage primary care physicians to participate in the program (Levitt & Lupea, 2009).

The target of the Colon Cancer Check program is to increase FOBT participation from 17% in

2005 to 55% in 2013 (Care). Different strategies were used to achieve this target. An intense but

temporary mass media campaign marked the launch of the program including television

advertising, radio messages, newspapers clips and pamphlets in 22 languages across the

province. The media campaign lasted for six months. After that, several continuous community-

based awareness programs took place in various regions of Ontario. Since it is a provider-led

program, strategies to increase awareness and harness the support of primary care providers for

the program were also implemented. An educational campaign targeting primary care providers

was conducted between 2008 and 2010. The objectives of the campaign were to diffuse any

8

confusion among physicians about the evidence underlying anchoring the CCC program to

FOBT and colonoscopy, to clarify the process of ordering FOBT kits and reporting results to the

patient, to empower providers with counseling tools to assist them in education and provision of

service (OCFP, 2009). In fall 2008, a province wide primary care strategy was launched to

engage primary care physicians to participate in the program (Levitt & Lupea, 2009). Further,

knowing that adoption of guidelines requires more steps than simple dissemination (Grimshaw,

et al., 2004; Rabeneck, 2007; Vernon, 1997), the CCC program uses financial incentives to

encourage physicians to expand their delivery of screening services (Chassin, 2006). The

financial strategy of the CCC program includes rewarding physicians for providing the

following services: 1- recommending and dispensing the FOBT kit to eligible individuals while

visiting the practice; 2- calling in or sending a letter to individuals who did not visit the office

asking them to book an appointment to discuss colorectal cancer screening; 3- for completion of

the test and the follow-up thereafter; 4- and for accepting new unattached patients (Care)

(Appendix 2: Colon Cancer Check physician incentives).

To increase reliability and validity, only a few community laboratories that signed an agreement

with the Ministry of Health and Long Term Care are allowed to perform the CCC FOBT test

and claim the specimen collection fee set by the program (Care). Staff training and quality

control are frequently implemented to reduce variability in examining and reporting the results

(L. Rabeneck, 2007).

Thesis overview

The goal of this thesis is to evaluate the effect of the Colon Cancer Check program on colorectal

cancer screening in Ontario. Three studies were conducted:

1. First study: Trends and disparities in colorectal cancer screening participation in

Ontario, 2005-2011.

2. Second study: The influence of physician recommendation on participation in Fecal

Occult Blood Screening test in Ontario using population-based data.

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3. Third study: The effect of the Colon Cancer Check program on Fecal Occult Blood test

participation in Ontario: An interrupted time series using segmented regression.

Specific objectives

1. To describe the trends of FOBT screening rates, endoscopy screening rates and ‘up-to-date’

with CRC guidelines rates, overall and stratified by demographic characteristics, between

April 1st 2005 and March 31st 2011.

2. To describe the pattern of physician’s recommendation in Ontario and test the effect of

physician’s recommendation on FOBT participation and disparities in participation, between

April 1st 2008 and March 31st 2010.

3. To estimate the effect of the Colon Cancer Check program on the trend of FOBT

participation, overall and stratified by demographic characteristics between April 1st 2005

and September 30th 2010.

Significance of this thesis

This thesis addresses three policy issues. The first issue concerns the trend of disparities before

and after the organized screening programs. When screening was opportunistic before the CCC,

differences in participation by gender, age, income, and geographic region were observed (CCC,

2010). Two and a half years after, did the organized screening program reduce those disparities?

In the first study, we highlight whether the disparity after the CCC narrowed, remained as is, or

increased by gender, age, income, urban rural and we add recent registrant variable as a proxy

measure for recent immigrant.

The second policy issue concerns the effectiveness of a provider-led organized screening

program in increasing participation and mitigating disparities. When the United Kingdom Bowel

Cancer Screening Program (BSCP) started in 2006, they used mail outreach to invite individuals

for screening. Thirty months after its implementation, von Wagner et al. found that the response

rate for returning kits ranged from 32% in the most deprived quintile to 49% in the least

deprived indicating that using the mailing outreach motivated higher income individuals to get

screened more than those with lower incomes (von Wagner, et al., 2009). The Colon Cancer

Check program is a provider-led program. Primary care physicians play a central role in

10

motivating their patients to be screened. In the second study, we test whether physician’s

recommendation in a provider-led approach increases the likelihood of FOBT participation and

compliance among all population subgroups.

The third issue concerns the short-term effect of an organized screening program on

participation and disparities in participation. Studies that have directly evaluated changes

following the introduction of a public health intervention showed a rapid increase in

participation at first. This increase stabilized forming a plateau for some time (D. Baker &

Middleton, 2003; Victora, Vaughan, Barros, Silva, & Tomasi, 2000). These studies suggest that

the intervention reached out first to the wealthiest (Victora, et al., 2000) and early adopters

(Anderson, Mullins, Siahpush, Spittal, & Wakefield, 2009). Once the wealthiest and early

adopters reach a level of improvement beyond which the intervention is likely to make progress,

then the pattern stabilized forming a plateau. In the third study, we describe the secular trend of

FOBT participation before the CCC, and then we estimate the effect of the CCC on the level of

FOBT participation immediately after its implementation and the trend thereafter using an

interrupted time series design. We compare the pattern the CCC had with that described in the

literature and interpret the findings.

Conceptual framework

In formulation of a theoretical perspective for evaluating the effect of the Colon Cancer Check

program on colorectal cancer screening in Ontario, a socio-ecological conceptual framework

provides a useful prototype. Since the purpose of this thesis is to evaluate the effect of a new

health policy on a health care system, then a system model would be an appropriate model.

The Socio-Ecological Model (SEM) is a system model. Unlike individually or socially oriented

models, SEM considers both the effect of individual and social environment on outcome. The

central hypothesis of SEM posits that the behavior of an individual living in a system is

determined by a dynamic and ‘reciprocal causation’ between the individual and the

environment. A change in the social environment produces change in individuals and the

support of individuals is essential for implementing social changes (McLeroy, Bibeau, Steckler,

& Glanz, 1988).

Alternatively, SEM differs from other traditional system models by viewing patterned behavior

of individuals or aggregates as outcomes of interest. Researchers using SEM usually explore the

11

effect of the different levels of influence in the social environment on individual’s behavior.

SEM depicts the different levels of social environment influence as overlaps of circles. The

individual is at the center of the model and the other circles surround the individual indicating

that the behavior of an individual is not acting alone in this universal system but rather all other

levels of influence converge to shape an individual behavior.

In an SEM, human behavior is determined by:

• Intrapersonal factors – Individual’s knowledge, attitudes, skills or intention to comply

with certain behavioral norms, which are usually the target for change. Demographic

characteristics are often used as effect modifiers for these characteristics.

• Interpersonal factors – Formal and informal social network and social support including

family, friends, neighbors, coworkers and health care providers, which combined shape

human’s behavior.

• Institutional factors – Social institutions with organizational characteristics to support

behavioral change. Institutions, such as school, work, church, professional or neighborhood

groups, may have positive or negative effects on the health of their members. Since they are

important sources and transmitters of social norms and values, organizations can provide the

opportunity to build social support for a desirable behavior change. Organizational changes

are needed to support long-term behavioral changes among individuals.

• Community Factors – Relationships between institutions, organizations and groups to

which an individual can belong. These "mediating structures," such as family, church,

informal social networks, and neighborhoods, may provide social identity and resources.

• Public Policy - Regulatory policies, procedures and laws have been passed (national,

state or local) to help protect the health of communities. As a part of the policy development

process, increasing the public's awareness of health and policy issues must be included.

Socio-Ecological Models have been applied to a wide range of health issues including

improving dietary habits (Robinson, 2008), tobacco control (X. Zhang, Cowling, & Tang,

2010), increasing physical activities among adolescents (Elder, et al., 2007), chronic disease

management (Cassel, 2010) and increase compliance to screening services such as cervical

cancer screening (Daley, et al., 2011), gastric cancer (Bae, et al., 2008). The Centre for Disease

12

Control adopted the Socio-Ecological Model to represent the colorectal cancer screening

program approach to CRC prevention in the US(CDC, 2011).

Explaining the social ecological model, McLeroy (McLeroy, et al., 1988) indicated that when a

new policy is introduced into the system, it uses different ‘levels of interventions’ to cause a

change in ‘outcomes among the target population’. And it is crucial to distinguish between level

of intervention and target of the intervention. When the intervention takes the form of mass

media, then the level of intervention is the community and the target is the individuals’

characteristics including their knowledge attitudes, and skills in an attempt to increase their

compliance to the new behavior. Another intervention within the same policy can take the form

of educational programs targeting practitioners, then the level of intervention is institutional and

the target for the intervention are the members of that institution. But, it should be clear that by

changing the latter (members of the institution), we are also making changes to the former

(individuals). Hence, the converging circles depicted in the model, that all levels of influence act

together to influence the individual who is at the center of the model.

In the application of the SEM to this thesis, the five levels of influence identified by McLeroy

(McLeroy, et al., 1988) will be defined in the following manner:

1. At the intrapersonal level, we have the individuals who belong to all walks of life.

Participation to colorectal cancer screening using FOBT or endoscopy is the outcome or

the desired individual behavior.

2. At the interpersonal level, we have the physician. The interaction between physician and

individual (physician-patient dyad) is the expected behavior that motivates individuals to

be screened.

3. At the organizational level, we have the educational program for physicians. The

educational programs are meant to increase awareness and harness the support of

physicians to discuss screening with their patients. The educational programs are

expected to increase physician recommendation at the practice level.

4. At the community level, we have the mass media. Mass media is an intervention to

increase awareness among the general public and also among health care providers about

colorectal cancer disease and how screening save lives.

13

5. At the policy level, we have the Colon Cancer Check program. This health policy

influences availability, accessibility and support for screening in all Ontario. The CCC is

the mastermind intervention, which is expected to stir a wave of change in the system.

With these specific variables, the formula for behavior, which was formulated by McLeroy

would be adapted to read: the potential for screening participation to occur in Ontario is a

function of individuals’ receptiveness to the mass media campaign. In addition, the effect of the

educational program on physicians must be considered in relation to increase the person-to-

person interaction with individuals as a motivator for screening. Finally, certain characteristics

such as age, gender, income, recent immigration and living in rural neighborhoods may act as

effect modifier of the program on the overall participation to screening.

The following statement represents the underlying logic for designing and conducting this

thesis. If individuals receive a) the broadcasted message over the radio and the TV meet and

found it that it meets their perceived needs and susceptibility, b) their primary care physicians

are endorsing this message again during their interpersonal encounter, c) and no other personal

or structural barriers is keeping them from taking the test, then the level of screening

participation in Ontario will attain a level never reached before.

Figure 1.1: Thesis conceptual framework

A literature map

Intrapersonal level

Although all average risk individuals could benefit from screening tests for colorectal cancer,

not everyone uses them. The limited use of CRC tests by certain sub-population groups is very

common.

Studies showing females are more likely to have an FOBT than males (Ioannou, Chapko, &

Dominitz, 2003; Ko, Kreuter, & Baldwin, 2005; Krzyzanowska MK, 2009; McQueen, Vernon,

Meissner, Klabunde, & Rakowski, 2006) suggested that because females have more frequent

contacts with the health care system than men for reasons related to their own health and the

health of their families, they have more opportunities to learn about screening than males

(Ramji, et al., 2005). Other studies suggested that because females are exposed and participate

14

more to other preventive services than males, they have positive attitudes towards screening,

thus increasing their likelihood to take a CRC test (Shapiro, Seeff, & Nadel, 2001; Slattery,

Kinney, & Levin, 2004). In some studies, we found that males are more likely to have an

endoscopy than females (colonoscopy, flexible simgoidoscopy) (Christman, et al., 2004; Janz,

Wren, Schottenfeld, & Guire, 2003; Ko, et al., 2005; McQueen, et al., 2006) and the authors

suggested that the attitude towards the test differs between females and males. Some females

may be more concerned about the gender of the physician delivering the endoscopy and more

embarrassed from bodily exposures than males hence the differences in participation by type of

tests(Farraye, et al., 2004; Menees, Inadomi, Korsnes, & Elta, 2005; Stockwell, et al., 2003).

Individuals aged 50-55 have the lowest FOBT participation rates and the lowest ‘up-to-date’

status as compared to other age groups (CCC, 2010; Canadian Cancer Statistics CCS, 2011)

(CCC, 2010; Christman, et al., 2004; Fisher, et al., 2004; Honda, 2004; Meissner, Breen,

Klabunde, & Vernon, 2006; Ramji, et al., 2005; Tessaro, Mangone, Parkar, & Pawar, 2006).

The difference is attributed to the perceived risk for the disease, which increases with age.

Screening services are secondary preventive measures aiming to reduce the burden of disease at

a time when no sign that the disease is actually occurring(Rogers, 2002). Unlike acute care

where the demand for the service follows the onset of the disease and is for immediate relief of

symptoms, the demand for preventive care precedes the onset of the disease and is for self-

protection(Kenkel, 1994). Therefore, the perceived risk for the disease is low for a young

healthy adult and screening is not an identified priority. As they get older, their health starts to

show signs of illnesses, then their perceived risk for the disease increases and eventually their

attitudes towards screening change and they become more likely to be screened.

The levels of education and income influence participation in screening. Higher education is

associated with higher uptake of FOBT screening (Meissner, et al., 2006; Pollack, Blackman,

Wilson, Seeff, & Nadel, 2006; Thompson, Coronado, Neuhouser, & Chen, 2005; Ye, Xu, &

Aladesanmi, 2009) .The level of education reflects a higher score of knowledge, which affects

the cognitive functioning and make the individual more receptive to health messages or more

able to communicate with and access appropriate health services (Galobardes, Shaw, Lawlor,

Lynch, & Davey Smith, 2006). Level of education also facilitates better access to employment

and income. Income by itself does not generate health; but what money can get affects health.

Better income is associated with better access to quality material resources such as food and

15

shelter and allows access to improved health services (Galobardes, et al., 2006). The combined

effect of health literacy, level of communication, affordability and accessibility provides a

favorable environment for the client to participate in screening.

Ethnicity also influences participation in screening. Undoubtedly, every individual in any

society acknowledges the importance of prevention in order to remain healthy. But preventive

practices are influenced by cultural and social factors. Screening is a western practice medicine.

Older adults in some cultures tend to use alternative medicine as their primary source of

preventive care and rely on western medicine when the former is ineffective (Garces, Scarinci,

& Harrison, 2006). But, if they receive recommendation from their physicians, they may be

more likely to comply to their physicians’ advices despite their beliefs and practices (Shokar,

Carlson, & Weller, 2008).

Country of birth also determines participation in screening. In Canada and the US immigrants,

especially those who lived in the country for less than ten years, are less likely to have a CRC

test than individuals born in Canada or US (Shih, Elting, & Levin, 2008; Wilkins, 2009).

Several barriers contribute to this disparity. Recent immigrants tend to be more of low-income

(Shokar, et al., 2008), low health literacy (Kreps & Sparks, 2008) and have less ability to speak

the official language (Thomson & Hoffman-Goetz, 2010), leading to their marginalization and

lack of acculturation. Acculturation is the process whereby one whose learning was in one

culture then adopts values, attitudes and behavior of another culture (Suarez, 1994). Recent

immigrants tend to form social networks with individuals from the same background, preserving

their own cultural and social practices; and depending on the country of birth, they may not have

heard about CRC screening or cancer fatalism in some cultures may keep them for being

screened (Powe & Weinrich, 1999). But these factors tend to disappear with acculturation. For

example, Hispanics in United States have lower CRC screening than whites (Beydoun &

Beydoun, 2008), their odds for screening increases, however, with time since they immigrated,

with speaking the language and their frequent utilization of health services (Afable-Munsuz,

Liang, Ponce, & Walsh, 2009; Honda, 2004; Shah, Zhu, & Potter, 2006)

Interpersonal level

Neighborhood characteristics influence individuals’ attitudes towards colorectal cancer

screening participation (89-95). Gresenz et al. (91) used the well-established sociological

16

principle of “homophily” by Lazarsfeld & Merton in 1954 (Lazarsfeld, 1954) to suggest that

similar people flock together, which results in social networks that are homogenous with regards

to socio-demographic and behavioral characteristics. Individuals who share the same ethnic

origin tend to live in geographical proximity and are more likely to be of the same education,

income, and language. This homogeneity in social network affects the information exchange,

social values, beliefs and attitudes. Individuals living in proximity get together more often to

exchange information about practically everything including sources of culturally competent

care and transmit norms about the appropriate use of care.

Interpersonal communication with the physician influences individuals’ attitudes towards

colorectal cancer screening. A conversation with the physician increases perceived susceptibility

to CRC, provides cues to action and ultimately leads to completion of screening(Fenton, Jerant,

von Friederichs-Fitzwater, Tancredi, & Franks, 2011). Several barriers affect the interpersonal

communication during an office visit. Some are behavioral others are organizational. Physicians

may have ineffective patient-centered communication, which affects patient compliance to

physicians’ recommendations(Stewart, et al., 2000). Following an interpersonal communication

with the provider, patients perceive whether their physicians made an effort to listen and

understand their concerns, responded to their needs and that their communication led to a

common understanding. Following a patient-centered communication, patients are more

satisfied with the services, have better compliance to care leading to improved efficiency and

outcomes(Stewart, et al., 2000). Bias and stereotyping affect the amount of information

delivered during a communication. Stereotypes are interpreted as “an adaptive cognitive strategy

of making the world manageable by using categorizing and generalizing techniques” (van Ryn,

Burgess, Malat, & Griffin, 2006). Using this strategy, individuals develop beliefs and

expectations about these groups and mentally label them with specific characteristics. Because it

is unintentional, most individuals deny engaging in stereotyping. But, stereotyping is common

to all individuals and physicians are not immune to it. For example, physicians’ perceptions that

certain ethnic minorities may have different illness beliefs, different social and cultural

backgrounds, and different interest in engaging in screening, find it a challenge discussing

screening with them (Fiscella & Epstein, 2008), which lead them to deliver less information to

these ethnic groups (van Ryn & Fu, 2003).

17

The organizational characteristics of the practice affecting physician-provider interaction

leading to physician’s recommendation of screening services include high volume practice (101-

103), and lack of allied health professionals to assist in the delivery of services (42, 98, 105). In

team-based practices, the characteristics of team members, the skill mix, the ability of staff to

participate in decision-making influence the prospect of discussing screening services. Other

structural characteristics include office infrastructure such as electronic medical records and

audio-visual resources, which increase the potentials for motivating patients to receive screening

(Hogg, Rowan, Russell, Geneau, & Muldoon, 2008)

Social level

Mass media is one of the social factors influencing screening uptake (58, 107, 108). Rogers,

E.M. suggested that mass media are effective in diffusing knowledge about an innovation and

most individuals adopt that innovation not because of the scientific expert opinion but based on

the subjective evaluations of near peers(Rogers, 2002). Similarly, Whynes et al. (Whynes,

Philips, & Avis, 2007) found that screening is primarily driven by a search for reassurance, a

sense of duty, and herd signaling (Catalano, Winett, Wallack, & Satariano, 2003; Jacobsen &

Jacobsen, 2011). So, how does mass media influence behavior? The influence of mass media on

health behavior comes in two forms. The first form is to increase health information on the topic

of interest; second, to frame the issue as a public health problem and attract the attention of the

audience on the tools to solve that problem(Randolph & Viswanath, 2004). In the case of mass

media for colorectal cancer screening, a mass media campaign draws the attention on the high

incidence of the disease and how screening could save lives. The effect of mass media on

screening participation comes in stages. Diffusing that knowledge to a large audience

encourages certain individuals to adopt screening faster than others. First, to early adopters

constituting 13% of the population, followed by early majority who look for early adopters for

advice and information about screening (34% of the population), then the late majority and the

laggards constituting the rest of the population(Rogers, 2002).

Political level

Organized screening programs, such as the Colon Cancer Check program, are an example for a

health policy influencing screening. Organized screening programs reflect a political will to

support screening in the population. They are meant to enhance the quality of screening and to

18

provide population-wide coverage (Miles, Cockburn, Smith, & Wardle, 2004). They mitigate

various barriers for screening. They remove the financial cost of purchasing the screening test.

They ensure the test is available to all individuals targeted by the program. They set standards

for the quality of screening tests. They reduce overuse and encourage under-users. Organized

screening programs were proven to be effective in increasing cervical cancer participation in

United Kingdom (Miles, et al., 2004), breast cancer and cervical cancer screening participation

in Ontario (CSQI, 2011)

Ethics statement

This thesis only occurred after approval by the Institute for Clinical Evaluative Sciences at

Sunnybrook Health Sciences Centre and University of Toronto institutional review boards. All

data were uniquely labeled using encrypted health card numbers. No unique identifiers such as

patient name, OHIP number, postal code or address were recorded.

19

Figure for background chapter

Figure 1.1: Thesis conceptual framework

20

CHAPTER 2: COHORT IDENTIFICATION AND METHODS

Study design

The studies conducted in this thesis were retrospective cohort studies using administrative data

collected regularly at the Institute for Clinical Evaluative Sciences (ICES). Retrospective cohort

studies are the most reliable design for observational studies exploring the effect of an

intervention in a natural experiment. Because the data is collected in temporal sequence, it is

easy to measure the prevalence of the outcome over time. Since retrospective cohorts use

already collected data originally intended for other purposes, the advantages of using secondary

data include quicker and cheaper access to data (Mann, 2003). Moreover, in retrospective cohort

studies, it is also easier to estimate the relative risk of the outcome associated with other

variables of interest especially when the outcome is quite prevalent among the population. The

main disadvantage of retrospective cohort studies is that they may be missing important

variables for the analysis.

The main advantage of using population-based administrative data, on the other hand, is the

external validity of the results. Findings usually apply to all defined population. Moreover, the

quality and validity of ICES data are well documented. Raw data received at ICES undergo

rigorous quality standards before it becomes available for research use. Data is collected

regularly over time with no interruption, which enabled us to do a time series analysis. Finally, I

was personally privileged to have all three of my thesis committee members ICES scientists,

which enabled me to access the data. The main disadvantage of using ICES administrative data

is that not all variables, especially demographic variables, are included in the original data

collection.

Perhaps, an alternative for administrative data would have been to use survey data such as the

Canadian Community Health Survey (CCHS), which up to 2007 used to collect data every two

years and after 2007 the data collection became yearly. CCHS uses a sampling frame that

represents Canadians aged 12 and over. CCHS is a reliable and regular source of data used by

many health services researchers in Canada. The advantage of the CCHS is that it includes a

wide range of individual level data including data on ethnicity, immigration, and language. The

disadvantages of CCHS include the recall bias associated with any survey data; external validity

is less precise than population-based because it is based on a sample of the population. Finally,

21

for this particular study we could not use CCHS because the latest files available at ICES were

the 2007-2008, the period just before the CCC, which did not serve the purpose of this thesis.

Data sources

We linked five data sources in order to identify individuals eligible for CRC screening in

Ontario, their characteristics, and their screening status. The following data sources were used:

Registered Persons Database (RPDB)

The Registered Persons Database (RPDB) provides basic demographic information on those

who have ever received an Ontario health card number. The Ministry of Health provides the

information to ICES, where it is updated every month and enhanced by additional data. ICES

analysts offset the completeness and inaccuracies of this database, which are largely due to the

untimely updates sent to ICES on deceased, moved in or moved out of the province individuals,

by using other databases to update the information. For example, if individuals had a contact

with the health care system, they are most likely still alive and in the province and their

addresses are considered up-to-date.

In this study, we used two files from the RPDB database: 1- the contact YYYY file, which

contains information on eligibility and contact with health care system; and 2- the PSTLYEAR

file, which provides information on the best known postal code of the individual.

Ontario Health Insurance Plan (OHIP)

Ontario Health Insurance Plan database contains claims for services provided by eligible

physicians, groups, and laboratories. OHIP database is a well valid and accurate database.

Typically, about 97% of the claims having service dates in a given month arrive within 3

months, 99.7% arrive after 6 months and close to 100% arrive within 1 year. We used OHIP

database to retrieve information on physician billings for CRC tests, counseling and

management of CRC tests, for recent immigrant status and for physician providing the majority

of care.

The Canadian Institute of Health Information- Discharge Abstracts Database (CIHI-DAD)

The CIHI-DAD is a database of information abstracted from hospital records. It includes patient

22

demographic data, acute and chronic hospital care, diagnostics tests, and other administrative

information. The main data elements of the CIHI-DAD are: encrypted patient identifier, patient

demographics (age, sex, residential postal code), diagnoses, procedures, and administrative

information (length of stay, institution number). For the purpose of this study, we used the

CIHI-DAD to identify individuals who were diagnosed with any inflammatory or bowel

diseases prior to the beginning of each annual cohort included in the study.

The Ontario Cancer Registry (OCR)

The OCR database includes all Ontario residents who had cancer or who died from cancer since

1974. The sources of this database are from hospital discharge summaries, pathology reports,

and records from regional cancer centers or Princess Margaret Hospital and from death

certificates. We used OCR to identify and exclude individuals who were diagnosed with CRC

prior to the beginning of each annual cohort covered in this study.

2006 Census Data

The 2006 census contains a short questionnaire (eight questions), completed by 80% of

households in Canada and a long questionnaire with 53 additional questions completed by 20%

of the population. As a result, the 2006 census files at ICES contain aggregated data for Ontario

and Canada that describe the general demographic information on 100% of the population

including age, sex and marital status. The remaining information including income, education

and ethnicity is taken from 20% of the population. We used Census data to identify the

neighborhood characteristics of individuals living in Ontario. The postal code of an individual

on the census was linked to the Dissemination Area (DA) using the Postal Code Conversion File

6+ (described below).

In this study, we used the dissemination area (DA) as the standard geographic area for the

analysis. DA’s have specific criteria that maximize their usefulness for data analysis(Statistics

Canada). The dissemination area (DA) is the smallest standard geographic area for which one

surveyor disseminates all census data thus increasing the reliability during the data collection.

DAs cover all the territory of Canada unlike Census Tract (CT) that covers the metropolitan

areas only. In most cases, DAs are uniform in terms of population size. They have between 400

and 700 individuals. They are stable over time thus can be used to compare between censuses.

DA’s are compact in shape and composed of one or more adjacent dissemination blocks. They

23

can be added together or 'aggregated' to create any of the other standard geographic areas such

as Census subdivisions (CSDs) and census tracts (CTs) (Statistics Canada)

The Postal Code Conversion File is the digital file that provides correspondence between the

Canada Post Corporation (CPC) six character postal code and Statistics Canada’s standard

geographical areas for which census data and other statistics are produced. The link of the postal

codes to the standard geographic areas using PCCF permits the integration of data from various

sources. The PCCF is updated on a regular basis and is released every six months. The PCCF

flags automatically the neighborhood income quintile of the individual and the urban/rural status

(a full description is provided in the section on measuring neighborhood characteristics).

Method

Data linkage

From the RPDB, cohorts of individuals eligible for health coverage in the Province of Ontario

aged 50-74 were identified for each of the following years:

• April 1st, 2005- March 31st, 2006

• April 1st 2006- March 31st 2007




• April 1st 2010 – March 31st 2011.

For each cohort year, the following exclusions were applied:

1- Previous history of colorectal cancer since 1974 using the following codes from the OCR

database

• ICD-9 codes: 153.0-153.4; 153.6-154.1

• ICD-10 codes: C18, C19, C20, C21, C180, C182-C184, C186-C189

2- History of Crohn’s disease and Ulcerative colitis using CIHI database:

• ICD-9 codes: 556, 556.0 to 556.9 and 555, 555.0 to 555.9

• ICD-10 codes: K500, K501, K508 to K515

24

3- Individuals excluded by their physician using OHIP billing code ‘Q142’. The exclusionary

code for colorectal screening Q142 is used for the following:

(i) Enrolled Patients with known cancer being followed by a physician;

(ii) Enrolled Patients with known inflammatory bowel disease;

(iii) Enrolled Patients who have had colonoscopies within five (5) years;

(iv) Enrolled Patients with a history of malignant bowel disease; and

(v) Enrolled Patients with any disease requiring regular colonoscopies for surveillance

purposes.

Using the encrypted numeric identifier (IKN), we linked each cohort on the RPDB list to OCR

to identify and exclude those who had previous colorectal cancer, and to CIHI-DAD for Crohn’s

disease or ulcerative colitis. Further, eligible individuals for screening were linked to OHIP to

identify those who received screening tests in each year from 2005-2011 and those who received

tests in previous five years from the beginning of the cohort.

Using the postal code from RPDB, we linked the cohort to the PCCF file in order to assign the

DA, neighborhood income quintile and the urban/rural status for each individual.

(Appendix 3: Data linkage flow chart)

Study cohorts

First study

For the first study, we were interested in describing the trend of FOBT and endoscopy,

colonoscopy and flexible simgoidoscopy, participation among individuals ‘due’ for screening

from April 1st 2005 till March 31st 2011. We used the physician billing codes to identify and

exclude individuals who received CRC tests prior to the beginning of each annual cohort: we

excluded those who received FOBT in previous 12 months, or a flexible simgoidoscopy and

colonoscopy in previous 48 months using the following codes:

• FOBT: L181 (FOBT prior to CCC) & L179 (after the CCC) G004 (regular medical

check-up), Q152, Q118-Q123

25

• Flexible simgoidoscopy: Z555 (without E740 or E741 or E747 or E705) or Z580

(flexible simgoidoscopy using 60 cm scope)

• Z555 with E740 (flexible simgoidoscopy or colonoscopy to splenic flexure)

• Z555 with E740 and/or E 741 and/or E740 and/or E 741 and/or E 747 (incomplete

colonoscopy )

• Z555 with E740 and E 741 and E 747 and E 705 (colonoscopy into terminal ileum)

In this thesis, we combined colonoscopy and flexible endoscopy into one test and named it

endoscopy. This choice was based on the complexity of billing for colonoscopy seen above.

This complexity stems from the fact that each combination of codes is used incrementally

depending on the section of the colon reached. This resulted in an overlap of fee codes used for

an incomplete colonoscopy and flexible simgoidoscopy (Z555 and E740). Therefore, it is

almost impossible to discern whether the physician billed for a flexible simgoidoscopy or for an

incomplete colonoscopy (Vinden C, 2004).

The exclusions were based on the following criteria:

1- Canadian guidelines recommend a biennial FOBT for average risk individuals. In this

thesis, individuals who received FOBT before 12 months from the beginning of each

fiscal year were kept in the cohort because according to guidelines, they will be due for

another test anytime during that fiscal year.

2- The 5-year cut-off point rather than the 10-year for individuals who received endoscopy

was used for three reasons: first, each of colonoscopy and flexible simgoidoscopy has

different recommendation for repeat test. Flexible simgoidoscopy is recommended every

five years and colonoscopy every ten years for average risk individuals. A 5-year cut off

point is a conservative measure for both tests. Second, other Canadian studies used the 5-

year cut-off point in their definitions, so we used it for comparability(31, 36). Third,

given the small proportion of individuals who received endoscopies in the look back

period between 1995-2001 (1.5-3%), excluding them would not affect our estimates

(Jacob, et al., 2011)

3- Individuals who received endoscopy or barium enema before 48 months from the

26

beginning of each fiscal year were kept because they will be due for another test anytime

during that year according to guidelines.

Second study

For the second study, we were interested in testing the influence of contact with physician and

physician’s recommendation on FOBT participation. We used two annual cohorts following the

CCC to be able to capture the physician’s incentives, which became effective after April 1st

2008. Physician’s recommendation codes were:

• Q150A for distribution of the kit and counseling during an office visit

• Q005A for sending a letter or calling individuals inviting them to take an appointment to

discuss colorectal cancer screening.

In each year, each individual who had at least one contact with a general practitioner/family

physician in Ontario for a core primary service was assigned a physician on OHIP database. We

used this variable to identify an individual who had at least one contact in one year.

Third study

For the third study, we used all six cohorts identified above. We traced individuals who were

due for screening in each quarter of the year. We describe the algorithm for tracing eligibility in

each quarter below.

Definitions of variables

Demographics

Individual characteristics

The RPDB provides individual information on the following variables: age, gender and

eligibility for Ontario Health Insurance plan. We used these variables as follow:

Sex: a dichotomous variable we referred to it as: Female (F) & Male (M)

Age: was originally a continuous variable, we transformed into 5 years interval: 50-55; 56-60;

61-65; 66-70; and 71-74.

27

Recent registrant: In this study, we used recent registration on OHIP as a proxy measure for

recent immigration. It is estimated that about 80% of recent registrants are recent immigrants

(95). Recent registrant on Ontario Health Insurance Plan was calculated based on the date the

individual started to be eligible for OHIP services. An individual whose eligibility for OHIP

started within five years from the beginning of the cohort, i.e. for 2005 cohort, if eligibility

started after the second quarter of the 2000 fiscal year, the individual was considered as recent

registrant; otherwise, if eligibility started before the second quarter of the 2000 fiscal year then

the individual was considered non-recent registrant.

Neighborhood characteristics

Neighborhood Income quintile and quintiles of population by neighborhood income

Since the administrative data used in this study does not provide data on the income of

individuals, the aggregate level was used to measure income commonly referred to as the

Neighborhood Income Quintile. The method developed by Borugian et al. and used by the

PCCF assigned a quintile for a neighborhood income for each dissemination area as

follow(117):

• Census data collects information from 20% of the population on the average household

income and the number of individuals in a household in addition to their postal code.

• Using this information, it calculates the average income per single person equivalent

(SPE) in a dissemination area based on the following formula:

Income per Single Person Equivalent in a DA= average household income x the

number of households/ total number of Single Person Equivalent.

How to derive the SPE? Each household in each DA is single, or two person, or three person

and so on. A single person costs more to live than two or more people living together. To adjust

for the household size, each person in the household is given a weight (multiplier) and the total

number of single person equivalent is the sum of all the person weights in each DA.

Statistics Canada have a more or less standard scale for person weight: “the oldest person in the

family receives a factor of 1.0; the second oldest person in the family receives a factor of 0.4; all

other family members aged 16 and over each receive a factor of 0.4; all other family members

under age 16 receive a factor of 0.3” (S. Canada)

28

• This measure is captured in the PCCF and identified as the (IPPE) or Income Per Person

Equivalent in a DA.

• IPPE for all DA’s are ranked and divided into five quintiles of equal population size.

• Because the cost of living within a large census metropolitan area are comparable within

each census area but not between census areas, in order to create a standard to identify

those living below a “low income cut off (LICO)1 within each area, a quintile of the

neighborhood income is calculated for each aggregation and then pooled across areas.

For example, in Ontario, there are 15 CMA. For each CMA, a quintile of neighborhood

income is derived and then all quintiles are pooled together. This measure is captured in

the PCCF and identified as the QAIPPE (Quintile of neighborhood income per person

equivalent within a CMA, CA or Residual Area). The QAIPPE was used as a measure of

the neighborhood income quintile of the individual in this study.

Urban/ Rural Index

Several definitions for “rural” are available for national and provincial analysis using the

databases at Statistics Canada(du Plessis, 2001). Each definition emphasizes different criteria

such as population size, density and context. In this study, the rural definition used by the PCCF

(du Plessis, 2001) which takes into consideration the size of the community is adopted. A rural

area is defined in terms of a community size < 10,000. The PCCF has the capability of adding a

flag for the corresponding urban/rural status.

1 LICO reflects a consistent and well-defined methodology that identifies those who are substantially worse off than average (Stats Canada). The Canada average family expenditure on food, shelter and clothing is calculated. This is expressed as a percentage of pre-tax income. When families spend 20 percentage points of their income more than the Canadian average on food, shelter and clothing, then these families live below the LICO. The FAMEX data is used to measure the amount spent on these necessities. The income levels, differentiated by size of area of residence, family size become the base year low-income cut-offs. The LICO are updated annually based on the consumer price index.

29

Analyses

For the first study, we used descriptive statistics to measure the rates of CRC testing and logistic

regression to test whether the change in trend over time was significant. For the second study,

we used unadjusted and multivariate log binomial regression to examine the relation between

contact with physician, physician’s recommendation and demographic factors on FOBT

participation. Detailed descriptions for the first and second study are provided in chapter 3 and

4. Herein, we provide a detailed background for the interrupted time series and segmented

regression used in the third study.

Time Series Analysis

Evaluation is an important tool used by policy makers to reveal whether an intervention

achieved its intended objectives and identifies elements for improvement. Different types of

evaluation can be done at different phases of the program and each is associated with its own

method and statistical techniques (121, 122). Choosing the right method and statistical technique

is an important step in the evaluation process(Cook TD, 1979)

Health care evaluation research distinguishes between experimental and natural or quasi-

experimental interventions in choosing the appropriate method and statistical analysis for

evaluation(Cook TD, 1979). In an experimental setting, the randomization between study and

control allows researchers to use randomized controlled trial (RCT) to control for bias. In

natural experiments, where randomization to study and control is not possible, Interrupted Time

Series (ITS) offer the strongest quasi-experimental design to control for potential biases (Cook

TD, 1979; Ray, 1997). Interrupted time series are designed to compare the change in outcome

before and after the introduction of a program (124, 125) using data collected over time for the

outcome. Segmented regression analysis allows estimating in statistical terms the effect of an

intervention on the outcome, immediately and over time (126, 127).

Interrupted time series have been used to evaluate the effectiveness of population-based

programs (P. R. Baker, Francis, Soares, Weightman, & Foster, 2011; Gillings, Makuc, & Siegel,

1981; Glenton, Scheel, Lewin, & Swingler, 2011; R. K. Heyding, A. M. Cheung, E. J. M.

Mocarski, R. Moineddin, & S. W. Hwang, 2005; Morrato, et al., 2009; Novoa, Perez,

Santamarina-Rubio, & Borrell, 2011; Oyo-Ita, Nwachukwu, Oringanje, & Meremikwu, 2011),

the effect of health policies and guidelines on disease outcomes (Ansari, et al., 2003; Kastner, et

30

al., 2011; Mol, et al., 2005; Pande, Ross-Degnan, Zaslavsky, & Salomon, 2011; Peto, et al.,

2008; Rodriguez-Bano, 2010; Valiyeva, Herrmann, Rochon, Gill, & Anderson, 2008; Walthour,

Seymour, Tackett, & Perri, 2010), the impact of natural disasters on the health care system

(Rodgers, St John, & Coleman, 2005; Wicke & Silver, 2009; Yang, Xirasagar, Chung, Huang,

& Lin, 2005) and the effect of disease outbreaks on health system (Chang, et al., 2004; Huang,

Lee, & Hsiao, 2009; Hwang, Cheung, Moineddin, & Bell, 2007; Moineddin, Nie, Domb, Leong,

& Upshur, 2008). Heyding et al. (R. K. Heyding, A. M. Cheung, E. J. Mocarski, R. Moineddin,

& S. W. Hwang, 2005) used interrupted times series to test the effect of community-based

program on mammography utilization among disadvantaged women in Toronto and found that

mammography participation increased from 4.7% pre-intervention to 29.2% post intervention

(p<0.0001). Michielutte et al. (Michielutte, Shelton, Paskett, Tatum, & Velez, 2000) used

interrupted times series to test the effect of a community-based program on mammography

participation and tested the differential effect of this program by age. Moineddin et al.

(Moineddin, et al., 2008) used time series to test the temporal variation of respiratory diseases

on primary care services utilization and found that winter visits are threefold summer visits. In a

systematic review of interrupted times series looking at the effect of media campaign on

screening participation, Grilli et al. and Vidanapathirana et al. (Grilli, Ramsay, & Minozzi,

2002; Vidanapathirana, Abramson, Forbes, & Fairley, 2005) showed that media campaigns have

an immediate but not a long term effect on screening participation.

In this thesis, we used interrupted time series and segmented regression to describe the trend of

FOBT participation over time and to estimate the immediate effect of the Colon Cancer Check

program on the level and trend of FOBT participation after its launch on April 1st 2008.

Interrupted time series design in this study allowed us to control for potential biases expected to

happen in natural experiments including secular trend, seasonality and autocorrelation

(Campbell, 1966; Ramsay, Matowe, Grilli, Grimshaw, & Thomas, 2003). In chapter 5 we

provide a detailed description of how we dealt with those biases. Further, in a segmented

regression, a sufficient number of time points are needed to be able to conduct the analysis,

preferably 12 time points before the intervention and 12 time points after (Wagner, Soumerai,

Zhang, & Ross-Degnan, 2002). Our data had six consecutive years, not enough to conduct a

time series. Therefore, we divided each year into four quarters. By doing so, we managed to get

22 points in total and we were able to provide an estimate effect with statistical power estimated

between 81% and 87% (F. Zhang, Wagner, & Ross-Degnan, 2011).

31

Tracing eligibility and calculating person-month in each quarter

By just observing the data, we realized that early repeats of screening tests is very common.

Early repeat of screening tests without clear indication is also reported in the literature

(Goodwin, Singh, Reddy, Riall, & Kuo, 2011; Ko, Dominitz, Green, Kreuter, & Baldwin,

2010). Not knowing whether early repeats were misuse of tests or for follow-up, we decided to

remove any potential bias that these early repeats may bring into the study by excluding them

from our denominator. Similarly, approximately six thousand individuals die in each quarter and

another 25,000 receive screening tests, hence, excluding them once the event occurred would

make more sense.

We divided each year into 4 quarters. For each quarter, we calculated the person-month

contribution of the individual to the cohort based on the following events: death, receiving any

large bowel test and previous large bowel test. The following algorithm explains how we

measured person-month in the denominator:

• If no prior large bowel test ever, then the individual contribution to the denominator was

3 person-months.

• If death occurred during the quarter, then person-month contribution was calculated as

(death date- beginning of the term) / 30.

• If first large bowel test received during the quarter, then person-months contribution was

calculated as (service date- beginning of the term) / 30.

• If prior colonoscopy, sigmoidoscopy and barium enema were received in:

➢ More than 60 months, then the individual’s contribution to the denominator was 3

person-months.

➢ 48 to 60 months prior to the beginning of the cohort-year, then we identified the quarter

the individual is due for screening again and person-month contribution was calculated as (end

of quarter – date eligible for screening again) /30

➢ Less than 48 months, then the individual is excluded from the analysis.

• If prior FOBT was received in:

32

➢ More than 24 months, the individual’s contribution was 3 person months.

➢ 12- 24 months prior to the beginning of the cohort-year then we identified the quarter the

individual is due again for another test and calculated the person month as (end of quarter-date

eligible for screening again) / 30.

➢ Less than 12 months, then the individual is excluded from the analysis

• Then we summed all person-months for that quarter

(Appendix 5: Person-month calculation flowchart)

33

CHAPTER 3: TRENDS AND DISPARITIES IN COLORECTAL CANCER

SCREENING TESTS PARTICIPATION IN ONTARIO, 2005-2011

ABSTRACT

BACKGROUND: Colorectal Cancer (CRC) is the second leading cause of cancer deaths in

Ontario, Canada. Screening tests are generally underused, and there is evidence of disparities in

utilization. The Colon Cancer Check program, launched on April 1st 2008, is the organized

screening program for CRC in Ontario. The goal of this study is to describe the effect of the

CCC on disparities in CRC participation.

METHOD: This study identified six annual cohorts of individuals aged 50 to 74 eligible for

health coverage in Ontario for each year from April 1st 2005 to March 31st 2011 using the

Registered Persons Database. The cohorts were linked to Ontario Health Insurance Plan for

information on CRC tests, and to 2006 Census from Statistics Canada for information on

neighborhood income and rural community size. The percentage of Fecal Occult Blood Test and

endoscopy participation were calculated using direct age standardization. The proportions of

individuals who were ‘up-to-date’ with screening according to guidelines were calculated using

direct aged standardization. The difference in participation between 2005 and 2011 were

reported as percentage point changes. The statistical significance of the change in trend was

determined using logistic regression. To test for change in disparities, the difference in

percentage points between demographic subgroups were calculated.

RESULTS: The overall increase in FOBT participation between 2005 and 2011 was 5.2%

(Wald χ2 <0.01). The percentage point increase by demographic characteristics ranged from 4%

to 7 % (Wald χ2 <0.01). Higher FOBT participation was consistently reported among females,

high-income, non-recent registrants, individuals aged 65 and above. Rural and urban living

disparity was removed after the CCC. The overall increase in endoscopy participation was 1.3%

(Wald χ2 <0.01). The percentage point increase by demographic characteristics ranged from

0.7% to 1.28% (Wald χ2 <0.01). The disparity gap increased across all sub-groups and ranged

from 0.2% to 1.8%. ‘Up-to-date’ status increased 14.6% from 2005 to 2011 (Wald χ2 <0.01).

The increase in percentage points by demographic characteristics ranged from 13.5% to 16.4%

(Wald χ2 <0.01). Lower ‘up-to-date’ rates were consistently reported among males, low-

34

income, recent immigrants, and individuals aged 50-55. Disparities associated with rural and

urban residence were eliminated after the CCC.

CONCLUSION

Rates of CRC testing have increased significantly over time but have remained suboptimal.

Although the increase occurred in all sub-groups of the population, the disparities persisted. In

2010/11, a large proportion of individuals eligible for screening remain unscreened or not ‘up-

to-date’ with guidelines. Strategies to increase participation among under-users are essential.

Future studies can use our results as a benchmark to monitor disparities over time.

35

BACKGROUND

Colorectal Cancer (CRC) is the second leading cause of cancer deaths in the province of

Ontario, Canada. In 2010, CRC accounted for more than 3,400 deaths and 8,200 new cases

(Statistics, 2011). Screening for colorectal cancer is generally low. In 2004/2005, FOBT

participation was 14.9%, this rate went up to 29.7% in 2007/08 (in two calendar years)(CCC,

2010). Between 1996 and 2005, colonoscopy rates increased from 1.55 to 4.7% (Jacob, et al.,

2011). CRC testing is disparately distributed across geographic areas. Lower participation rates

have been reported among low income, men, adults aged 50-55 and recent-immigrant (CCC,

2010; Krzyzanowska MK, 2009; Ramji, et al., 2005; Vinden C, 2004).

The high incidence rate and the low screening uptake led the government of Ontario to

introduce an organized screening program, the Colon Cancer Check (CCC) program. The CCC,

launched on April 1st 2008, recommends a biennial Fecal Occult Blood Test (FOBT) followed

by a colonoscopy for positive tests. Prior to 2008, opportunistic screening was the dominant

channel for delivering CRC test. Opportunistic screening is a medical practice model targeting

individuals (Rabeneck, 2007). Testing is embedded in routine primary care and occurs when the

physician captures the opportunity to recommend and deliver the test and occasionally on

individuals to request the test (Senore, Armaroli, et al., 2010). In contrast, an organized

screening program is a public health model targeting the population at large who are eligible and

due for screening (Levin, et al., 2011; Rabeneck, 2007; Senore, Malila, et al., 2010). Organized

screening programs are efficient, reduce overuse and improve the quality of screening, while

opportunistic screening is less efficient, contribute to inequalities and have low coverage

(Senore, Malila, et al., 2010)

The goal of this study is to describe the changes in CRC testing rates over time and to examine

the effect of the Colon Cancer Check program on the disparities in testing in Ontario. The three

specific objectives are: 1- to describe the trend of FOBT rates and endoscopy rates between

April 1st 2005 and March 31st 2011; 2- to measure the proportions of individuals who were ‘up-

to-date’ with screening consistent with guidelines between April 1st 2005 and March 31st 2011;

3- to test whether the disparity by demographic characteristics is narrowing over time.

In adaptation of the social ecological model of McLeroy (McLeroy, et al., 1988) to this study,

we posited that by introducing the new health policy (CCC) we expect a change in screening

36

participation among the target population. However, variation in individuals’ characteristics

determines the potential for participation. Some individuals are expected to be more receptive to

screening than others. Hence, demographic characteristics such as age, gender, income, recent

immigration and living in rural neighborhoods may act as effect modifier to overall participation

to screening.

METHOD

Data Sources

This study used administrative databases collected regularly at the Institute for Clinical

Evaluative Sciences at Sunnybrook Health Sciences Centre. After ethics approval from

Sunnybrook Health Sciences Centre and University of Toronto institutional review board, we

had access to the following data sources.

• The Registered Persons Database (RPDB) provided basic demographic information on

those who have ever received an Ontario health card number.

• Ontario Health Insurance Plan (OHIP) database contained claims for services provided

by eligible physicians, groups, and laboratories.

• The Canadian Institute of Health Information-Discharge Abstracts Database (CIHI-

DAD), a database of information abstracted from hospital records, included patient


information.

• The Ontario Cancer Registry (OCR) database included all Ontario residents who

developed cancer since 1974.

• The 2006 census files from Statistics Canada at ICES contained data at the

Dissemination Area level for Ontario population that described the general demographic

information on 100% of the population including age, sex and postal code, and socio-

demographic information on 20% sample of the population including income and education.

Cohort Identification

Using the RPDB, we identified six annual cohorts of individuals aged 50 to 74 eligible for

health coverage in Ontario for each fiscal year from April 1st 2005 to March 31st 2011. To

identify individuals eligible for screening, we used the encrypted numeric identifier at ICES

37

(IKN) and linked it to OCR to exclude colorectal cancer patients, to CIHI-DAD to exclude

Crohn’s and ulcerative colitis patients, and to OHIP for individuals exempted from the tests by

their physicians (Q142)2.

We linked the postal code on the RPDB to the Dissemination Area using the PCCF 6+. The

PCCF flags the neighborhood income quintile and the urban/rural status of the individual.

To identify individuals who received CRC tests prior to each annual cohort, we linked

individuals to OHIP database. We used physician’s billing codes for FOBT, barium enema,

simgoidoscopy and colonoscopy (Appendix 3). The billing codes for FOBT and barium enema

are straightforward. Billing codes for colonoscopy and simgoidoscopy, on the other hand, are

more complex. They require a combination of codes and each combination of codes is

incremental depending on the section of the colon reached. This resulted in an overlap of fee

codes used for an incomplete colonoscopy and flexible simgoidoscopy (Z555 and E740). Since,

it was almost impossible to discern whether the physician billed for a flexible simgoidoscopy or

for an incomplete colonoscopy (Vinden C, 2004), we combined the two tests into one and

named it endoscopy.

To identify individuals who were due for screening, we excluded those who received FOBT less

than 12 months prior to the beginning of the fiscal year, and those who received barium enema

less than 48 months prior to the beginning of the fiscal year. The exclusions were based on the

following criteria:

1- Canadian guidelines recommend a biennial FOBT for average risk individuals.

Individuals who received FOBT before 12 months from the beginning of each fiscal year

were kept in the cohort because according to guidelines, they will be due for another test

2 The exclusionary code for colorectal screening Q142 is used for the following: (i) Enrolled Patients with known cancer being followed by a physician; (ii) Enrolled Patients with known inflammatory bowel disease; (iii) Enrolled Patients who have had colonoscopies within five (5) years; (iv) Enrolled Patients with a history of malignant bowel disease; and (v) Enrolled Patients with any disease requiring regular colonoscopies for surveillance purposes.

38

anytime during that fiscal year.

2- The 5-year cut-off point rather than the 10-year for individuals who received endoscopy

was used for three reasons: first, each of colonoscopy and flexible simgoidoscopy has

different recommendation for repeat test. Flexible simgoidoscopy is recommended every

five years and colonoscopy every ten years for average risk individuals. A 5-year cut off

point is a conservative measure for both tests. Second, other Canadian studies used the 5-

year cut-off point in their definitions, so we used it for comparability(31, 36). Third,

given the small proportion of individuals who received endoscopies in the look back

period between 1995-2001 (1.5-3%), excluding them would not affect our estimates

(Jacob, et al., 2011)

3- Individuals who received endoscopy or barium enema before 48 months from the

beginning of each fiscal year were kept because they will be due for another test anytime

during that year according to guidelines.

It is noteworthy to mention that most claims for FOBT or endoscopy on OHIP, may be

performed for a number of reasons, including screening and diagnosis except the CCC billing

codes (L179, Q152, Q118-Q123), which are exclusively for screening. Regardless of the reason

for the test, any individual receiving any CRC test is considered screened in this study.

Measures

Outcome measures

The two outcome measures used in this study were:

1- Crude and age standardized percentage of individuals due for screening who received the

first CRC test, either FOBT or endoscopy, in each cohort.

2- Crude and age standardized percentage of individuals who were ‘up-to-date’ consistent

with guidelines in each cohort.

We defined ‘up-to-date’ consistent with guidelines as individuals who received at least one

test in each fiscal year in addition to those who received at least one FOBT in previous two

years or those who received an endoscopy or barium enema in previous five years.

39

Demographic variables

The trends of tests participation were stratified by age, gender, neighborhood income quintile,

recent registrant status and urban/rural status.

From the RPDB, we identified the gender and age of individuals in the cohort. Using the postal

code from RPDB, we assigned the Dissemination Area of individuals by linking it to the 2006

Statistics Canada Postal Code Conversion File 6 + (Borugian, et al., 2005). This file derives the

corresponding neighborhood income quintile (higher quintile corresponds to higher income) and

the urban/rural status of each individual in the cohort. Recent registrant in Ontario Health

Insurance Plan was calculated based on the date the individual started to be eligible for OHIP

services. An individual whose eligibility for OHIP started within five years from the beginning

of the cohort (i.e. for 2005 cohort, if eligibility started in the second quarter of the 2000 fiscal

year and onward) was considered as recent registrant. Otherwise, if eligibility started more than

five years before the beginning of the cohort then the individual was considered non-recent

registrant. Recent registrant is used as a proxy measure for recent immigrant status in Ontario.

It is estimated that 80% of recent registrant to be recent immigrant (Lofters, Glazier, Agha,

Creatore, & Moineddin, 2007).

(Appendix 6: Definition of outcome measures)

Statistical Analyses

All data were analyzed using SAS software 9.2. (SAS Institute, Cary, North Carolina). We used

descriptive statistics to measure the rates of participation by demographic characteristics. We

calculated the percentage of individuals who completed their index FOBT or endoscopy per

cohort year (April 1st to March 31st) stratified by gender, age, income, recent registrant and

urban/rural status.

To compare rates of participation over the 6 years period, we used age-standardized rates of

FOBT and endoscopy participation and their 95% confidence interval for every year. We

standardized percentages to the 1991 Canadian Census population using direct standardization

method. We chose the 1991 Census population to be able to compare them to other Canadian

reports that used the same Census (CCC, 2010; Krzyzanowska MK, 2009; Statistics, 2011). We

measured the difference in participation between 2005 and 2011 and reported the percentage

40

point changes and their 95% confidence intervals.

To test the statistical significance of the overall change in rates between 2005 and 2011 and the

change in rates for each subgroup of the population, we used logistic regression. The model for

the overall trend included age in 5-year period to control for changes in the age structure over

time and the year the test was taken. Separate models were repeated for each subgroup category

(e.g. for females, we run a separate model including females only). The Wald Chi-square

statistic was used to test the significance (Carrie N. Klabunde, et al., 2011). Statistical

significance was two sided, a p<0.05 was considered significant.

To test for change in disparities in participation by demographic characteristics (e.g. females vs.

males), we calculated the difference in percentage points between subgroups, their confidence

intervals, and their Z value. We compared the Z value to a two sided Z table and a p<0.05 was

considered significant.

To describe the trend of ‘up-to-date’ with screening over time, we calculated the crude rates, the

age standardized rates and their 95% confidence interval in any given year. We stratified the

‘up-to-date’ status by subgroups. We tested the statistical significance of the overall change in

rates and stratified rates using logistic regression. To test for change in disparities, we measured

the difference in percentage point, their p values and their confidence interval as described

above.

RESULTS

From 2005 to 2011, the six cohorts of eligible individuals who were due for screening each year

and their breakdown distribution by socio-demographic characteristics are in table 3.1. All six

cohorts were equally distributed by gender and income; on average, 35% were between the age

of 50 and 55 and 10% between 71 and 74; 5% were recent registrants on OHIP and 14% were

living in rural areas.

Trends in FOBT participation

FOBT participation rates were on the rise between 2005 and 2011 (Table 3.2). There was a peak

in 2008/09 cohort, the year the CCC was launched, followed by a slight decline in participation

between 2009/10 and 2010/11. The overall increase between 2005 and 2011 was 5.2%, or an

41

estimated 155,000 more FOBT tests in 2011 than in 2005. The increase in trend was significant

(Wald χ2 <0.01).

The increase in trend was apparent across all subgroups and ranged from 4% to 7 % (Table 3.2).

All percentage point changes were significant (Wald χ2 <0.01). The trend of increase among

males was lower than females (4.6% and 5.9% respectively) and the disparity increased by 1.3%

between 2005 and 2011 (p<0.05) (Figure 3.1.A). The increase in participation by income ranged

from 4.9% to 5.6%. The disparity between low and high-income quintile was reduced by 0.2%

but this decrease was not significant (p=0.79) (Figure 3.1.B). The increase in participation

among recent registrant was 4.2 compared to 5.2 among non-recent registrant. The disparity

between recent and non-recent increased by 1% between 2005 and 2011 (p <0.05) (Figure

3.1.C). The increase in participation in rural area surpassed the participation in urban area (7%

and 5.1 respectively). The rural/urban difference changed from -1.4% in 2005 to 0.53% in 2011,

a 1.9% increase in participation in rural area (p< 0.05) (Figure 3.1.D).

With respect to age, the results of the crude rates are presented in table 3.3. The increase in

percentage point for the different age groups ranged from 4.5% (71-74) to 8.4% (66-70),

significant for all age groups. The disparity gap was reduced from -2.5% to -1.6% (0.9%,

p<0.05) but persisted between youngest adults (50-55) and oldest (71-74) (Figure 3.2).

Trends in endoscopy participation

The trend of endoscopy participation is presented in table 3.4. The increase in overall trend in

endoscopy participation was more modest than for FOBT (1.3%, Wald χ2 <0.01). The increase

was significant across all subgroups and ranged from 0.7% to 1.28% (Wald χ2 <0.01). The

disparity increased across all sub-groups and ranged from 0.2% to 1.8%. Females, high income,

non-recent registrants, individuals living in urban areas and individuals aged 50-55 (table 3.5)

had higher increase in participation.

Trends in ‘up-to-date’ status

The standardized rates for ‘up-to-date’ with CRC screening and their breakdown by gender,

income, recent registrant and urban/rural status are presented in table 3.6. The overall increase

in percentage point was 14.6%, a significant increase from 2005 to 2011 (Wald χ2 <0.01). In

42

2010/11, there were about two million individuals eligible for screening that either did not

receive any test or were not ‘up-to-date’ consistent with guidelines.

The increase in percentage points was also noted across all subgroups and ranged from 13.5% to

16.4% (Wald χ2 <0.01) (Table 3.6). The increase in trend among males was lower than among

females (13.8% and 15.5% respectively); the disparity increased by 1.7% between 2005 and

2011 (p<0.05). The increase in ‘up-to-date’ status by income ranged from 13.8% to 15.8%. The

disparity between low and high-income quintile increased slightly (0.6%, p<0.05). In 2010/11,

low-income were 10% less likely to be ‘up-to-date’ than high-income individuals (38.3% and

48.1% respectively). The increase in ‘up-to-date’ status among recent immigrants was less than

non-recent immigrants (13.5% and 14.5% respectively); the disparity increased by 1.1% from

2005 to 2011 (p <0.05). In 2010/11, recent immigrants were 12% less likely to be ‘up-to-date’

than non-recent immigrants (32.3% and 44.2% respectively). The increase in percentage point in

‘up-to-date’ status in residents of rural areas was higher than residents of urban areas (16.4%

and 14.6% respectively). The rural/urban difference changed from -2.1% in 2005 to -0.3% in

2011, a 1.8% increase in participation in rural area (p< 0.05) and the disparity was removed in

2010/11.

With respect to age, the results of the crude rates are presented in table 3.7. The increase in

percentage point for the different age groups ranged from 13.5% (50-55) to 21.8% (71-74),

significant for all age groups (Wald χ2 <0.01). The disparity increased from -11.1% to -19.4%

(8.3%, p<0.05).

DISCUSSION

This study showed that rates of CRC testing have increased significantly between 2005 and

2011. In 2010/2011, 44% of individuals eligible for screening were ‘up-to-date’ with CRC

screening compared to 29% in 2005 (Age standardized rate, Table 3.6). Most CRC testing was

undertaken with FOBT. Between 2005 and 2011, FOBT participation increased 5.2 percent

point; the increase in endoscopy participation among those eligible for screening was more

modest (1.3 percent point).

Over the period from 2005 to 2011, we observed a statistically significant increase in CRC

testing among all sectors of the population including under-users i.e. males, individuals aged 50-

55, low-income, recent registrant, and individuals living in rural areas. However, the disparity

43

remained significant for most demographic characteristics after the CCC. Noticeably, the

disparity in ‘up-to-date’ with screening increased significantly by age group (8.3%), varied

slightly by sex (1.7%), income (0.6%), and recent registrant (1.1%) and almost disappeared for

urban/rural. FOBT participation also increased significantly for all sub-groups including under-

users. The disparity remained the same after the CCC by sex, age, income and recent registrant.

The urban/rural gap narrowed significantly over time.

From a policy perspective, we were interested to analyze the effect of the CCC on disparities in

participation. Two and a half years after its launch, did the CCC contribute to more inequalities

in CRC testing? The good news is that the CCC did not increase inequality. We saw that the

increase in FOBT participation was parallel across most sectors of the population (Figure 3.1).

But, the CCC did not narrow the disparities either. Males, low-income, recent registrants and

individuals aged 50-55 remained the under-users of FOBT. Other studies looking at short-term

effect of organized screening programs on CRC testing (von Wagner, et al., 2009), cervical

cancer testing (D. Baker & Middleton, 2003), and breast cancer testing (Gatrell, Garnett, Rigby,

Maddocks, & Kirwan, 1998) showed that disparities by income, ethnicity and gender persisted

after the implementation of these programs. Long-term evaluation of these programs, however,

showed that these disparities decreased over time. Moss et al. showed that gender differences in

CRC screening in UK after the third round of invitations was decreasing but income and

ethnicity gaps were persisted (Moss, et al., 2011). Baker et al. found that after ten years (1991-

2001), a provider-led organized screening program for cervical cancer reduced the socio-

economic inequality in incidence and mortality from the disease in England (D. Baker &

Middleton, 2003). One explanation for these findings may relate to the ‘inverse equity

hypothesis’ proposed by Victora et al.(Victora, et al., 2000). The hypothesis postulates that the

most advantaged in the society make greater and earlier use of the program. Therefore,

inequities at the beginning of a program may get worse, or remain the same at the best.

However, over time the most advantaged reach a level of improvement beyond which it is

unlikely to make progress, and the least advantaged begin to catch-up, this is when equity

improves (Victora, et al., 2000). This hypothesis has been proven correct in the cervical cancer

screening program in UK (D. Baker & Middleton, 2003). Future studies looking at the long-

term effect of the CCC on the trends of inequities in participation will need to test Victora’s

hypothesis.

44

The rates of CRC tests we found in this study remind us of the rates when the organized breast

cancer-screening program in Ontario was first launched. In 1990, the rates of mammography in

Ontario were 40% compared to 72% in 2008. But it took twenty years of relentless efforts,

multiple strategies and political determination to reach that level. We now have the opportunity

to increase the trend of CRC testing in Ontario. Several strategies are needed to achieve

meaningful advances in the overall population. But additional efforts are required to reduce

disparities. Tailored interventions targeting under-users knowledge, attitudes and perceived

needs for screening are required.

Individuals aged 50-55 had the lowest FOBT participation rates and the lowest ‘up-to-date’

status as compared to other age groups(Statistics, 2011) (CCC, 2010; Christman, et al., 2004;

Ramji, et al., 2005; Tessaro, et al., 2006). Naturally, healthy middle-aged individuals have less

contact with the health care system and have less perceived risk for the disease (Kenkel, 1994).

Their lack of awareness combined with low perceived risk for the disease could be addressed

through education. Health education using behavioral modification models (health belief model

or the trans-theoretical model) would change their perceptions and increase their likelihood for

participation(Menon, Belue, Sugg Skinner, Rothwell, & Champion, 2007). Given they have less

contact with the health care system, using mass media to diffuse the knowledge about screening

would be effective for this age group (Randolph & Viswanath, 2004)

Females are more likely to have an FOBT than males (Janda, Hughes, Auster, Leggett, &

Newman, 2010; Shim, et al., 2010; Steele, et al., 2009). The increase in likelihood may be due

to the fact that women are faster in adopting new health behaviors than men (Janda, et al.,

2010), have more contacts with the health care system for various health concerns (Ramji, et al.,

2005) or participate more in other preventive services (Shapiro, et al., 2001; Slattery, et al.,

2004) . Having integrated screening programs including breast, cervical and colorectal cancer is

a successful strategy to increase screening among women(CCO, 2008). Similar initiatives are

needed for men. Combining prostate cancer screening, blood pressure and diabetes screening

with colorectal cancer may increase the likelihood of participation among men. Worksite

screening programs would be a potentially strategic intervention to target men in the workforce

cohort (Lemon, et al., 2009; Menon, et al., 2003; Tilley, et al., 1997; Watts, Vernon, Myers, &

Tilley, 2003; J. M. Zapka, Lemon, Magner, & Hale, 2009).

45

Several factors in Ontario reduce socio-economic inequalities on CRC testing. The CCC incurs

no financial cost on the individual, access to a regular source of care is not a major concern in

Ontario (Sanmartin, 2003), yet inequities in test participation exist. Galobardes et al. indicates

that individuals with higher income may have better access to quality material resources, and

improved health services, which increases their likelihood for screening (Galobardes, et al.,

2006). Recent arguments indicate that information technology may have widened the income

equality gap. Higher income individuals harness the benefits of health information available on

the internet better than low-income (von Wagner, et al., 2009). Interventions effective in

reducing socio-economic disparities include equipping providers serving low-income

neighborhoods pamphlets and videotapes designed for motivating and educating low literacy

patients and simplified instructions for completing the test(Fitzgibbon, et al., 2007). Other

recommendations targeted structural barriers such as after-hours accessibility to primary care

services, reducing traveling distances to get screened, and using client incentives (Brouwers, et

al., 2011).

Recent immigrants were less likely to participate in screening as compared to non-recent

immigrant groups (Shih, et al., 2008) due to lack of access to a regular source of care (Talbot,

Fuller-Thomson, Tudiver, Habib, & McIsaac, 2001), low socioeconomic status (Shokar, et al.,

2008), low level of health literacy (Kreps & Sparks, 2008) , their inability to speak the official

language (Thomson & Hoffman-Goetz, 2010) and their lack of acculturation. Using patient

navigator to assist individuals booking an appointment is important for linguistically challenged

recent immigrants (Christie, et al., 2008; Jandorf, Gutierrez, Lopez, Christie, & Itzkowitz,

2005). Addressing fatalism and increasing the perceived value of screening through education is

another approach to engage recent immigrants(Makoul, et al., 2009) (Powe & Weinrich, 1999).

One-to-one education with an educator who is culturally and linguistically appropriate is

effective in increasing screening participation among ethnically diverse communities(Brouwers,

et al., 2011).

LIMITATIONS

There were limitations to this study. The Ontario Health Insurance Plan (OHIP) administrative

data used to identify individuals who received an FOBT or endoscopy do not discern between

tests done for diagnostic and those done for screening, especially for FOBT tests done before the

46

CCC (2008). We included all physician billings and considered them as screening in this study.

Therefore, our rates may be slightly higher than the actual screening rates.

Further, billing on OHIP does not include providers outside the fee for service system, which

affects three geographic areas in Ontario (Fontenac county, Kenora and Sault St. Marie) and

salaried providers in community health centers. In this study, we used the laboratory requisition

form in addition to physician’s billing fee codes to identify individuals who received an FOBT.

Therefore, we were assured that we captured almost all FOBT tests regardless of the billing

physician or the geographic region. For endoscopies, since the rates of participation for these

tests are relatively very low in the province, this data limitation would not have a major impact

on the endoscopy estimates.

Attributing an ecological level measure such as income to individuals may be considered by

epidemiologist as a limitation due to two validity issues: measurement error and construct

validity. From an analytical perspective, using an ecological level data as proxy for individual

characteristics is associated with an increased random measurement error due to the

heterogeneity within the group, which usually leads to attenuation of the effect of this measure

on the outcome as compared to an individual level data (Greenland & Morgenstern, 1989). But,

this approach has been validated using Canadian database and is widely used by researchers in

the province (Mustard, Derksen, Berthelot, & Wolfson, 1999). Moreover, in this study we used

the smallest geographic area, the dissemination area, to measure the neighborhood income

quintile. Reducing the community size leads to reduction in the measurement error. As for

construct validity, we tried to answer the following questions: are we measuring what we are

supposed to measure? We think we do. Our interest in income in this study is not to examine

what the income of the individual can do for screening participation (although important) but we

are interested to identify the impact of lack of resources within the social and physical

environments of less advantaged individuals on screening participation (Mustard, et al., 1999)

and compare them with individuals living in wealthier environments.

CONCLUSION

In conclusion, colorectal cancer screening in Ontario has increased between 2005 and 2011. The

increase reached all sectors of the population but the disparities in participation persisted even

after the implementation of the organized screening program. A large proportion of eligible

47

individuals for screening remained unscreened or not up-to-date with guidelines. Several

strategies were proposed to increase participation among under-users. In this study, we captured

the effect of the Colon Cancer Check program on disparities in participation. In the third study

of this study, we examine the effect of the CCC on the trend of participation. Future studies can

use this study as a benchmark to monitor the trend of inequities in CRC testing in Ontario.

48

Figures for chapter 3

Figure 3.1. Age standardized percent of FOBT participation by demographic characteristics, Ontario, 2005-2011 A. Sex B. Income

C. Recent registrant D. Rural/Urban

49

Figure 3.2. Percent of FOBT participation by age, Ontario, 2005-2011

50

Tables for chapter 3

Table 3.1: Population due for colorectal cancer screening by demographic characteristics, Ontario, 2005-2011

2005/06

2006/07 2007/08 2008/09 2009/10 2010/11

N 2,468,566 2,472,113 2,450,372 2,442,384 2,291,929 2,479,600

Sex Females 1,239,728 (50%)

1,236,667 (50%)

1,221,388 (50%)

1,213,790 (50%)

1,127,955 (49%)

1,225,228 (49%)

Males 1,228,838 (50%)

1,235,446 (50%)

1,228,984 (50%)

1,228,594 (50%)

1,163,974 (51%)

1,254,372 (51%)

Age 50-55 887,032 (36%)

897,650 (36%)

900,686 (37%)

905,139 (37%)

859,714 (38%)

922,740 (37%)

56-60 583,243 (24%)

594,474 (24%)

596,769 (24%)

579,996 (24%)

535,892 (23%)

577,532 (23%)

61-65 420,219 (17%)

420,091 (17%)

413,602 (17%)

429,839 (18%)

411,532 (18%)

455,153 (18%)

66-70 337,535 (13%)

329,608 (13%)

318,723 (13%)

314,264 (13%)

290,338 (13%)

317,972 (13%)

71-74 240,537 (10%)

230,290 (9%)

220,592 (9%)

213,146 (9%)

194,453 (8%)

206,203 (8%)

Income * Quintile 1 (low)

481,460 (20%)

481,746 (19%)

476,697 (20%)

476,965 (19%)

450,121 (20%)

484,473 (20%)

Quintile 2 499,559 (20%)

498,460 (20%)

491,520 (20%)

488,630 (20%)

456,403 (20%)

494,697 (20%)

Quintile 3 477,430 (19%)

479,325 (19%)

475,465 (20%)

473,734 (19.4%)

442,852 (20%)

478,616 (20%)

Quintile 4 486,311 (20%)

487,416 (20%)

483,537 (20%)

482,077 (20%)

450,827 (20%)

488,988 (20%)

Quintile 5 (high)

509,883 (21%)

507,241 (21%)

500,358 (21%)

493,136 (20%)

459,949 (20%)

495,385 (20%)

Recent registrant

< 5 years 123,851 (5%)

149,230 (6%)

170,495 (7%)

124,013 (5%)

113,220 (5%)

116,008 (5%)

> 5 years 2,344,715 (95%)

2,322,883 (94%)

2,279,877 (93%)

2,318,371 (95%)

2,178,709 (95%)

2,363,592 (95%)

51

Rural/ Urban *

Rural 341,480 (14%)

339,386 (14%)

334,829 (14%)

335,627 (14%)

310,013 (14%)

325,807 (13%)

Urban 2.118,789 (86%)

2,120,433 (86%)

2,098,423 (86%)

2,084,871 (86%)

1,956,057 (86%)

2,124,657 (87%)

On April 1st of each year, individuals who received FOBT in the previous year or those who received endoscopy or barium enema in the previous four years were excluded. * May not add up to total population due to missing values.

52

Table 3.2: Age standardized§ percent of Fecal Occult Blood Test participation* by demographic characteristics, Ontario, 2005-2011

2005/06 (95% CI)

2006/07 (95% CI)

2007/08 (95% CI)

2008/09 (95% CI)

2009/10 (95% CI)

2010/11 (95% CI)

Percentage point change# 2005-2011

Overall 6.9% (6.8-6.9)

9% (8.9-9.1)

10.2% (10.1-10.3)

13.6% (13.5-13.6)

11.8% (11.7-11.8)

12.1% (12.1 -12.2)

5.2% a (5.12-5.28)

Sex

Males 6.5% (6.4-6.5)

8.5% (8.4-8.5)

9.6% (9.5-9.6)

12.6% (12.5-12.6)

10.8% (10.7-10.8)

11.1% (11-11.1)

4.6%a

(4.5-4.6)

Females 7.3% (7.2-7.4)

9.5% (9.5-9.6)

10.9% (10.8-10.9)

14.6% (14.5-14.7)

12.8% (12.7-12.9)

13.2% (13.2-13.3)

5.9%a (5.8-6)

Difference (M – F)

-0.8% (-0.9- -0.7)

-2.1% (-2.2- -1.9)

-1.3% a (-1.4- -1.1)

Income

Quintile 1 (low)

5.6% (5.5-5.5)

7.7% (7.6-7.8)

8.76% (8.6-8.7)

12% (11.9-12.1)

10.2% (10.1-10.3)

10.7% (10.6-10.8)

5.1%a (5.06-5.24)

Quintile 2 6.7% (6.6-6.7)

8.9% (8.8-8.9)

10.2% (10-10.2)

13.7% (13.6-13.8)

11.7% (11.6-11.8)

12.2% (12.1-12.3)

5.5%a (5.4-5.6)

Quintile 3 7.1% (7-7.2)

9.3% (9.2-9.4)

10.6% (10.5-10.7)

14.2% (14.1-14.3)

12.3% (12.2-12.4)

12.7% (12.6-12.8)

5.6%a (5.5-5.8)

Quintile 4 7.5% (7.4-7.5)

9.7% (9.6-9.8)

11% (10.9-11.4)

14.5% (14.4-14.6)

12.8% (12.6-12.9)

13.1% (12.9-13.2)

5.6%a (5.5-5.7)

Quintile 5 (high)

7.7% (7.6-7.8)

9.7% (9.6-9.8)

10.9% (10.8-11)

14% (13.9-14.1)

12.6% (12.5-12.7)

12.6% (12.5-12.7)

4.9%a (4.7-5)

Difference (Q1-Q5)

-2.1% (-2.2- -1.9)

-1.9% (-1.9 - -1.7)

0.2%b (-0.3-0.8)

Recent Registrant

< 5 years 5.5% (5.4-5.6)

7.7% (7.6-7.9)

8.9% (8.7-9)

11.5% (11.3-11.6)

10.3% (10.1-10.5)

9.7% (9.6-9.94)

4.2%a (4.1-4.5)

> 5 years 7% (6.9-7)

9.1% (9-9.1)

10.3% (10.2-10.3)

13.7% (13.6-13.7)

11.9% (11.8-11.9)

12.2% (12.2-12.3)

5.2%a (5.20-5.30)

53

Difference (Recent-non-recent)

-1.5% (-1.6- -1.3)

-2.5% (-2.6- -2.3)

-1%a (-1.2 - -0.7)

Rural/ Urban

Rural 5.7% (5.6-5.8)

7.9% (7.8-8)

8.6% (8.5-8.7)

12.4% (12.3-12.5)

12.1% (12-12.2)

12.7% (12.6-12.8)

7 %a (6.8-7.2)

Urban 7.1% (7-7.2)

9.2% (9.2-9.3)

10.5% (10.5-10.6)

13.9% (13.8-13.9)

11.9% (11.8-11.9)

12.2% (12.1-12.2)

5.1%a (5-5.1)

Difference (Rural- urban)

-1.4% (-1.5- -1.3)

0.5% (0.4-0.6)

1.9% a (1.7-2.1)

§ Direct standardization to the 1991 census population. * The first test received in a given year (FOBT) was counted in this table. # Statistical significance was tested using logistic regression. Significant if Wald Chi-square < 0.05 a= significant (p<0.05) b= not significant (p>0.05)

54

Table 3.3: Percent of Fecal Occult Blood Test * by age group, Ontario, 2005-2011 2005/06

(95% CI)

2006/07 (95% CI)

2007/08 (95% CI)

2008/09 (95% CI)

2009/10 (95% CI)

2010/11 (95% CI)


Total

population 2,468,566

2,472,113 2,450,372 2,442,384 2,291,929 2,479,600

FOBT N (%)

202,494 (8.2%)

263,727 (10.7%)

296,179 (12.1%)

392,036 (16%)

320,542 (14%)

355,832 (14.4%)

Age

50-55 59,884 (6.7%)

78,474 (8.7%)

90,774 (10.1%)

121,168 (13.4%)

102,993 (12%)

112,453 (12.1%)

5.4%a (5.3-5.5)

56-60 47,335 (8.1%)

63,159 (10.6%)

71,398 (12%)

91,768 (15.8%)

73,778 (13.7%)

81,624 (14.1%)

6%a (5.9-6.1)

61-65 39,557 (9.4%)

51,671 (12.3%)

56,932 (14%)

78,298 (18.2%)

65,181 (15.8%)

74,898 (16.5%)

7.1%a (7-7.2)

66-70 33,579 (10%)

42,966 (13%)

47,485 (14.9%)

62,701 (20%)

49,132 (17%)

58,557 (18.4%)

8.4%a (8.2-8.6)

71-74 22,139 (9.2%)

27,457 (11.9%)

29,590 (13.4%)

38,101 (18%)

29,458 (15.2%)

28,300 (13.7%)

4.5%a (4.3-4.7)

Difference ((50-55)-(71-74)

-2.5% (-2.6 - -2.4)

-1.6% (-1.7- -1.4)

0.9%a (0.6-1.1)

* Multiple tests may have been given to the same individual but the first test received in a given year (FOBT) was counted in this table. Individuals who received FOBT in previous year or those who received endoscopy or barium enema in previous four years were excluded. # Statistical significance was tested using logistic regression. Significant if Wald Chi-square < 0.05 Percent of individual who received FOBT in each age group a= significant (p<0.05) b= not significant (p>0.05)

55

Table 3.4: Age standardized percent endoscopy participation * test by demographic characteristics, Ontario, 2005-2011

2005/06 (95% CI)

2006/07 (95% CI)

2007/08 (95% CI)

2008/09 (95% CI)

2009/10 (95% CI)

2010/11 (95% CI)

Percentage point

change 2005-2011

Overall 2.9%

(2.9-3)

3.4%

(3.3-3.4)

4.1%

(4-4.1)

4.3%

(4.2-4.3)

4.3%

(4.3-4.4)

4.2%

(4.1-4.2)

1.3%a

(1.2-1.4)

Sex

Males 2.8% (2.8-2.9)

3.3% (3.2-3.3)

3.9% (3.8-3.9)

4.1% (4.1-4.2)

4.1% (4-4.1)

4% (3.9-4)

1.2%a (1.1-1.3)

Females 3% (3-3.1)

3.5% (3.5-3.6)

4.2% (4.2-4.3)

4.4% (4.4-4.5)

4.5% (4.5-4.6)

4.4% (4.3-4.4)

1.4%a (1.3-1.5)

Difference (F-M)

-0.2% (-0.34- -0.)

-0.4% (-0.4- -0.3)

-0.2%a (-0.3—0.05)

Income

Quintile 1 (low)

2.1% (2.1-2.2)

2.4% (2.4-2.5)

2.9% (2.8-2.9)

3.1% (3-3,2)

3.2% (3.1-3.3)

3.1% (3.1-3.2)

1%a (0.9-1.1)

Quintile 2 2.5% (2.4-2.5)

2.8% (2.8-2.9)

3.4% (3.4-3.5)

3.7% (3.6-3.7)

3.7% (3.7-3.8)

3.7% (3.6-3.7)

1.2%a (1.1-1.2)

Quintile 3 2.8% (2.7-2.9)

3.3% (3.2-3.3)

3.9% (3.9-4)

4.2% (4.1-4.3)

4.3% (4.2-4.3)

4.1% (4.1-4.2)

1.3%a (1.1-1.4)

Quintile 4 3.2% (3.1-3.3)

3.7% (3.7-3.8)

4.5% (4.5-4.6)

4.8% (4.7-4.9)

4.8% (4.7-4.9)

4.7% (4.6-4.7)

1.5%a (1.4-1.6)

Quintile 5 (high)

4% (3.9-4.1)

4.8% (4.7-4.9)

5.6% (5.6-5.7)

5.8% (5.7-5.9)

5.7% (5.7-5.8)

5.4% (5.3-5.5)

1.4%a (1.3-1.5)

Difference (Q1-Q5)

-1.9% (-2 - -1.7)

-2.3% (-2.4 - -2.1)

-0.4%a

(-0.6- -0.2)

Recent Registrant

< 5 years 1.7% (1.6-1.8)

1.9% (1.8-1.9)

2.3% (2.2-2.4)

2.4% (2.3-2.5)

2.6% (2.5-2.7)

2.4% (2.3-2.5)

0.7%a (0.5-0.8)

> 5 years 3% (2.9-3)

3.5% (3.4-3.5)

4.2% (4.1-4.2)

4.4% (4.3-4.4)

4.4% (4.3-4.4)

4.2% (4.2-4.3)

1.2%a (1.2-1.4)

56

Difference (recent-non_recent)

-1.3% (-1.4 - -1.1)

-1.8% (-1.9- -1.7)

-0.5%a

(-0.75 – -0.4)

Rural/ Urban

Rural 3.1% (3-3.1)

3.6% (3.5-3.6)

4.1% (3.9-4.1)

4.4% (4.3-4.4)

4.3% (4.3-4.4)

4.1% (4-4.2)

1%a (0.9-1.1)

Urban 2.9% (2.9-3)

3.4% (3.3-3.4)

4% (3.9-4.1)

4.3% (4.3-4.4)

4.3% (4.3-4.4)

4.2% (4.2-4.3)

1.3%a (1.2-1.4)

Difference (Rural-urban)

0.2% (0.1-0.3)

-0.1% (-0.2- 0.0)

-0.3%a

(-0.4- -0.1)

* Multiple tests may have been given to the same individual but the first test received in a given year (colonoscopy or simgoidoscopy), was counted in this table. # Statistical significance was tested using logistic regression. Significant if Wald Chi-square < 0.05 a= significant (p<0.05) b= not significant (p>0.05)

57

Table 3.5: Percent of endoscopy participation* by age group, Ontario, 2005-2011 2005/06

(95% CI)

2006/07 (95% CI)

2007/08 (95% CI)

2008/09 (95% CI)

2009/10 (95% CI)

2010/11 (95% CI)


Total population

2,468,566

2,472,113 2,450,372 2,442,384 2,291,929 2,479,600

Endoscopy N (%)

88,082 (3.6%)

102,637 (4.1%)

121,753 (5%)

128,770 (5.3%)

121,228 (5.3%)

128,531 (5.2%)

Age

50-55 29,447 (3.3%)

35,346 (3.9%)

42,921 (4.7%)

46,357 (5.1%)

44,347 (5.1%)

48,654 (5.3%)

2%a (1.9-2.1)

56-60 22,064 (3.8%)

25,940 (4.4%)

31,399 (5.3%)

31,803 (5.5%)

28,787 (5.4%)

30,990 (5.4%)

1.6%a (1.5-1.7)

61-65 16,054 (3.8%)

18,981 (4.5%)

22,226 (5.4%)

24,282 (5.7%)

23,165 (5.6%)

25,213 (5.5%)

1.7%a (1.6-1.8)

66-70 12,540 (3.7%)

13,880 (4.2%)

15,821 (4.9%)

16,483 (5.2%)

15,627 (5.4%)

16,464 (5.2%)

1.5%a (1.4-1.6)

71-74 7,977 (3.3%)

8,490 (3.7%)

9,386 (4.3%)

9,845 (4.6%)

9,302 (4.8%)

7,210 (3.5%)

0.2%a (0.1-0.3)

Difference ((50-55)-(71-74)

0% (-0.1 – 0.1)

1.8% (1.7-1.9)

1.8%a (1.6-2)

* Multiple tests may have been given to the same individual but the first test received in a given year (colonoscopy or simgoidoscopy), was counted in this table. Individuals who received FOBT in previous year or those who received endoscopy or barium enema in previous four years were excluded. Percent of individual who received endoscopy in each age group # Statistical significance was tested using logistic regression. Significant if Wald Chi-square < 0.05 a= significant (p<0.05) b= not significant (p>0.05)

58

Table 3.6: Age standardized percent of ‘up-to-date’ status by demographic characteristics, Ontario, 2005-2011

2005/06 (95% CI)

2006/07 (95% CI)

2007/08 (95% CI)

2008/09 (95%CI)

2009/10 (95% CI)

2010/2011 (95% CI)

Percent point change#

2005-2011

Overall 29.2%

(29.1-29.2)

33.3%

(33.2-33.4)

37.1%

(37-37.1)

41.9%

(41.8-41.9)

43.6%

(43.5-43.7)

43.8%

(43.7-43.9)

14.6%a

(14.5-14.7)

Sex

Males 27.7% (27.6-27.8)

31.6% (31.5-31.6)

35.2% (35.1-35.3)

39.8% (39.7-39.9)

41.4% (41.3-41.5)

41.5% (41.4-41.6)

13.8%a (13.7-13.9)

Females 30.6% (30.5-30.7)

35% (34.9-35)

38.9% (38.7-38.9)

43.9% (43.7-44)

45.8% (45.6-45.9)

46.1% (46-46.2)

15.5%a (15.4-15.6)

(F-M) -2.9% (-3.1- -2.8)

-4.6% (-4.7- -4.5)

-1.7%a (-1.8- -1.5)

Income

Quintile 1 (low)

24.5% (24.3-24.6)

28.1% (28-28.2)

31.4% (31.3-31.5)

36.1% (36-36.3)

37.8% (37.6-37.9)

38.3% (38.2-38.4)

13.8%a (13.7-14)

Quintile 2

27.3% (27.1-27.4)

31.3% (31.1-31.4)

35% (35-35.2)

40.1% (40-40.3)

41.8% (41.7-42)

42.2% (42-42.3)

14.9%a (14.7-15.1)

Quintile 3

29.1% (29-29.2)

33.3% (33.1-33.4)

37.2% (37-37.3)

42.3% (42.1-42.4)

44.1% (43.9-44.2)

44.4% (44.3-44.6)

15.3%a (15.1-15.5)

Quintile 4

30.9% (30.7-31)

35.2% (35.1-35.4)

39.3% (39.2-39.4)

44.3% (44.1-44.4)

46.1% (46-46.3)

46.4% (46.2-46.5)

15.5%a (15.4- 15.7)

Quintile 5 (high)

33.7% (33.5-33.8)

38.1% (37.9-38.2)

42% (41.8-42.1)

46.5% (46.4-46.7)

48.3% (48.1-48.4)

48.1% (48%-48.3)

14.4%a (14.2-14.6)

(Q1-Q5) -9.2% (-9.4- -9.2)

-9.8% (-10- -9.6)

-0.6%a

(-0.85- -0.3)

Recent registr-ant

< 5 years 18.8% (18.6-19)

22.1% (21.9-21.3)

25.8% (25.6-26)

29.1% (28.8-29.3)

30.8% (30.5-31.1)

32.3% (32-32.6)

13.5%a (13.1- 13.9)

> 5 years 29.7% (29.6-29.7)

34% (33.8-34)

37.8% (37.7-37.8)

42.4% (42.4-42.5)

44.1% (44-44.2)

44.2% (44.1-44.3)

14.5%a (14.4-14.6)

59

(Recent-non-recent)

-10.8% (-11- -10.6)

-11.9% (-12.2 - -11.6)

-1.1%a (-1.6- -0.7)

Rural/ Urban

Rural 27.4% (27.3-27.5)

31.7% (31.6-31.9)

35.1% (35-35.3)

40.1% (39.9-40.3)

42.7% (42.5- 42.8)

43.8% (43.6-44)

16.4%a (16.1-16.6)

Urban 29.5% (29.4-29.5)

33.6% (33.5-33.6)

37.5% (37.4-37.5)

42.4% (42.3-42.4)

44% (43.9-44.1)

44.1% (44-44.2)

14.6%a (14.5- 14.7)

(Rural-urban)

-2.1% (-2.2 - -1.9)

-0.3% (-0.5- -0.1)

1.8%a (1.5-2)

Individuals screened as per guidelines i.e. those who received at least one test in each cohort year in addition to those who received at least one FOBT in previous two years or those who received at least one endoscopy or barium enema in previous five years. # Statistical significance was tested using logistic regression. Significant if Wald Chi-square < 0.05 a= significant (p<0.05) b= not significant (p>0.05)

60

Table 3.7: Percent of ‘up-to-date’ by age group, Ontario, 2005-2011 2005/06 2006/07 2007/08 2008/09 2009/10 2010/2011 Percentage

point change

2005-2011#

3.148,382 3,261,582 3,382,216 3,511,224 3,483,685 3,937,079

N

(%)

1,101,631

(35%)

1,302,450

(40%)

1,504,973

(44.5%)

1,767,117

(45.6%)

1,825,946

(52.4%)

2,068,398

(52.5%)

Age

50-55 309,131

(28.9%)

366,801

(33%)

428,894

(37%)

510,806

(42.6%)

534,697

(44.8%)

562,317

(42.4%) 13.5%a

(13.38-13.6)

56-60 261,265

(35.1%)

314,635

(40.1%)

368,643

(44.7%)

420,748

(50.5%)

426,251

(52.4%)

483,482

(53.1%) 18%a

(17.8-18.1)

61-65 215,249

(38.7%)

257,248

(44.4%)

297,000

(49%)

361,664

(55.4%)

381,607

(57.4%)

453,244

(58.2%) 19.5%a

(19.3-19.6)

66-70 186,107

(40.8%)

216,568

(46.5%)

246,958

(51.7%)

287,454

(58.2%)

294,248

(60.2%)

348,467

(61.9%) 20.9%a

(20.7-21)

71-74 129,879

(40.1%)

147,198

(45.5%)

163,523

(50%)

186,445

(56.3%)

189,143

(58.7%)

220,888

(61.8%) 21.8%a

(21.6-22)

Difference

((50-55)-

(71-74))

-11.1

(-11.2 -10.9)

-19.4

(-19.5- -19.2) -8.3%a

(-0.9- -0.8)

Individuals screened as per guidelines i.e. those who received at least one test in each cohort year in addition to those who received at least one FOBT in previous two years or those who received at least one endoscopy or barium enema in previous five years. Percent of individual who were ‘up-to-date’ in each age group # Statistical significance was tested using logistic regression. Significant if Wald Chi-square < 0.05 a= significant (p<0.05) b= not significant (p>0.05)

61

CHAPTER 4: THE INFLUENCE OF PHYSICIAN RECOMMENDATION

ON PARTICIPATION IN FECAL OCCULT BLOOD SCREENING TEST

IN ONTARIO USING POPULATION BASED DATA

ABSTRACT

BACKGROUND: An organized screening program for colorectal cancer screening was

launched in Ontario on April 1st 2008. The Colon Cancer Check program is a provider-led

program. Primary care physicians are encouraged and remunerated for discussing colorectal

cancer screening with their patients. The goal of this study is to describe the pattern of

physician’s recommendation in Ontario two years after the implementation of the CCC and

examine the effect of physician’s recommendation on Fecal Occult Blood Test screening

participation and disparities in participation.

METHOD: We identified two annual cohorts of individuals aged 50 to 74 eligible for health

coverage in Ontario from April 1st 2008 to March 31st 2010 using the Registered Persons

Database. The cohorts were linked to Ontario Health Insurance Plan for information on CRC

testing and recommendation and to 2006 Census data for information on income and rural

neighborhood. We examined the unadjusted Prevalence Rate Ratio (PRR) and the multivariate

log binomial adjusted PRR of FOBT participation to test the association between physician

recommendation and demographic characteristics on FOBT participation.

RESULTS: We found that 28% of individual eligible for screening received physician

recommendation to complete an FOBT and disparities in recommendation. Physician

recommendation was lower for males, recent immigrants and individuals who moved residence;

and increased with income and age. Less than one third (29%) of the population completed an

FOBT in 2008-2010. We noted disparities in FOBT participation by demographic

characteristics. Physician recommendation tripled the likelihood of FOBT participation and

mitigated disparities by sex, income, recent registrant and mobility. Physician recommendation

had little effect on individuals aged 50-55 but increased participation for individuals aged 71-74.

CONCLUSION: Physician recommendation is a key strategy to increase FOBT participation in

Ontario. Physician recommendation remains suboptimal. Every effort needs to be done to

optimize the efficiency during primary care visits to increase physician recommendation rates.

62

INTRODUCTION

The Colon Cancer Check (CCC) program is the organized screening program for colorectal

cancer in Ontario. Launched on April 1st 2008, the goals of the program are to reduce the

mortality from colorectal cancer and increase the capacity of primary care providers to

participate in an organized screening program(CCC, 2010). The CCC recommends a biennial

Fecal Occult Blood Test (FOBT) for average risk individuals aged 50-74 followed by a

colonoscopy for positive cases. The CCC is a provider led program. In office, the physician

discusses with patients the benefits of screening and dispenses the FOBT kits. Rostered patients

who do not visit the office receive an invitation from the doctor to make an appointment to

discuss colorectal cancer screening. Individuals without primary care physicians (unattached

patients) can ask for FOBT kits from pharmacies or through Tele-health Ontario. Physicians are

remunerated for counseling and dispensing the FOBT kits, for sending invitation letters and for

completion of tests. Similar programs have been successfully implemented in the UK for breast

and cervical cancer. Their coverage rates in 2011 were 78% for cervical cancer and 77% for

breast cancer (T. N. I. C. NHS, Public Health Indicators Team, 2011; T. N. I. C. NHS, Public

Health Indicators Team 2011). (Appendix 2: Colon Cancer Check physician incentives).

Provider-led organized screening programs are associated with strengths and challenges. The

emphasis in a provider-led program is on physician’s recommendation for the test. Knowing that

a conversation between a physician and her patient plays a pivotal role in the decision making of

the individual to participate in screening is a major strength(Fenton, et al., 2011). However,

several barriers affect physician’s recommendation and counseling during an office visit. Some

are behavioral others are organizational. Ineffective patient-centered communication (Stewart,

et al., 2000), bias and stereotyping (van Ryn, et al., 2006) are examples of physician behavioral

barriers. The organizational characteristics of the practice affecting physician’s recommendation

include high volume practice (101-103), and lack of allied health professionals to assist in the

delivery of services (42, 98, 105). In team-based practices, the characteristics of team members,

the skill mix, the ability of staff to participate in decision-making influence the prospect of

discussing screening services. Other structural characteristics include office infrastructure such

as electronic medical records and audio-visual resources, which increase the potentials for

motivating patients to receive screening (Hogg, et al., 2008)

63

The effect of physician’s recommendation on screening uptake is extensively reported in the

literature (De Jesus, Puleo, Shelton, McNeill, & Emmons, 2010; Gilbert & Kanarek, 2005;

Gonzalez, et al., 2011; Guerra, et al., 2007; Honda, 2004; C. N. Klabunde, et al., 2009; C. N.

Klabunde, Schenck, & Davis, 2006; McGregor, et al., 2007; Sarfaty & Wender, 2007;

Sieverding, Matterne, & Ciccarello, 2008; Viguier, Calazel-Benque, Eisinger, & Pivot, 2011).

Most studies, however, used self-reported surveys to examine the association between

physician’s recommendation and screening. In this study we used physicians’ billing for

recommending and inviting patients to book an appointment to discuss CRC screening to

measure physician’s recommendation. The CCC financial incentives to remunerate physicians

offered us a unique opportunity to use objective data to examine the influence of physician’s

recommendation on FOBT participation.

The goal of this study is to describe the pattern of physician’s recommendation in Ontario two

years after the implementation of the CCC and examine the effect of physician’s

recommendation on FOBT screening participation and on disparities in participation. We

discuss strategies to optimize efficiency in primary care setting in order to increase physicians’

recommendation rates in Ontario.


we posited that the financial incentives set by the CCC are expected to motivate physicians to

discuss CRC screening with their patients. This interpersonal communication between the

physician and the patient, labeled as physician’s recommendation in this study, is expected to

make a change in screening participation among the target population regardless of their

demographic characteristics.

METHOD

Data Sources







64


by eligible physicians, groups, and laboratories. This database also contained information on

contact with physician.




information

• The Ontario Cancer Registry (OCR) database included all Ontario residents who

developed cancer since 1974.

• The 2006 census files from Statistics Canada at ICES contained aggregated data at the

dissemination area level (DA) for Ontario population that described the general demographic

information on 100% of the population including age, sex and postal code, and socio-

demographic information on 20% sample of the population including income and education.


Using the RPDB, we identified a cohort eligible for health coverage in Ontario aged 50 to 74

between April 1st 2008 and March 31st 2010. We excluded individuals with history of colorectal

cancer, Crohn’s disease and ulcerative colitis as well as those individuals exempted from the

tests by their physicians (Appendix 3: Data linkage flow chart)

To identify individuals who received a Fecal Occult Blood Test and physician recommendation,

we linked individuals included in the cohort to OHIP database. We used L181, G004 or L179,

Q152A, Q118-Q123 billing codes for FOBT test, and Q150A, Q005A for physician’s

recommendation and counseling. It is noteworthy to mention that L181 code can be performed

for a number of reasons, including screening, diagnosis or follow-up and monitoring, while the

rest of the codes are for screening purposes only(MOHLTC, 2011). Regardless of the reason for

the test, we considered an individual receiving any FOBT as essentially screened. The Q150A

is the billing code for physicians who discussed and dispensed the FOBT kit during an office

visit. The Q005A is the billing code for physicians who have contacted their enrolled patients to

schedule an appointment for colorectal cancer screening. (Appendix 2: Colon Cancer Check

program physician incentives)

We used the postal code on the RPDB database to link individuals included in our cohort to the

65

Dissemination Area (DA) using the Postal Code Conversion File (PCCF 6+). The PCCF flags

the neighborhood income quintile and the urban/rural status of the individual.

Definition of Measures

Outcome measure

Individuals who received their index FOBT during the two-year period (April 1st 2008-March

31st 2010).

Contact with physician measure:

In each year, each individual who had at least one contact with a general practitioner/family

physician in Ontario for a core primary service was assigned a physician on OHIP database. We

used this information to identify an individual who had at least one contact in one year. We

created a three level variable: ‘at least 2 contacts’ in two years for individuals who had

physicians assigned to them for two consecutive years; ‘at least 1 contact’ in two years for

individuals who had one physician assigned to them in to two years; ‘no contact’ in two years

for individuals who did not have any physician assigned to them in two years.

Recommendation measure

Recommendation of an FOBT is based on physician’s billing for either discussing FOBT in the

office or for calling or sending a letter for individuals who did not visit the practice. We used the

remuneration billing codes assigned by the CCC for that purpose (Q005A and Q150A).

Demographic variables







services. An individual whose eligibility for OHIP started within five years from the beginning

of the cohort (i.e. if eligibility started in the second quarter of the 2003 fiscal year and onward)

66

was considered as recent registrant. Otherwise, if eligibility started more than five years before

the beginning of the cohort then the individual was considered non-recent registrant. Recent

registrant is used as a proxy measure for recent immigrant status in Ontario; an estimated 80%

of recent registrants are recent immigrants (Lofters, et al., 2007). Finally, if the individual

changed their Dissemination Area (DA) from one year to another, it was an indication for

changing residence. We referred to this measure as mobility.



bi-variate statistics to measure the percent and the 95% confidence intervals of individuals who

received an FOBT in two years, percent of contact with physician and percent of physician’s

recommendation. We stratified each measure by gender, age, income, recent registrant,

mobility, and urban/rural status. We examined the unadjusted Prevalence Rate Ratio (PRR) of

each outcome. A PRR greater than 1 indicated increased likelihood of participation as compared

to the referent group.

We used multivariate log binomial regression to examine the relation between contact with

physician, physician’s recommendation and demographic factors on FOBT participation. We

used the method of maximum likelihood in PROC GENMOD in SAS. We presented the

estimates as Prevalence Rate Ratio (PRR), a better estimate than odds ratio when the outcome is

higher than 10% (L. A. McNutt, Wu, Xue, & Hafner, 2003; T. L. McNutt, Olds-Clarke, Way,

Suarez, & Killian, 1994). First, we run the main effect model. Then, we run a model with

interaction terms between contact with physician and physician recommendation. We run the

final model to test the following null hypothesis: there is no effect for physician’s

recommendation on FOBT participation; the alternative hypothesis was: physician’s

recommendation significantly increased FOBT participation. We used a p-value of 0.05 as cut

point to test the two-sided statistical significance difference.

RESULTS

The study had 3,189,219 individuals eligible for screening between April 1st 2008 and March

31st 2010 (Table 4.1). Individuals were equally distributed by gender and income; 34% were

between 50 and 55 and 11% between 71 and 74 years in age; 5% were recent registrants on

OHIP and 13% were living in rural areas; 16% changed their residence in those two years.

67

Distribution of contact with physician and physician’s recommendation by demographic

characteristics

The proportion of individuals who had ‘no contact’ was 14%, ‘at least 1 contact’ 13%, and ‘at

least 2 contacts’ was 73% (Table 4.2). We assessed the linear trends between ‘no contact’, ‘at

least 1 contact’ and ‘at least 2 contacts’ for each variable using the Cochran-Armitage Trend

test. All point estimates were <0.001. Females were more likely to have contact with physicians

than males. Contact increased with age and income. Recent registrants were less likely to have

‘at least 2 contacts’ than non-recent registrants. Recommendation increased with ‘at least 2

contacts’.

From April 1st 2008 to March 31st 2010, the proportion of those who received recommendation

from their physicians to participate in an FOBT test was 28% (Table 4.3). The proportion of

females receiving recommendation was higher than males (PRR= 1.1, CI (1.1-1.11).

Recommendation increased with income (Q1 vs. Q5, PRR= 0.88, CI: 0.88-0.89). The likelihood

of recommendation decreased with recent registrants (PRR= 0.62, CI (0.62-0.63)), rural

residence (PRR=0.86, CI (0.86-0.88)), and among those moved their residence (PRR=0.62, CI:

0.62- 0.63). The likelihood of recommendation did not differ by age (PRR for 50-55 compared

to 71-74 = 1.03, (CI: 1.02-1.03)) (Figure 4.1).

We conducted additional analyses to examine whether physician’s recommendation changed

between 2008/09 and 2009/10. We found that among those eligible for CRC screening, 16

percent received a physician recommendation in each year.

Unadjusted and multivariate regression adjusted prevalence rate ratio of FOBT participation

The proportion of individuals who had at least one FOBT in 2 years was 29% (N= 935,652)

(Table 4.4.). The proportion of FOBT participation was higher among the following categories:

females, individuals aged 71-74, highest income quintile, non-recent registrants, urban living,

and non-mobile individuals (Table 4.4. & Figure 4.2). The proportion of participation was

higher among those who had ‘at least 2 contacts’ (26%), and those who received

recommendation (17%) (Table 4.4.) Individuals who had ‘at least 2 contacts’ with physicians

were 1.26 times more likely to have an FOBT compared to those who had no contact

(PRR=1.26, CI=1.25-1.27); if they had ‘at least 1 contact’ they were 1.7 times more likely to

have an FOBT (PRR=1.7, CI=1.6-1.8); if they received recommendation they were 4.17 times

68

more likely to have an FOBT as compared to those who did not have recommendation

(PRR=4.17, CI=4.15-4.18) (Table 4.4)

We ran the main effect model including contact with physician, physician recommendation and

all explanatory variables. There was a strong association between contact and physician’s

recommendation and FOBT participation (data not shown). We ran a second test including the

interaction term (contact with physician and physician recommendation) and found it

significant. We ran the final to test the null hypothesis that ‘physician recommendation does not

affect FOBT participation’. The results are presented in table 4.5. We found that among

individuals who had ‘at least 2 contacts’ with a physician, their likelihood to have an FOBT

increases 3.23 times as compared to no recommendation; those who had ‘at least 1 contact’,

their likelihood to have an FOBT increases 3.04 times compared to those who did not receive

recommendation. We found that adjusting for recommendation reduced significantly the

disparity by gender (PRR=1.04, CI=1.04-1.05), by income (PRR=0.95, CI= 0.95-0.96), by

recent registrant (PRR=0.96, CI: 0.95-0.97), and by mobility (PRR=0.83, CI: 0.83-0.84). There

was little effect on rural/urban gap (PRR=-0.94, CI= 0.93-0.94). Interestingly, the age gap

increased (age 50-55 PRR=0.74), which may be explained by a larger increase in participation

among the reference group (71-74).

DISCUSSION

The results of this study showed that in two years, 28% of individuals eligible for screening

received recommendation either in the form of an office discussion about FOBT or received a

letter to take an appointment to discuss FOBT. Among those eligible for CRC screening, 16

percent received a physician recommendation in 2008/09 and in 2009/10. Physician’s

recommendation was disparately distributed among population sub-groups. The likelihood of

physician recommendation was lower for males, recent registrants, rural and mobile individuals

and increased with age and income.

Less than one third (29%) of the population eligible for screening in Ontario completed an

FOBT in 2008-2010. Participation rate increased significantly among those who had contact

with physician and with physician’s recommendation. We noted disparities in FOBT

participation by demographic characteristics. However, among those who received physician

recommendation, the demographic disparities by gender, income, recent registrant and mobile

69

individuals were removed. Recommendation had little effect on individuals aged 50-55 but

increased participation among individuals aged 71-74.

In our study, we found contact with a primary care physician an enabler for screening uptake.

The association between contact with a physician and screening participation is reported

elsewhere (Ananthakrishnan, Hoffmann, & Saeian, 2010; Simonds, Colditz, Rudd, & Sequist,

2011; Zarychanski, et al., 2007) . Although different studies used different measures for contact

with the physician, all results agreed that any contact with a primary care physician increases the

likelihood of CRC screening. Primary care physicians play a pivotal role in engaging individuals

in screening. Therefore every contact with a physician should be taken as an opportunity to

discuss prevention. Physicians need first to be themselves convinced and clear about the

guidelines (Viguier, et al., 2011). The primary care campaign aiming to harness physicians’

support for the program (CCC, 2010) is a welcome strategy to increase screening in the province

(Damery, Clifford, & Wilson, 2010).

Most importantly, we found that physician recommendation was the stronger predictor for

screening uptake. We know about the barriers affecting physician’s recommendation during an

office visit including competing demand with the main reason for the visit (Nutting, et al.,

2001), the equivocal concerns with other health problems (Guerra, et al., 2007; Levy, Nordin,

Sinift, Rosenbaum, & James, 2007) and an ever growing list of preventive services to be

delivered (Yarnall, Pollak, Ostbye, Krause, & Michener, 2003) . We also know that discussing

screening options with vulnerable populations poses additional challenges including

communication across languages, culture and health literacy(Fiscella & Epstein, 2008). The

breast and cervical cancer programs in the UK worked relentlessly on removing those barriers to

achieve the levels they reached in recent years(T. N. I. C. NHS, Public Health Indicators Team

2011). Similar initiatives are needed in Ontario primary care system.

Traditional patient-physician interaction within an office visit is becoming inadequately

prepared for the increasing demands for primary care services (Reid & Wagner, 2008). In order

to improve the quality of preventive services, several approaches are proposed to optimize the

efficiency in the practice(IHI, 2011). We categorize these approaches into three domains:

structure, process and outcomes domains, after the classical paradigm of Avedis Donabedian

(Donabedian, 1966). The main approaches include structural re-design (structure),

70

organizational behavior modifications (process), and continuous quality improvement strategies

(outcome).

The shift from a physician-led primary care to a team approach for primary health care is the

main structural re-design for a high performing primary care (Fiscella & Epstein, 2008; IHI,

2011; C. N. Klabunde, et al., 2009; MOHLTC, 2011). Teams are multidisciplinary and tasks are

distributed among team members based on capability rather than on traditional role(Fiscella &

Epstein, 2008). In a team approach, physicians rely on other health professionals to deliver

important preventive services messages(Holtrop & Jordan, 2010). The critical role of a team

approach in primary care on CRC screening participation is well documented (C. N. Klabunde,

et al., 2009; J. Zapka, 2008). The Family Health Team in Ontario is an example of a team

approach for primary care. This model was introduced in 2006 and the Quality Improvement &

Innovation Partnership (QIIP)(QIIP) is the body coaching and training primary health care

professionals during their transition into a multidisciplinary approach to primary care. Given

their recent implementation, there is no formal evaluation of their performance. Future studies

need to look at the impact of family health teams on screening participation in Ontario.

Information technology is another structural approach for a high performing health care (Dale,

Ralston, Doherty, & Ginsburg, 2008) (Reid & Wagner, 2008). Several forms of information

technology are proposed. The most common comes in the form of electronic health records or

electronic medial records, which can be used not only to exchange information between

providers(Reid & Wagner, 2008), but also to enhance provider-patient communication(Urowitz,

et al., 2008). Electronic medical record can be used to post physician’s reminder, patient

reminder and can be used for continuous quality improvement. Patient’s accessible electronic

health record can be used as a channel to post physician’s recommendation, and to empower

individuals with information on how to manage screening, circumventing the barriers of too

little time to discuss prevention during an office visit (Fiscella & Epstein, 2008). The Veterans

Administration (VA), US, offers a stellar example of the effect of electronic health records on

quality of preventive services in primary care (Dale, et al., 2008; Jha, Doolan, Grandt, Scott, &

Bates, 2008). In Canada, electronic medical records are still in their novice stage. They are used

in various primary health care settings in Ontario and grey literature reports indicate their

effectiveness in increasing FOBT participation (QIIP). The validity of using electronic medial

71

record data in Ontario has been tested(Tu, Klein-Geltink, Mitiku, Mihai, & Martin, 2010) but so

far no study tested the effect of electronic records on screening uptake in Ontario.

From an organizational perspective, approaches such as planning ahead the visit or optimizing

the care team efficiency are proposed (IHI, 2011). Planning the visit would involve identifying

individuals who are due for screening on the day of the visit and prompting the patient to do the

test. Optimizing the efficiency of the team requires a clear identification of the types of services

delivered in the practice, and then deciding how to delegate work according to capabilities and

not on the traditional role. It may also involve hiring culturally appropriate staff to work as

language and cultural interpreter when needed(Fiscella & Epstein, 2008).

Finally, continuous quality improvement using multiple strategies was found to be highly

effective in increasing screening participation(Brouwers, et al., 2011; J. Zapka, 2008). Audit and

feedback is a common approach which involves evaluating the continuous performance of the

practice in delivering screening to clients and providing feedback to primary care providers

using a facilitator (Brouwers, et al., 2011; Sabatino, et al., 2008)

LIMITATIONS

This study has limitations. Using the physician’s recommendation incentives need to be taken

with a few precautions. First, the in-office incentive (Q150) is offered to all physicians as well

as for nurse practitioners led-clinics and for pharmacists. In this study, we combined them all

into one category. Physician’s endorsement may have a stronger effect on the outcome than

nurses or pharmacists (Fitzgibbon, et al., 2007). Hence, our results may show stronger

association if we had used physician recommendation only. Second, we found that 18% were in-

office and 10% were in the form or a letter or telephone (data not shown), either channel was

found effective in increasing FOBT participation according to Brouwers et al(Brouwers, et al.,

2011). We purposefully did not differentiate between the two channels of recommendation

because it is beyond the scope of our research. Perhaps, future studies can look at the

effectiveness of in-office and letter on FOBT participation using population-based data. Third,

in order to bill for recommendation and counseling, physicians need to keep a paper trail

indicating the date and outcome of the service. Because it is fairly new no study has looked at

the completeness of this data, hence the precautionary note.

72

CONCLUSION

In conclusion, we found that physician’s recommendation tripled the likelihood of FOBT

screening uptake in Ontario and mitigated demographic disparities in participation. Physician’s

recommendation of CRC is still suboptimal. Every effort needs to be done to increase physician

capacity and optimize the efficiency during a primary care visit.

73


Figure4.1.Percentofphysicianrecommendationbydemographiccharacteristics,Ontario,2008‐2010

74

Figure4.2.PercentofFOBTparticipationbydemographiccharacteristics,Ontario,2008‐2010

75


Table4.1:Populationeligibleforcolorectalcancerscreeningby demographiccharacteristics,Ontario,2008‐2010

2008/10

N 3,189,291

Sex Females 1,614,408(51%)

Males 1,574,883(49%)

Age(yrs) 50‐55 1.075,113(34%)

56‐60 724,336(23%)

61‐65 589,686(18%)

66‐70 436,679(14%)

71‐74 363,477(11%)

Income Quintile1(low) 600,112(19%)

Quintile2 633,768(20%)

Quintile3 626,449(20%)

Quintile4 650,268(20%)

Quintile5(high) 678,694(21%)

Registrant <5years 145,286(5%)

>5years 3,044,005(95%)

Rural# Rural 423,982(13%)

Urban Urban 2,765,309(87%)

Mobility* Yes 505,731(16%)

No 2,683,560(84%)

#Communitysize<10.000;*Individualswhochangedresidenceintwoyears.

76

Table4.2:Contactwithphysicianbydemographiccharacteristicsandphysician recommendation,Ontario,2008‐2010.Lineartrendswereassessedbythe Cochran‐ArmitageTrendtest.Allpointestimateswere<0.001.

No contact

At least 1 contact

At least 2 contacts

Overall 454,804(14%) 421,552(13%) 2,312,935(73%)

Sex Females 190,114(42%)¥ 198,971(47%) 1,225,323(53%)

Males 264,690(58%) 222,581(53%) 1,087,612(47%)

Age 50‐55 188,640(42%) 227,423(54%) 659,050(28%)

56‐60 106,078(23%) 52,373(12%) 565,885(24%)

61‐65 74,190(16%) 37,556(9%) 477,940(21%)

66‐70 47,898(11%) 24,727(6%) 364,054(16%)

71‐74 37,998(8%) 79,473(19%) 246,006(11%)

Income

Quintile1(low)107,185(24%) 82,134(20%) 410,793(18%)

Quintile2 93,423(20%) 84,700(20%) 455,645(19%)

Quintile3 82,790(18%) 83,167(20%) 460,492(20%)

Quintile4 81,493(18%) 85,479(20%) 483,296(21%)

Quintile5(high)

89,913(20%) 86,072(20%) 502,709(22%)

Registrant <5years 40,716(9%) 40.914(10%) 63,656(3%)

>5years 414,088(91%) 380,638(90%) 2,249,279(97%)

Rural/Urban Rural 54,679(12%) 59,390(14%)

309,913(13%)

Urban 400,125(88%) 362,162(86%) 2,003,022(87%)

77

Mobility Yes 83,870(18%) 260,264(62%) 161,597(7%)

No 370,934(82%) 161,288(38%) 2,151,338(93%)

Recomm‐endation

Yes 16,196(4%) 71,462(17%)

800,859(35%)

No 438,608(96%) 350,090(83%) 1,512,076(65%)

¥Columnpercentofindividualsbycontactwithphysicianstatus

78

Table4.3:Physicianrecommendationbydemographiccharacteristics,Ontario,2008–2010

PHYSICIANRECOMMENDATION

YES

NO PrevalenceRateRatio(95%CI)

Overall 888,517(28%) 2,300,774(72%)

Sex(ref=M) Females 482,156(54%) 1,132,252(49%)

Males 406,361(46%) 1,168,522(51%)1.1(1.1‐1.11)

Age(ref=71‐74)

50‐55 276,819(31%) 798,294(35%) 1.03(1.02‐1.03)

56‐60 211,190(24%) 513,146(22%) 1.09(1.09‐1.1)

61‐65 179,902(20%) 409,784(18%) 1.13(1.13‐1.14)

66‐70 134,961(15%) 301,718(13%) 1.17(1.17‐1.18)

71‐74 85,645(10%) 277,832(12%)

Income(ref=Q5)

Quintile1(low)

147,268(17%) 452,844(20%) 0.88(0.88‐0.9)

Quintile2 174,922(20%) 458,846(20%) 0.96(0.96‐0.97)

Quintile3 179,978(20%) 446,471(19%) 0.99(0.99‐1)

Quintile4 190,568(21%) 459,700(20%) 1.01(1‐1.03)

Quintile5(high)

195,781(22%) 482,913(21%)

Registrant(ref=>5yrs)

<5years 28,362(3%) 116,924(5%)0.62(0.62‐0.63)

>5years 860,155(97%) 2,183,850(95%)

Rural/Urban

Rural 106,565(12%) 317,417(14%) 0.87(0.86‐0.88)

79

(ref=urban) Urban 781,952(88%) 1,983,357(86%)

Mobility Yes 98,209(11%) 407,522(18%)

(ref=nomobility)

No 790,308(89%) 1,893,252(82%)0.62(0.62‐0.63)

80

Table4.4:UnadjustedPrevalenceRateRatio(PRR)ofFOBTparticipation bydemographiccharacteristics,Ontario,2008‐2010

Unadj.=Unadjustedprevalencerateratio

FOBTN(%)

PRRUnadj.

Contactwithphysician‘Atleast2contacts’‘Atleast1contact’Nocontact(ref)

839,034(90%)76,002(8%)20,616(2%)

1.26(1.26‐1.27)1.7(1.7‐1.8)1

RecommendYesNo(ref)

563,079(60%)372,573(40%)

4.17(4.15‐4.18)1

SexFemaleMale(ref)

508,664(54%)426,988(46%)

1.11(1.10‐1.11)1

Age50‐5556‐6061‐6566‐7071‐74(ref)

250217(27%)212,773(23%)197,833(21%)159,830(17%)114,999(12%)

0.89(0.88‐0.89)0.96(0.96‐0.97)1.03(1.03‐1.04)1.10(1.09‐1.11)1

IncomeQ1(low)Q2Q3Q4Q5(high,ref)

155,131(17%)185,147(20%)189,576(20%)200,930(21%)204,868(22%)

0.88(0.88‐0.89)0.97(0.97‐0.98)1(0.99‐1)1.02(1.01‐1.02)1

Registrant<5yrs>5yrs(ref)

32,593(3%)903,059(97%)

0.69(0.68‐0.7)1

RuralUrban(ref)

117,117(13%)818,535(87%)

0.92(0.91‐0.93)1

MobilityYesNo(ref)

103,354(11%)832,298(89%)

0.62(0.61‐0.62)1

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Table4.5.Multipleregressionadjusted*prevalencerateratioofFOBTparticipationamongthosewhohadcontactwithphysiciansbydemographiccharacteristics,Ontario,2008‐2010

*Adjustedforphysician’srecommendationanddemographiccharacteristics.

Characteristic PRR(95%,CI)

Contact‘Atleast2contacts’‘Atleast1contact’

RecommendYesvs.NoYesvs.No

3.23(3.22‐3.24)3.04(3‐3.06)

Sex Femalevs.male 1.04(1.04‐1.05)Age

50‐55vs.71‐74

56‐60vs.71‐74

61‐65vs.71‐74

66‐70vs.71‐74

0.74(0.73‐0.74)

0.83(0.83‐0.84)

0.91(0.91‐0.92)

0.98(0.98‐0.99)Income Q1vs.Q5

Q2vs.Q5

Q3vs.Q5

Q4vs.Q5

0.95(0.95‐0.96)

0.99(0.99‐1)

1(1‐1.01)

1.01(1‐1.01)Registrant <5yrsvs.>5yrs 0.96(0.95‐0.97)Rural/urban Ruralvs.Urban 0.94(0.93‐0.94)Mobility Yesvs.no 0.84(0.84‐0.85)

Screening

82

CHAPTER 5: THE EFFECT OF THE COLON CANCER CHECK

PROGRAM ON FECAL OCCULT BLOOD TEST PARTICIPATION IN

ONTARIO: AN INTERRUPTED TIME SERIES USING SEGMENTED

REGRESSION ANALYSIS

ABSTRACT

BACKGROUND: On April 1st, 2008, the Colon Cancer Check (CCC) program, the first

provincial screening program for colorectal cancer, was introduced in Ontario. Two

interventions accompanied the launch of the CCC: a mass media campaign, and new physician

incentives for CRC screening. The goal of this study is to test the effect of the CCC on FOBT

participation thirty months after its implementation.

METHOD: An interrupted time series analysis using segmented regression was conducted.

Using the Registered Persons Database, we identified six annual cohorts of individuals aged 50

to 74 eligible for health coverage in Ontario. The cohorts were linked to Ontario Health

Insurance Plan for information on CRC tests, and to 2006 Census from Statistics Canada for

information on neighborhood income and rural/urban status. We used quarterly data and

reported the results of FOBT participation per 1000 person-months. We tested for auto-

correlation and seasonality using AUTOREG and SPECTRA in SAS.

RESULTS: Screening for CRC using FOBT was steadily increasing before the launch of the

CCC. The CCC rose participation to its highest level. The increase in participation immediately

after the CCC was 8.2‰ person-months, followed by a declining trend (-1.5‰ 1000 person-

months) in 2009 and 2010. We found summer troughs in participation in every year (on average

-1.9‰ person-months). Stratified results showed that a significant increase in level after the

CCC across all population sub-groups.

CONCLUSION: The public launch the CCC led to a significant increase in level of FOBT

participation in 2008. A decline in trend was noted in 2009 followed by a plateau in 2010, which

leveled off at a level higher than pre-CCC. Both elements that accompanied the launch of the

CCC were effective in increasing FOBT participation, but the mass media is likely the major

83

factor that led to the significant increase in level immediately after April 1st 2008. Strategies to

increase the overall participation are needed. Tailored strategies to reduce disparities are

important at this stage.

84

BACKGROUND

Colorectal cancer (CRC) poses a significant burden on Ontario’s health care system(CCC,

2010). Screening is an effective measure to reduce the burden of the disease. Regular screening

using Fecal Occult Blood Test (FOBT) detects cancer at an early stage and increases the chance

of cure by 90%(Winawer, et al., 1993). FOBT followed by a colonoscopy for positive results

reduces mortality by 16%(Hardcastle, et al., 1996; Hewitson, et al., 2007).

Prior to 2008, the delivery of CRC screening tests in Ontario was opportunistic. This approach

is best described as passive case finding approach, largely relying on primary care physician for

recommending and delivering the test and occasionally on individuals requesting the test

(Senore, Armaroli, et al., 2010). On April 1st, 2008, the Colon Cancer Check (CCC) program

was introduced into Ontario. The CCC is an organized screening program for CRC. The dual

goals of the program are to reduce the mortality from colorectal cancer and increase the capacity

of primary care providers to participate in an organized screening program(CCC, 2010).

Organized screening programs, as opposed to opportunistic, are an active approach for

screening. Certain features characterize organized screening programs: a targeted population for

screening, a specific test for screening and recommended interval for repeat screening, a

management team for the implementation of the program, a health care team for the delivery of

services, a standardized quality assurance program for the laboratory tests, and performance

measurement and monitoring (IARC, 2005b).

The CCC program incorporates most elements of an organized screening program features

(Rabeneck, 2007). The CCC identifies individuals aged 50-74 as the target population eligible

for screening in Ontario, adopts the biennial Fecal Occult Blood Test as a primary test for

screening, and colonoscopy to follow-up for positive cases. A capable management team leads

the day-to-day activities of the program. Using an evaluation framework and indicators, the

team is responsible for monitoring the uptake, and for measuring and reporting the performance.

In addition, a province wide primary care strategy is set to engage primary care physicians to

participate in the program (Levitt & Lupea, 2009).

Two interventions were introduced at the launch of the CCC, a public-directed intervention and

a provider-directed intervention. A public media campaign marked the launch of the CCC on

April 1st, 2008 (CCC, 2010). The purpose of the public media campaign was to promote the

85

CCC program province wide and to educate Ontarians about the importance of screening. The

intense but temporary public media campaign at the launch of the program included television

advertising, radio messages, newspapers clips and pamphlets in 22 languages across the

province. The provider directed intervention was the financial incentives to remunerate

physicians for recommending and dispensing the FOBT kit, and for the completion of the test

(Care, 2008). These incentives became effective on April 1st 2008.

The goal of this study is to evaluate the effect of the introduction of the CCC program on the

trend of FOBT participation in Ontario. Using interrupted time series, we compared the trend of

FOBT participation three years before and two and half years after April 1st 2008 (from April 1st

2005 to September 30th 2010). We decided to end the analysis on September 1st 2010 rather than

March 31st 2011, due to the introduction of a new intervention, the recall letter program, in fall

2010 (Care, 2011). Our analyses addressed the following objectives: 1- to examine the secular

trend of FOBT participation prior to the launch of the CCC; 2- to test the effect of introducing

the CCC on FOBT participation, immediately after the launch and the trend thereafter; 3- to

examine the differential effect of the CCC on FOBT participation by individual and

demographic characteristics.


we posited that the two elements introduced by the CCC, financial incentives and mass media,

are expected to lead to a change in screening participation among the target population.

However, variation in individuals’ characteristics determines their propensity for participation.

Some individuals are expected to be more receptive to screening than others. Hence,

demographic characteristics such as age, gender, income, recent immigration and living in rural

neighborhoods may be affected differently by the two elements of this policy.

METHOD

Data Sources



86






by eligible physicians, groups, and laboratories.




information

• The Ontario Cancer Registry (OCR) database included all Ontario residents who had

cancer or who died from cancer since 1974

• The 2006 census files from Statistics Canada at ICES contained aggregated data for

Ontario population that described the general demographic information on 100% of the

population such as age, sex and postal code, and socio-demographic information on 20%

sample of the population including income and education.


Using the RPDB, we identified six annual cohorts of individuals eligible for health coverage in

Ontario. Each cohort had individuals aged 50 to 74 for each year from April 1st 2005 to Match

31st 2011. To identify individuals eligible for screening, we used the encrypted numeric

identifier at ICES (IKN) and linked it to OCR to exclude colorectal cancer patients, to CIHI-

DAD to exclude Crohn’s and ulcerative colitis patients, and to OHIP for individuals exempted

from the tests by their physicians (Q142)3.

3 The exclusionary code for colorectal screening Q142 is used for the following: (i) Enrolled Patients with known cancer being followed by a physician; (ii) Enrolled Patients with known inflammatory bowel disease; (iii) Enrolled Patients who have had colonoscopies within five (5) years; (iv) Enrolled Patients with a history of malignant bowel disease; and (v) Enrolled Patients with any disease requiring regular colonoscopies for surveillance purposes.

87

To identify individuals who received a Fecal Occult Blood Test, we linked individuals included

in the cohort to OHIP database. We used L181, G004 or L179, Q152A, Q118-Q123 to identify

individuals who completed an FOBT in each year.

The postal code on the RPDB database was used to link individuals included in our cohort to the

Dissemination Area using the PCCF 6+. The PCCF flags the neighborhood income quintile and

the urban/rural status of the individual.

Measures

Outcome measures:

We measured the rate of FOBT participation per quarter per 1000 person-months. We divided

each year into 4 quarters. Our denominator included individuals who were due for screening in

each quarter of the year (April-June; July-September; October-December; January-March). For

each quarter, we calculated the person-month contribution of the individual to the cohort based

on the following events: death, receiving any large bowel test and previous large bowel test. For

a complete description on how we calculated the person-month please refer to chapter 2 and

appendix 5.

Explanatory measures







services. We examined eligibility for OHIP in the past five years from the beginning of the

cohort (e.g. for 2008 cohort, we examined the status of eligibility starting from the second

88

quarter of the 2003 fiscal year). If the individual was eligible all along then he/she was

considered as non-recent registrant, otherwise he/she was a recent registrant). It is estimated that

80% of recent registrants are recent immigrants, therefore recent registrant was used as a proxy

measure for recent immigrant status in Ontario(Lofters, et al., 2007).

Study design

This study used Interrupted Time Series (ITS) design to test the effect of introducing the Colon

Cancer Check program on FOBT participation in Ontario. The essence in an interrupted time

series design is to have an intervention at a certain point in time and to have data collected on a

regular basis, before and after the intervention (Campbell, 1966). The main advantage of an

interrupted time series design is that it provides a powerful statistical method to estimate the

effect of an intervention on the outcome (Ramsay, et al., 2003; Wagner, et al., 2002).

Additional strength for using interrupted time series design is it allows for the statistical

investigation of potential biases expected to happen in natural experiments including secular

trend, seasonality and autocorrelation (Campbell, 1966; Ramsay, et al., 2003). A secular trend

means a change in the outcome may have happened before the intervention, if undetected the

effect is erroneously attributed to the intervention(Ramsay, et al., 2003). Seasonality is when the

outcome has a cyclical pattern over time (Campbell, 1966). Autocorrelation is when data close

to each other are similar such as one high observation is followed by another high observation

(Ramsay, et al., 2003; Wagner, et al., 2002). Failing to correct for seasonality and auto-

correlation may lead to underestimated standard error and overestimated parameter effect.

Two major statistical techniques are recommended for interrupted time series: the Auto

Regressive Integrated Moving Average (ARIMA) (Box, 1994) and segmented regression (Cook

TD, 1979; Gillings, et al., 1981) (Cook TD, 1979; Gillings, et al., 1981). ARIMA analysis

assumes a complex autocorrelation structure and requires sample sizes of at least 50 consecutive

time points (Biglan, Ary, & Wagenaar, 2000; F. Zhang, Wagner, Soumerai, & Ross-Degnan,

2009). Segmented regression uses shorter series, aggregate data and is amenable for graphic

presentation. Given that the total points of this study are 22, using segmented regression would

be a more appropriate statistical technique(Biglan, et al., 2000; Gillings, et al., 1981; Wagner, et

al., 2002; F. Zhang, et al., 2009).

The purpose of a segmented regression is to measure the change in level and trend in the

89

outcome after the intervention(Inc., 2010; Wagner, et al., 2002). Usually, the data is divided into

two segments: one segment containing observations before the intervention and the second

segment observations after the intervention. The cut-point is the intervention. The pre-

intervention segment is used as a control for the post-intervention segment, thus correcting for

secular trend, and providing a methodologically acceptable approach to test the effect of an

intervention without using a control group (Campbell, 1966; Inc., 2010; Ramsay, et al., 2003;

Wagner, et al., 2002).



a p-value of 0.05 as cut point to test the two-sided statistical significance difference between two

groups.

Data analyses used several statistical techniques to test for seasonality, auto-correlation, and to

estimate the change in level and trend in FOBT participation. First we calculated the FOBT test

participation per 1000 person-months per quarter of a year. We used the aggregate data to plot

the graph. A simple visual inspection of the graph was used to detect any abrupt or lagged

change in level or direction after the intervention, outliers, and seasonal variation. Then, the

statistical analysis included fitting a simple ordinary least square (OLS) regression, using

AUTOREG in SAS without auto-correlation. Testing for auto-correlation used the generalized

Durbin Watson (DW) test (Inc., 2010). To test for seasonality, we used the visual inspection and

statistical analyses. A simple visual inspection of the data may reveal a seasonal fluctuation, in

our case drop in rates during the summer quarter. Statistically, we run spectral analysis to test

for seasonality. PROC SPECTRA in SAS provides a Bartlett Kolmogorov Smirnov’s (BK)

White Noise test to examine departures from white noise over all frequencies (Moineddin, et al.,

2008). If BK is significant we reject the null hypothesis and confirm the presence of seasonality.

To correct for seasonality, we simply added a dummy variable for summer into our model

(Hogberg, et al., 2005). Once auto-correlation and seasonality were detected, an ordinary least

square estimation would not be appropriate for the analysis. The maximum likelihood method

was used to estimate the parameters and auto correlation term was added to the model(Inc.,

2010; Spitzer, 1979; F. Zhang, et al., 2009).

Using the aggregate rates, we used auto-regression analysis to test the change in level and slope

90

before and after the CCC (April 1st 2005-March 31st 2008 compared to April 1st 2008-September

30th 2010), and to test for seasonality in participation. For the differential effect of the CCC, we

stratified the auto-regression analysis by individual and socio-demographic characteristics. We

tested the significance of the interaction terms using multiple logistic regression and significant

terms were used as strata for the final auto-regression analysis.

To test for the differential effect of the CCC program by gender, age, income, recent registrant

and urban/rural, we stratified the aggregate data by sex, age group 50-55 (lowest age group) and

71-74 (highest age group), quintile 1 (lowest income) and quintile 5 (highest income), recent

registrant vs. non recent, and by urban vs. rural. We run the regression model (correcting for

auto-correlation and seasonality when necessary) for each stratum and used their β2 and β3

estimates and their corresponding standard errors to calculate the Z value and test whether the

changes in level or slope were statistically significant by sex, age, income, recent registrant

status and urban/ rural status. Finally, we run a multiple logistic regression using individual data

testing the association between FOBT participation and individual and socio-demographic

characteristics and their associated interaction terms. Significant interaction terms (e.g. Age

group and sex, age group and income) were used to run a final auto-regression model and test

the effect of the CCC on each sub-group. (Appendix 7: Statistical analysis for segmented

regression).

RESULTS

From 2005 to 2011, the six cohorts of eligible individuals who were due for screening and their

breakdown distribution by socio-demographic characteristics are in table 5.1. All six cohorts

were equally distributed by gender and income; 35% were between the age of 50 and 55 and

10% between 71 and 74; 5% were recent registrants on OHIP and 14% were living in rural

areas.

The aggregate data for FOBT participation rates per 1000 person-months by quarter from April

1st 2005 to September 30th 2010 are shown in figure 5.1. The visual inspection of the graph

showed a steady increase in rates before the CCC program, reaching a peak between April 1st

and December 31st 2008. There was a steady decline between January 1st 2009 and August 31st

2009 followed by a plateau between September 1st 2009 and September 30th 2010. We noted

drop in rates in every second quarter of the year (June-September).

91

The parameter estimates of the auto-regression without and with correction for auto-correlation

are shown in table 5.2. The OLS results without auto-correlation are in table 5.2.A. The Durbin

Watson test was 1.22 (PR<DW=0.004 positive auto-correlation; PR> DW= 0.996 negative auto-

correlation) indicating the presence of auto-correlation. The BK test for seasonality was 0.73

(p<0.001), highly significant confirming the presence of seasonality.

The final parameters of the maximum likelihood regression correcting for first order auto-

correlation and seasonality are shown in table 5.2.B. At baseline, the rate was 13‰ person-

months and the trend before the CCC was increasing at a rate of 0.75‰ person-months

indicating a secular increase in trend prior to the CCC. Immediately after introducing the CCC,

the participation level increased by 8.2‰ person-months (p < 0.001), but then the trend

decreased at a rate of 1.5‰ (p<0.001). Summer season decreased participation by 1.9‰ person-

months (p=0.001).

The results of the auto-regression analysis stratified by individual and socio-demographic

characteristics presented in table 5.3 showed a significant increase in level of FOBT

participation across all population sub-groups. The lowest increase in participation was among

younger adults (3.02) and the highest was among individuals aged 71-74(5.9). The decrease in

trend was significant across all strata. Summer season effect was significant for all

characteristics except for individuals aged 71-74. Figures 5.2-5.6 in appendix 8 show the trend

of FOBT participation by individual and socio-demographic characteristic.

DISCUSSION

This study showed that screening for colorectal cancer using FOBT was steadily increasing

before the launch of the CCC. The CCC raised FOBT participation to its highest level (29.1‰

person-months). The estimated increase in level immediately after the CCC was 8.2‰ person-

months, followed by a declining trend (-1.5‰ person-months). We found summer troughs in

participation in every year (-1.9 ‰ person-months on average). Stratified results showed that the

significant increase in participation immediately after the CCC was across all population sub-

groups.

Several milestones may have contributed to the increase in trend in FOBT participation before

the CCC program. First, in 2001, the Canadian Task Force on Preventive Health recommended

FOBT as the entry screening test (CTFPH, 2001) which explains the higher uptake in FOBT

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participation than in colonoscopy in Canada (Jacob, et al., 2011). In contrast in the US, the

increase is mainly in colonoscopies (Carrie N. Klabunde, et al., 2011). Second, in 2004, the

Canadian Association of Gastroenterology and the Canadian Digestive Health Foundation also

recommended a biennial FOBT as a screening test(D. Leddin, et al., 2004). The consensus on

FOBT may have diffused the confusion among physicians about the effectiveness of FOBT and

given their pivotal role on screening participation, physicians recommended FOBT to their

patients more than ever before. Third, in fall 2007, a health care provider awareness and support

campaign was launched to harness the support of physicians prior to the public launch of the

CCC(CCO, 2008). These factors may explain the increase in trend before the CCC.

There was 8.2‰ person-months increase in FOBT participation immediately after the launch of

the CCC and was maintained for three consecutive quarters (until the end of December 2008).

The two elements of the program that may have contributed to this increase were the mass

media and physician incentives, both were launched on April 1st 2008. But, the mass media was

temporary and lasted for six months, while the physician incentives were permanent. We noted

that the peak after the launch lasted as long as the public media campaign was ongoing.

Moreover, in the second study of this thesis, we showed that physician’s recommendation

remained steady for the two consecutive years following the public launch. These two

observations put together may indicate that the sudden increase in level of FOBT participation

immediately after the launch may be attributed to the incremental effect of the public media

campaign on participation rates.

Public media campaigns are an efficient channel to convey health messages to all sectors of the

population regardless of their social characteristics (Anderson, et al., 2009). Media campaigns to

raise awareness about colorectal cancer screening were successfully used by the Centers for

Disease Control and Prevention (CDC)(Jorgensen, Gelb, Merritt, & Seeff, 2001; Randolph &

Viswanath, 2004). Media campaigns were also effective in raising awareness and changing

behaviors for smoking cessation, breast cancer screening, cervical cancer screening, and heart

health promotion (Baron, et al., 2008; Jacobsen & Jacobsen, 2011; Mullins, Wakefield, &

Broun, 2008). The evidence that public media campaigns become more effective when coupled

with organized programs was reported in smoking campaigns(NCI) and in cervical cancer

screening programs (Anderson, et al., 2009). Anderson et al. found that mass media campaigns

prompted increase in rates in cervical cancer screening among all women, regardless of their

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socio-economic status (Anderson, et al., 2009). Mullins et al. found that mass media campaign

increased cervical cancer screening for women who were due or overdue for screening(Mullins,

et al., 2008). Snyder et al. found 4% increase in cervical and breast cancer screening in

response to a televised marketing campaign linked to an organized screening program(Snyder,

et al., 2004). There is little evidence on how sustainable is the effect of mass media on

screening participation after it has stopped; and no evidence yet on the effect of mass media

campaign alone, without any accompanying intervention, on screening participation.

We noted that the overall increase in uptake of FOBT in 2008 was rather moderate (8.2‰

person-months). Studies looking at trends of performances of organized screening programs

showed that uptake usually starts low at the beginning improves slowly over time (D. Baker &

Middleton, 2003; P. H. A. o. Canada; Libstug, Moravan, & Aitken, 1998; T. N. I. C. NHS,

Public Health Indicators Team 2011). In 1990, when the Ontario Breast Cancer Screening

Program (OBCSP) started, the annual number of screens were 590, in 1991 it went up to 15,405,

and in 1995, reached 58,320(Libstug, et al., 1998). By 2006, there were 318, 421 annual number

of screens; the increase was incremental every year (P. H. A. o. Canada). In 2011, the

percentage of women screened for breast cancer, through organized and opportunistic screening

is approaching the 70% target set by the Ontario Breast Cancer Screening Program(CCO, 2008).

To achieve its objectives, the OBCSP program took two decades of persistent efforts, many

public campaigns and rallies as well as initiatives to increase awareness and adoption of

screening tests. Similar trends were observed for cervical cancer screening in England. Baker

and Middleton showed that the establishment of the National Cervical Cancer Screening

Program in 1988, started showing increase in participation in 1991-1993 (D. Baker &

Middleton, 2003) and as of March 2011, the percentage of eligible women aged 25 to 64 who

were screened at least once in 5 years was 78.6% (T. N. I. C. NHS, Public Health Indicators

Team, 2011). We anticipate a similar pattern for the CCC program. The FOBT participation

rates started low, expecting a substantial increase over time. Therefore, a longitudinal evaluation

of the program is required.

Perhaps the original finding of this study is the seasonality in FOBT participation. We observed

a decrease in participation in the summer quarter (June-September). One study reported seasonal

variation in colonoscopy and CT colonography in Australia (Segarajasingam, et al., 2006). The

authors found that participation decreased in winter and they attributed the decrease to work

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commitments that become greater in the end of the financial year. Given that winter months in

Australia are between June and September, the dry months of the year, our findings showed that

also in Ontario’s summer, individuals were unlikely to participate in FOBT screening. Perhaps

we can attribute these troughs in participation to summer holidays and people traveling for

vacations. The impact of this finding may have implications on the timing of the campaigns in

particular. Perhaps avoid campaigning in the summer and focusing on winter or spring may

have more impact on participation.

The increase in participation reached out to all sectors of the population almost in a parallel way

(Figures 5.2- 5.6 in appendix 8). However, the disparity gap persisted. Males, individuals aged

50-55, low income, recent registrants were regularly under-screened. The rural/urban gap was

removed (Figure 5.7, in appendix 8). These disparities were also noted in the English Bowel

Cancer Screening program thirty months after its implementation (von Wagner, et al., 2009) and

in the NHS Cervical Cancer program in its early days (D. Baker & Middleton, 2003). Victora et

al. proposed the ‘inverse equity hypothesis’ to interpret this phenomenon. The hypothesis

postulates that the most advantaged in the society make greater and earlier use of the program.

Therefore, inequities at the beginning of a program may get worse, or remain the same at the

best. However, over time the most advantaged reach a level of improvement beyond which it is

unlikely to make progress, and the least advantaged begin to catch-up, this is when equity

improves (Victora, et al., 2000). This hypothesis has been shown in several trends of organized

screening programs. Knowing that adoption among the least advantaged in the society comes

later, prompts the need to address the knowledge gaps, change attitudes and perceptions among

these groups in order to increase their participation. Tailored interventions based on behavioral

or social theoretical foundations are essentials to address these gaps.

STRENGTHS & LIMITATIONS

The strengths of this study include the use of population-based data, over a long period of time

and the ability to associate FOBT participation to individual and socio-demographic

characteristics. Moreover, using quarterly rate had enabled us to identify the seasonality in

participation. The visual inspection of the graph was intuitive enough to describe the trend of

participation over time, and the statistical technique had enabled us to measure the change in

level and trend in participation even without using a control group to compare to (Cook TD,

1979).

95

Methodologically, this study provided a robust effect size of the intervention. We used 22 time

points in total, balanced between pre and post intervention, and the effect size was above 2.

According to Zhang et al. (F. Zhang, et al., 2011), this study had enough statistical power

(estimated between 81% and 87%) to trust the results.

In this study, the data on physician’s claims do not differentiate between diagnosis and

screening, therefore our parameter estimates may be slightly inflated for screening. Perhaps

future studies need to consider using the CCC program billing codes only since they are

specifically used for screening purposes (Care, 2008). Another analytical limitation is using

ecological level data for measuring income. In spite of its validity (Mustard, et al., 1999), using

an ecological level data as proxy for individual characteristics is mainly associated with an

increased random measurement error due to heterogeneity within the group, which usually leads

to attenuation of the effect of this measure on the outcome as compared to an individual level

data (Greenland & Morgenstern, 1989).

Ideally, we should have used a control group to test the effect of the CCC on FOBT

participation. In an experimental setting, the randomization between study and control allows to

use a randomized controlled trial (RCT), the gold standard for testing the effect of an

experiment on the outcome controlling for biases. In natural experiments, randomization to

study and control is often not possible. So, the segmented regression analysis uses the pre-

intervention segment as a control for the post-intervention segment to control for secular trend

and biases related to the characteristics of the population, hence their appeal to statisticians more

than a pre and post cross-sectional design (Campbell, 1966; Inc., 2010; Ramsay, et al., 2003).

However, other potential confounders may exist in a quasi-experimental design including

simultaneous interventions and changes in the population characteristics (Wagner, et al., 2002).

In April 2008, there was no other intervention than the CCC affecting FOBT participation that

we are aware of and it is rather unlikely that the population characteristics may have changed in

a very short period of time. Although lack of control may still be a limitation, we are confident

that our statistical approach is quite robust.

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CONCLUSION

In conclusion, we found an increase in trend in participation before the CCC and troughs during

the summer season. The launch of the Colon Cancer Check program led to a temporary and

moderate increase in level of FOBT participation in Ontario. The increase was followed by a

declining trend. The program reached all sectors of the populations in the same manner. The

disparity by demographic characteristics persisted but did not increase. There is considerable

potential for improvement among all subgroups. Mass media may have contributed to the early

increase in rates, however we cannot affirm the association.

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Figure 5.2: Quarterly Rate of Fecal Occult Blood Test (FOBT) Participation per 1000 person-months, Ontario, 2005-2010.

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2005/06

2006/07 2007/08 2008/09 2009/10 2010/ 2011

N 3.148,382 3,261,582 3,382,216 3,511,224 3,483,685 3,588,293

Sex Females 1,607,510 (51%)

1,663,996 (51%)

1,724,069 (51%)

1,788,196 (51%)

1,768,247 (51%)

1,821,458 (51%)

Males 1,540,872 (49%)

1,597,586 (49%)

1,658,147 (49%)

1,723,028 (49%)

1,715,438 (49%)

1,766,835 (49%)

Age 50-55 1,070,059 (34%)

1,109,323 (34%)

1,151,872 (34%)

1,198,597 (34%)

1,193,784 (34%)

1,258,937 (35%)

56-60 744,189 (24%)

784,300 (24%)

824,030 (24%)

834,067 (24%)

813,665 (23%)

838,403 (23%)

61-65 555,265 (18%)

578,896 (18%)

601,572 (18%)

653,368 (19%)

665,016 (19%)

689,605 (19%)

66-70 455,199 (14%)

465,515 (14%)

477,598 (14%)

494,168 (14%)

488,844 (14%)

491,026 (14%)

71-74 323,670 (10%)

323,548 (10%)

327,144 (10%)

331,024 (9%)

322,376 (9%)

310,322 (9%)

Income Quintile 1 (low)

585,960 (19%)

601,401 (19%)

617,252 (18%)

635,790 (18%)

630,156 (18%)

650,967 (18%)

Quintile 2

623,627 (20%)

641,832 (20%)

660,867 (20%)

682,430 (20%)

674,166 (19%)

695,748 (20%)

Quintile 3

607,101 (19%)

630,291 (19%)

654,588 (20%)

680,164 (20%)

675,247 (20%)

693,465 (19%)

Quintile 4

629,818 (20%)

655,783 (20%)

684,371 (20%)

714,412 (20%)

709,875 (21%)

731,519 (21%)

Quintile 5 (high)

685,230 (22%)

710,738 (22%)

737,944 (22%)

765,754 (22%)

757,416 (22%)

774,158 (22%)

Recent Registrant

< 5 years 140,631 (5%)

172,026 (5%)

203,871 (6%)

150,350 (4%)

142,323 (4%)

146,446 (4%)

> 5 years 3,007,751 (95%)

3,089,556 (95%)

3,178,345 (94%)

3,360,874 (96%)

3,341,362 (96%)

3,441,847 (96%)

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Rural/ Urban

Rural 430,827 (14%)

443,288 (14%)

456,694 (14%)

471,087 (14%)

463,281 (13%)

467,239 (13%)

Urban 2.707,664 (86%)

2,803,739 (86%)

2.905,611 (86%)

3,015,155 (86%)

2,991,560 (87%)

3,089,790 (87%)

Eligible for CRC screening include individuals: aged 50-74, eligible for OHIP, excluding those who had CRC, colitis, crohn’s disease, and those exempted by their physicians.

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Table 5.2: Parameter estimates from the segmented regression analysis for the effect of the Colon Cancer Check program on FOBT participation per 1000 person-months with and without adjustment for auto-correlation.

Β Estimate Standard Error

P value

A. Ordinary least square regression

Baseline level at time zero

β0

13.4

1.03

<0.001

Baseline trend before CCC

β1 0.75 0.15 <0.001

Level change after CCC

β2

8.2

1.3

<0.001

Trend change after CCC

β3

-1.5 0.26 <0.001

Summer β4 -1.9 0.50 0.001

Durbin Watson

1.22

Pr<DW =0.006

Pr> DW= 0.994

BK Test

0.73 P< 0.001

B. Maximum likelihood estimation

Baseline level at time zero

β0

13.4

1.03

<0.001

Baseline trend before CCC

β1 0.75 0.15 <0.001

Level change after CCC

β2

8.2

1.35

<0.0001

Trend change after CCC

β3

-1.53 0.26 <0.0001

Summer β4 -1.9 0.50 0.001

Durbin Watson

1.66

Pr<DW =0.05

Pr> DW= 0.95

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Table 5.3: Parameter estimates for the segmented regression analysis for the effect of the Colon Cancer Check program on FOBT participation per 1000 person-months by demographic characteristics (* not significant)

Characteristic

Difference (CI)

Sex Levela Trendb Summer

Females 4.79 -0.81 -0.8

Males 3.34 -0.74 -1.15

1.45 (-0.47- 3.37) -0.07 (-0.4- 0.29)

Age Levela Trendb Summer

50-55¥ 3.01 -0.64 -0.79

71-74¥ 5.9 -1.18 -0.22*

-2.89* (-4.9- -0.8) 0.55* (0.3- 0.8)

Income Levela Trendb

Summer

Quintile 1 (low) 4.27 -0.68 -0.74

Quintile 5 (high) 3.8 -0.74 -1.26

0.47 (-1.4- 2.3) 0.06 (-0.3- 0.4)

Recent Registrant Levela Trendb

Summer

< 5 years¥ 3.57 -0.67 -0.2*

> 5 years 4.23 -0.77 -1.01

-0.66 (-2.5 – 1.2) 0.09 (-0.25- 0.43)

Rural/ Urban Levela Trendb Summer

Rural 3.88 -0.45 -1.48

Urban 4.42 -0.8 -0.89

-0.54 (-2.4 – 1.4) 0.36* (0 – 0.72)

a Level change after CCC b Trend after CCC * NOT Significant at 5% level. ¥ No correction for auto-correlation required

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CHAPTER 6: DISCUSSION AND CONCLUSION

Thesis Summary

Chapters 3, 4 and 5 of this thesis describe the effect of the Colon Cancer Check program on

colorectal cancer screening in Ontario. All three studies were based on cohorts of individuals

eligible for screening between 2005 and 2011 using various administrative databases. The

Socio-Ecological Model was used as a conceptual framework for this thesis.

The first study described the trend of FOBT participation rates and endoscopy participation rates

over time and the change in proportion of individuals who were ‘up-to-date’ with screening

according to guidelines between 2005 and 2011. An overall increase in ‘up-to-date’ with

screening guidelines status, increase in FOBT participation, and a modest but significant

increase in endoscopy were noted. The disparity by gender, age, recent registrant and income

persisted after the implementation of the Colon Cancer Check program. The urban-rural gap was

removed. Interventions are needed to increase the overall participation and to reduce disparities.

The second study looked at the association between FOBT participation and physician’s

recommendation using a cohort of individuals eligible for screening between 2008 and 2010.

Physician’s recommendation tripled the likelihood of FOBT participation (PRR=3.23, CI=3.22-

3.24) and moderated disparities by demographic characteristics. Physician’s recommendation is

an important strategy to increase participation and reduce disparities in participation.

Interventions optimizing the efficiency of primary care visits and increasing the capacity of

physicians to recommend the test are needed.

The third study estimated the effect of the Colon Cancer Check program on FOBT participation.

Using quarterly data, we observed a secular increase in participation before the CCC, troughs

during the summer season, a temporary but moderate increase immediately after the CCC,

followed by a decline in participation and then a plateau. The increase in level of FOBT

participation immediately after the CCC was significant for all sub-population groups.

Although we cannot affirm it, but it is likely that the media campaign may have contributed to

the increase in level immediately after the CCC.

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Implications and Recommendations

Practice

Increasing screening participation at the individual level requires interventions that increase

knowledge, change attitudes and beliefs, and motivate individuals to be screened. In a busy

office practice, simple interventions that use minimal resources and increase awareness includes

using educational videos in waiting areas, exam-room posters and printed educational booklets

(Brouwers, et al., 2011; Potter, Namvargolian, Hwang, & Walsh, 2009; J. G. Zapka, et al.,

2004). Client reminders are an effective strategy to motivate individuals to participate in

screening (Brouwers, et al., 2011; Fiscella & Epstein, 2008). Different team members play

different roles in managing screening in the practice. A panel manager prepares a patient-

tracking registry to identify individuals in need for screening and to generate a prompt list. The

panel manager scans the appointment list for the day, and using the prompt list flags individuals

in need for screening(IHI, 2011). During the visit, a dedicated staff (nurse, health educator, or

physician) discusses screening with the patient and hands-in an easy to read decision aid, a

translated version if necessary(O'Connor, et al., 2007). For individuals who do not visit the

practice, an email reminder to book an appointment to discuss screening with a link to a website

where they can get screening information (Chan & Vernon, 2008), and for non-email users, a

letter with an educational booklet, or a phone call inviting them to visit the practice and discuss

screening would be sufficient.

Strategies targeting the entire population are needed to achieve meaningful advances in overall

participation, but these strategies are not enough to eliminate disparities. Tailored interventions

should be used to target socially disadvantaged populations. Training physicians and other staff

the communication skills, the educational skills, behavioral modification theories and cultural

competency skills improve patient-centered care (Fiscella & Epstein, 2008), empathy (Smith, et

al., 1995), and responsiveness to patients’ questions (Brown, Butow, Dunn, & Tattersall, 2001).

Actively recruiting language concordant staff (IHI, 2011), using culturally appropriate

educational materials encourage socially disadvantage populations to be more engaged in their

own health care (Fiscella & Epstein, 2008). Using group education motivates ethnic groups to

participate in screening (Blumenthal, Smith, Majett, & Alema-Mensah, 2010). Removing or

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simplifying organizational barriers including using a patient navigator to help scheduling

appointments, organize transportation and explaining the process are particularly helpful for

linguistically challenged individuals (Christie, et al., 2008; Jandorf, et al., 2005)

Health policy

National cancer campaigns, such as ‘October Breast Cancer Awareness Month’ in Canada or the

CDC ‘Screen for life’ for colorectal cancer in the US, are meant to increase community

awareness about early detection for the disease. The theory behind this campaign is based on the

assumption that screening is driven by a herd signaling (Whynes, et al., 2007)which explains

why following the National Awareness Breast Cancer Movement’ in October in the US, a rise in

screening participation occurs(Jacobsen & Jacobsen, 2011) . The Canadian Cancer Society

recognizes the month of March as the month of Colorectal Cancer and Nutrition Awareness

(Canadian Cancer Soceity CCS, 2011). The publicity around colorectal cancer, however, still

lacks the high profile of breast and prostate cancer. There is a need to intensify lobbying for

more powerful national and provincial campaigns for colorectal cancer.

Reaching out to under-served groups would also require funding and support for local

community-based promotion projects. In summer 2011, the Public Health Agency of Canada

launched a funding opportunity for programs for early detection of cancer among underserved

populations(P. H. A. o. Canada). The focus of this fund was on initiatives to remove barriers,

increase awareness and promote participation among the underserved populations. Projects

included developing educational materials at health literacy and in languages appropriate to

underserved populations; tools to evaluate promotion programs targeting underserved

populations; systematic review for evidence-based interventions to increase participation among

underserved populations; sharing promising practices successful in raising participation.

Similar initiatives are highly needed in Ontario to encourage initiatives that reduce disparities in

participation.

Finally, the provincial primary care and cancer engagement practice strategy is the Ontario

program overlooking improving screening for colorectal cancer in primary care settings(Levitt

& Lupea, 2009). The objectives of this strategy are to integrate primary care services with

cancer care. These initiatives are needed to remove the organizational and political barriers for

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screening. What is missing is a strategy to improve access to screening among the under-served

population.

Research

CCC strategies

The Colon Cancer Check program uses physicians’ incentives as a provider-directed approach

to increase screening participation in Ontario. Financial incentives are meant to encourage

physicians to expand their delivery of screening services(Chassin, 2006). The effectiveness of

physician’s incentives on screening uptake is still inconclusive (Brouwers, et al., 2011;

Sabatino, et al., 2008). Physician incentives are usually used with other strategies. The extent

incentives alone affect the outcome is not clear. The extent the effect of incentives is

incremental to other strategies is also not know (Sabatino, et al., 2008). The cost-effectiveness

of financial incentives also needs to be explored. Furthermore, primary care is moving towards

team-based approach of care. In that model, nurses, health educators and physician’s assistants

are the ‘actual’ providers of FOBT tests. Why not give them the incentives? Qualitative studies

are needed to assess nurses and other providers’ satisfaction from the present system and

perhaps explore alternative methods for remuneration

The second provider-directed approach to increase screening participation is a multi-faceted

education program. The objectives of the educational program are to diffuse any confusion

among physicians about the evidence underlying anchoring the CCC program to FOBT and

colonoscopy, to clarify the process of ordering FOBT kits and reporting results to the patient, to

empower providers with counseling tools to assist them in education and provision of

service(OCFP, 2009). Post-evaluation was done following the sessions and a high number of

participants indicated that they would increase screening and screening awareness in their

practice. Knowing that changing practice requires more steps than simple dissemination,

qualitative case studies are needed to explore the challenges and opportunities for increasing

screening awareness and delivery in primary care practices.

Disparity indicators and continuous monitoring

The Colon Cancer Check program developed an evaluation framework and indicators. Their

first report is showing differences in quality of services by gender, age and geographic region in

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Ontario(CCC, 2010). Measuring performance is a cornerstone for improvement. The key in

performance measurement is the measure (IHI, 2011). The disparities in participation identified

in this thesis need to be continuously monitored and reported. We recommend adding an

indicator for disparities in participation and reporting it across other indicators. For example, the

indicator for follow-up following a positive FOBT test needs to be also stratified by

neighborhood income quintile, recent registrant status and possibly other demographic

characteristics such as ethnicity and language.

Thesis Limitations

A number of general limitations must be acknowledged in this thesis.

The administrative data used in this thesis have limitations:

• Four limitations were identified with billings on Ontario Health Insurance Plan (OHIP)

database. Billing on OHIP does not include providers outside the fee for service system,

which affects three geographic areas in Ontario (Fontenac county, Kenora and Sault St.

Marie) and salaried providers in community health centers. In this thesis we used the

laboratory requisition form in addition to physician’s billing fee codes to identify individuals

who received an FOBT. Therefore, we were assured that we captured almost all completed

tests regardless of the billing physician or the geographic region. The second limitation of

OHIP billing data is that for the period from 2005-2008, the fee code for FOBT does not

discern between tests done for diagnostic and those done for screening. After 2008, billing

using CCC codes were specifically for screening. In our analysis, we included all OHIP

billing, CCC and not. We assumed that regardless of the reason of the test, the individual

who received an FOBT is essentially screened. A third limitation of OHIP is the complexity

of billing for colonoscopy and simgoidoscopy. Physicians use a different code for each

segment of the colon reached. Sometimes, the overlap in fee code between an incomplete

colonoscopy and flexible simgoidoscopy makes it impossible to discern whether the OHIP

code submitted was intended for a colonoscopy or for a simgoidoscopy. So we combined the

two tests and reported the results on endoscopy participation in this thesis. Alternatively, we

could have used Shultz et al. (Schultz, et al., 2007) approach to arbitrarily separate between a

colonoscopy and simgoidoscopy. They defined flexible simgoidoscopy as endoscopy up to

but not beyond the splenic flexure. Colonoscopy was defined as endoscopy to the hepatic

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flexure and beyond. Finally, physician billing for physician’s endorsement is fairly new.

Physicians are asked to retain a paper trail for all correspondence with their enrolled patients

in order to receive the incentives. So far, no study explored the accuracy and completeness of

these claims.

• To identify our cohort, we used the Registered Person Database (RPDB). This database does

not collect information on socio-demographic characteristics; therefore we could not use

individual level data to measure income and rural status. Instead, we used ecological level

data to assign an income or rural status for the individual. The two validity issues with this

approach are: 1- measurement error; 2- construct validity. Assigning an income level status

to an individual based on the average income of the neighborhood introduces a measurement

error because of the heterogeneity in income within a neighborhood. A few individuals may

be richer or poorer than most others. But, reducing the size of the neighborhood reduces the

measurement error. In this thesis, we used the smallest standard geographic area

(Dissemination Area) to calculate the neighborhood income quintile, reducing heterogeneity

and measurement error. The issue with construct validity is more complex. Construct validity

answers the question ‘are we actually measuring what we think we are measuring?’ We think

we do in this thesis. Our interest in income and rural are to identify less advantaged

individuals who have less access to screening due to lack of access to resources present

within the social and physical environments (Mustard, et al., 1999) and compare their

participation to more advantaged individuals. In epidemiology, each of individual and

ecological characteristics exerts an independent effect on the outcome. So poverty as an

individual characteristic and poverty as a neighborhood characteristic exert different,

independent effect on health and replacing one by the other is argued to be an ecological

fallacy (Shwartz, et al., 1994). But in a nice analytical paper Mustard et al. showed the

validity of using ecological variables as proxy for individual variables and on that premise

we used it in this study (Mustard, et al., 1999).

The methodological limitations in this thesis:

• Ideally, in the time series paper we should have used a control group to test the effect of the

CCC on FOBT participation. In an experimental setting, the randomization between study

and control allows to use a randomized controlled trial (RCT), the gold standard for testing

the effect of an experiment on the outcome controlling for biases. In natural experiments,

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randomization to study and control is often not possible. The segmented regression analysis

uses the pre-intervention segment as a control for the post-intervention segment to control for

secular trend and biases related to the characteristics of the population (Campbell, 1966; Inc.,

2010; Ramsay, et al., 2003).

• ‘Contact with a physician’ variable assessed in chapter 4 was limited to ‘at least 1 contact, ‘at

least 2 contacts’ or ‘no contact’. Specific data on the number of visits was not measured in

this study. Intensity of visits is independently associated with increased screening

participation(Zarychanski, et al., 2007). Future studies need to include number of visits to test

the association between contact with a physician and screening participation.

Future studies

Trends of participation

In chapter 3 we described the trends of participation by certain demographic characteristics. We

originally planned to include ethnicity and ability to speak official language in our analysis.

Using 2006 Census, we created one variable for neighborhood ethnicity. We calculated the

proportion of ethnic groups in each DA and using the Herfindhal index we measured the ethnic

concentration of each DA. We had three levels of neighorhood ethnic concentration (EC): high

ethnic concentration (herfindhal index > 0.6 and assign an ethnicity for that DA), moderate (0.3-

<0.6); low (<0.3). In this approach, more than 90% of DA had low EC. We decided to drop this

analysis because of the quality of the data. Future studies need to use different database such as

the Landed Immigrant Data System (LIDS) and link it to OHIP data to conduct the analysis.

As suggested above (Research section, Implications and Recommendations), trends over time

need to be continuously monitored and most importantly disparities in participation need to be

reported and addressed.

Misuse and over-use of colorectal cancer screening tests have been reported in US(Ko, et al.,

2010). This thesis concentrated on under-use and disparities in use. Future studies need to look

at over-use and identify the demographics associated with over-use.

Physician’s endorsement

109

Future studies need to explore the characteristics of physicians providing CRC services in

Ontario. We are particularly interested in testing whether physicians affiliated with a

multidisciplinary team of provider (Family Health Team) are more likely to endorse CRC

screening tests than solo or multi-group physicians. Using the Institute for Clinical Evaluative

Sciences Physician Database (IPDB) and linking to OHIP database, we should be able to

conduct this analysis.

Over 6000 physicians have adopted electronic medical records in their practice. Physicians that

are using EMRs have reported improvements in patient safety, continuity of care and overall

quality of care. Physicians are using the systems to write and renew prescriptions, manage lab

results, and for preventive care measures. Whether electronic medical records are improving

colorectal cancer screening tests needs to be empirically tested.

Evaluation of the Colon Cancer Check program

In 2011, the Colon Cancer Check program initiated an auditing and feedback strategy directed

towards primary care providers. A screening assessment report is sent to physicians enrolled in

Patient Enrolment Models (PEM) to report on the proportion of individuals screened on their

panels. The first round of report collection started February 2011. Two more rounds of

assessment will be done and then the CCC will send the results back to the physicians. This

approach is meant to prompt physicians to modify their practice if they are given feedback that

their clinical practice was inconsistent with that of peers or guidelines (Jamtvedt, Young,

Kristoffersen, O'Brien, & Oxman, 2006b). The auditing and feedback approach is particularly

effective for practices with low baseline adherence to screening (Jamtvedt, et al., 2006b;

Pattinson) and depending on the intensity of reports it can have a small to moderate absolute

effect on screening participation (Brouwers, et al., 2011; Jamtvedt, Young, Kristoffersen,

O'Brien, & Oxman, 2006a). The data collected from these assessment reports over time will be

useful for a future study testing the influence of audit and feedback on physician’s practice

pattern in Ontario.

In December 2010, the Colon Cancer Check program started a client-directed approach sending

recall/reminder letters to individuals who are due for a repeat biennial FOBT test and invitation

to individuals turning 50. In fact, in chapter 5, we indicated that we decided to end the analysis

on September 1st 2010 rather than March 31st 2011, due to the introduction of a new

110

intervention, the recall letter program (Care, 2011). Future study needs to look at the effect of

the recall/reminder/invitation letters on CRC screening participation preferably using an

interrupted times series which controls for auto-correlation in the data.

Conclusion

The focus of this thesis was on the effect of the organized screening program, Colon Cancer

Check, on colorectal cancer screening in Ontario. Three major themes were addressed: 1- the

trend and disparities in screening participation over time; 2- physician’s influence on

participation; 3- policy and community awareness effect on participation. We found that the

trend in CRC testing is increasing but remains suboptimal. Disparities by gender, income, age

and recent immigrant status were resilient over time. Physicians play a major role in increasing

participation and reducing disparities. The CCC program launch led to a temporary and modest

increase in participation and significantly affected participation among the under-screened. Our

results suggest we are moving towards the right direction in colorectal cancer screening in

Ontario, but there are still lots of opportunities to increase the trajectory of CRC screening

uptake in the province.

111

Appendices

Appendix 1: Characteristics of colorectal cancer screening tests used in Ontario4

Guaiac Fecal Occult Blood Test (gFOBT)

Double Contrast Barium Enema

Flexible sigmoidoscopy

Colonoscopy

Goal To detect through a chemical reaction a small amount of blood in the stool not visible to the naked eye.

To inspect the outline of the intestine using X-rays.

To inspect the lower part of the colon, but not the upper part, for polyps and adenomas and remove them.

To inspect the intestinal cavity for adenoma and cancer and remove them

Procedure Client is asked to collect three stool smears in three different days and to send them to the laboratory.

A barium solution is inserted into the rectum. Then air is pumped into the intestinal cavity. X-rays are taken from different angles

A soft flexible tube with a camera connected to a video monitor is inserted in the rectum up to the lower part of the colon.

A thin, flexible long tube with a camera connected to a video monitor is inserted in the rectum. The procedure is complete up to cecum or incomplete to hepatic flexure

Recommended frequency

CCC recommends a biennial test followed by a colonoscopy for those who test positive

Every five years

Every five years For low risk: every ten years

For high risk every 5 years

Provider (s) Primary care provider –laboratory

Radiology technician

Nurse practitioner- General physician

Specialist- radiologist

4 (CCAC, 2011)

112

Appendix 2: Colon Cancer Check physician incentives

Service provided Patient Enrolment Model (PEM)

Fee for service

Compensation Fee code

Amount

FOBT distribution and counseling fee

Yes Yes Fee for service Q150A $ 7

CRC screening Management fee

Yes

No

Fee for service

Q005A

$ 6,86 *

FOBT Completion fee

Family Health Group (FHG) & Comprehensive Care Model (CCM) with roster size: 450-650

Yes

Fee for service

Q152A

$ 5

CRC preventive care bonus

Yes

No

End of year bonus

Q118-Q123A

Based on threshold ranges from $220 to $ 4000 a year

New patient fee (FOBT positive)

Yes No New patient fee Q043A $150 for ≤ 64 yrs $170 for 65 –74 yrs $230 for ≥ 75 yrs

* A paper trail needs to be provided as evidence for claiming this fee.

113

Appendix 3: Data linkage flow chart

Data source Use Purpose

Registered Persons Database

(RPDB)

↓ (linked to)

Provides basic demographic

information on individuals

eligible for health care in

Ontario, their contact with

health care system and their

postal code

Cohort identification

↓

Canadian Institute of Health

Information- Discharge

Abstracts Database (CIHI-

DAD)

↓

Database of information

abstracted from hospital

records includes patient

demographic data, acute and

chronic hospital care,

diagnostics tests, and other

administrative information.

To identify and exclude

individuals with colitis and

crohns’ disease

(ICD-9 codes: 556, 556.0 to

556.9 and 555, 555.0 to

555.9; ICD-10 codes: K500,

K501, K508 to K515)

↓

Ontario Cancer Registry

(OCR)

↓

Includes all Ontario residents

who have cancer or who died

from cancer.

To identify and exclude

individuals

who have CRC

(ICD-9 codes: 153.0-153.4;

153.6-154.1; ICD-10 codes:

C18, C19, C20, C21, C180,

C182-C184, C186-C189)

↓

Ontario Health Insurance Contains claims for services To exclude individuals

114

Plan (OHIP)

↓

provided by eligible

physicians, groups, and

laboratories.

exempted from tests by their

physician.

The exclusionary code for

colorectal screening Q142 is

used for the following:

(i) Enrolled Patients with

known cancer being

followed by a physician;

(ii) Enrolled Patients with

known inflammatory bowel

disease;

(iii) Enrolled Patients who

have had colonoscopies

within five (5) years;

(iv) Enrolled Patients with a

history of malignant bowel

disease; and

(v) Enrolled Patients with

any disease requiring regular

colonoscopies for

surveillance

&

To identify individuals who

received bowel tests

↓

2006 Census Data Provides general Using the postal code

115

demographic information on

100% of the population (e.g.

age, sex) and other

information, such as

education, language and

income, on 20% of the

population.

conversion File PCCF+, the

postal code on the cohort is

linked to the dissemination

area on Statistics Canada.

PCCF+ flags the

neighborhood income

quintile and the urban/rural

status.

116

Appendix 4: Definition of demographic variables

Variable Definition Source Type

Individual level variables

Sex Individual’s gender Registered Person Database (RPDB)

Dichotomous: Female, Male

Age Individual’s age in years at the beginning of each cohort

RPDB Continuous variable transformed into categorical:

50-55; 56-60; 61-65; 66-70; 71-74.

Recent registrant Start of eligibility for Ontario Health Insurance Plan.

Ontario Health Insurance Plan

Dichotomous:

< 5 yrs

> 5 yrs

Ecological level

Neighborhood income quintile

Individual income status divided into quintiles based on neighborhood income from 2006 Census data

2006 Census – Statistics Canada

Ordinal variable:

Quintile 1 (lowest)

Quintile 2

Quintile 3

Quintile 4

Quintile 5(highest)

Rural The size of the community where the individual lives. A community size < 10,000 is rural

2006 Census-Statistics Canada

Dichotomous variable:

Urban

Rural

117

Appendix 5: Person-month calculation flowchart

118

Appendix 6: Definition of explanatory and outcome variables

Variable Definition Source

Objective 1: Trends and disparities in CRC screening participation

Age standardized percent of FOBT participation

Percent of individuals aged 50-74 who completed their index FOBT test in each year from April 1st 2005 to March 31st 2011 standardized to the 1991 Census population.

OHIPa

(L179, L181, G004

Q152, Q118-Q123)

Age standardized percent of endoscopy participation

Percent of individuals aged 50-74 who completed their index flexible simgoidoscopy or colonoscopy in each year from April 1st 2005 to March 31st 2011 standardized to the 1991 Census population

OHIP (Z580, Z555,

E740, E741, E747,

E705)

Age standardized percent of ‘up-to-date’ status.

Percent of individuals aged 50-74 who received at least one test in each cohort year in addition to those who received at least one FOBT in previous year or those who received at least one flexible simgoidoscopy or colonoscopy or barium enema in previous four years standardized to the 1991 Census population

OHIP (L179, L181,

G004, Q152, Q118-

Q123, Z580, Z555,

E740, E741, E747,

E705, X112, X113)

Objective 2: The influence of physician’s recommendation on FOBT participation Physician’s recommendation

Discussing and dispending FOBT in office or calling or sending a letter to those who did not visit the practice

OHIP (Q005, Q150)

Contact with physician Having a ‘virtual physician’ indicates the individuals had at least one visit to a primary care provider

OHIP

Prevalence rate ratio of FOBT participation The number of individuals who

Completed an FOBT in two years. A log binomial regression gave the prevalence rate ratio and its 95% confidence interval.

Objective 3: The effect of the CCC on FOBT participation using interrupted time series Trend before CCC The quarterly increase in FOBT

119

participation before the CCC. Change in level after CCC The change in percentage

Immediately after CCC

Change in trend after CCC Change in intercept after CCC a = Ontario Health Insurance Plan

120

Appendix 7: Segmented regression statistical analysis

The following programs were used for the interrupted time series:

1- For visual inspection of the data:

Proc SGPLOT data=segmented noautolegend;

Series x=time y=FOBT/markers;

Reg x=time y=FOBT/lineattrs = (color=black);

Run;

2. For running a simple regression analysis using AUTOREG without any correction

proc autoreg data=segmented; model FOBT=Time intervention timeafterintervention summer; run;

3. The following SAS code was used to plot the residual terms to test for auto-correlation:

proc reg data=segmented;

model rate = time intervention timeafterintervention summer;

plot rstudent.*obs.

/vref= -1.714 1.714 cvref=blue lvref=1

href = 0 to 60 by 5 chref=red cframe=ligr;

plot predicted.*residual.;

run;

4. Testing for autocorrelation using generalized Durbin Watson test, we used

proc autoreg data=segmented;

model rate=time intervention1 timeafterintervention Summer/ dwprob;

run;

5. Testing for seasonality (heteroscedastic), we used Proc SPECTRA data=segmented out=b p s adjmean whitest; var FOBT; weights 1 2 3 4 3 2 1; run;

6. This is the final program with correction for auto-correlation and seasonality

121

proc autoreg data=segmented;

model rate=time intervention timeafterintervention summer / method=ml nlag=1;

run;

7. For the overall effect of the CCC program on level change:

β2 + β4 / √ (SE (β2)2 + SE(β4)2 1. For the overall effect of the CCC program on slope change.

β3 + β5 / √ (SE (β3)2 + SE(β5)2

8. For differential effect by gender, age, income, recent registrant and rural as such:

The null hypothesis:

H0 = β3 (females) = β3 (males)

Ha = β3 (females) ≠ β3 (males)

Z= β3 (females) - β3 (males) / √ (SE (F)2 + SE(M)2

122

Appendix 8: FOBT participation by quarter per 1000 person-months by demographic

characteristics, Ontario, 2005-2010

124

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