Building on Existing Tools To Improve Chronic Disease Prevention and Screening in Public Health: Durham
Phase: NA
Conditions: Cancer and Chronic Disease Prevention
Sponsor: Unity Health Toronto

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RESEARCH PROTOCOL 
 
 
 
BETTER HEALTH: DURHAM 
BETTER = Building on Existing Tools To Improve Chronic Disease Prevention and Screening 
 
Funded as a research grant entitled:  Advancing Cancer Prevention in Deprived Neighbourhoods 
Funding agencies: 
 
Canadian Cancer Society Research Institute and  
 
 
 
 
Canadian Institutes for Health Research, Institute of Cancer 
Canadian Cancer Society Research Institute grant #704042 
Canadian Institutes for Health Research Institute of Cancer OCP #145450   
 
 
Principal investigators: Aisha Lofters, St. Michael’s Hospital 
Lawrence Paszat, Sunnybrook Hospital  
 
 
 
Andrew Pinto, St. Michael’s Hospital 
 
 
 
 
Mary Ann O’Brien, University of Toronto 
 
 
 
Mary-Anne Pietrusiak, Durham Public Health 
 
 
 
Regina Elliott, Durham Public Health 
 
 
 
Becky Wall, Durham Public Health 
 

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Contents 
1.0 
INTRODUCTION TO BETTER HEALTH: DURHAM  
 
 
 
 
2.0 
GOAL of BETTER HEALTH: DURHAM 
 
 
 
 
 
 
3.0 
Primary objective and methods 
 
 
 
 
 
 
4.0 
Secondary objectives and methods  
 
 
 
 
 
 
5.0 
ANALYSIS PLANS  
 
 
 
 
 
 
 
6.0 
References  
 
 
 
 
 
 
 
 
 
Appendix 1: 
Comparison of the original BETTER study in primary care22, and this project 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix 2: 
BETTER HEALTH: DURHAM 
Pathway of participants and their quantitative data 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix 3: 
BETTER HEALTH: DURHAM 
Quantitative analysis plan 
 
 
Appendix 4: 
Table of eligible Chronic Disease Prevention and Screening actions / activities 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix 5: 
Brief Action Planning as the approach to interviewing  
Appendix 6: 
Steps to Adapt the BETTER Intervention  
 
 
 
 
Appendix 7: 
Team of investigators for BETTER HEALTH: DURHAM   
 
 
Appendix 8: 
Required contracts 
 
 
 
 
 
 
 
Appendix 9: 
Data security, privacy, confidentiality and management at the Applied Health   
 
 
Research Centre, St Michael's Hospital                                                             

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1.0 
INTRODUCTION TO BETTER HEALTH: DURHAM 
1.0 
Introduction:  chronic disease prevention and screening 
Smoking cessation, healthy diet, sufficient physical activity, reduction / elimination of alcohol 
use reduce the risk of developing and dying from cancers, diabetes, and circulatory diseases8.  Cervical 
and colorectal cancer screening prevent the evolution of precursor lesions to invasive cancer and detect 
invasive cancers at localized stages, leading to decreased cancer-related mortality9,11. Mammography 
may reduce breast cancer related mortality10. 
1.1 
Chronic disease screening in deprived areas 
 
We recently studied persons eligible for breast, colorectal, cervical, glucose, and / or lipid 
screening, and the percent of eligibles with recent participation, in all dissemination areas (referred to 
as 'small areas', n= 18,950) in Ontario18.  Colorectal, breast, cervical, glucose and lipid screening are 
lowest in low income areas, especially among those without a regular Primary Care Physician (PCP)18,54b. 
Table 1: 
Screening participation rates by median household income quintiles in Ontario 
Quintile of median household income Colorectal Breast Cervical Glucose Lipids 
Highest quintile overall 
64.6% 
68.6% 
76.5% 
69.2% 
85.5% 
Lowest quintile  with regular PCP 
51.3% 
56.1% 
63.3% 
67.1% 
83.1% 
Lowest  without regular PCP 
9.7% 
8.7% 
12.1% 
8.6% 
23.25% 
 
 
 
Among low income clusters, a significant proportion of persons aged 40 - 64 years who have not 
yet developed circulatory, diabetic, or cancer morbidity, do not receive primary care, from one year to 
the next.  
Among these small areas, the median for enrollment by females  in a Patient Enrollment 
Model  PCP54b (whether or not they are able to see that physician) is only 72.1% (interquartile range IQR 
67.9% - 74.4%) and by males is 60.9% (IQR 56.6 - 63.4)18, compared to 86% (IQR 81% - 91%) overall for 
all females and 83% (IQR 76 - 88%) overall for all males18.   Low income is not the only factor associated 
with disparities in preventive and screening activities, and poor health outcomes, but it is the most 
common, negatively affecting many of the 1,320,000 premorbid persons aged 40 - 64 living in the lowest 
income quintile18. 
1.2 
Promotion of preventive behaviours, and activities (including screening) in Ontario  
 
In general, chronic disease preventive and screening behaviours are promoted as discrete, 
‘stand-alone’ activities, as opposed to an integrated set of Chronic Disease Preventive and Screening 
actions (CPDS)22.  Screening for cervical, colorectal, and breast cancer (and their risks), and assessment 
and screening for cardiovascular and diabetes risk factors, occur mainly in primary care, with minimal 
programmatic facilitation of participation (CancerCareOntario invitations to cervical and colorectal 
screening instruct the person to attend their regular PCP11b, which is impossible for those who have not 
had a regular PCP).  By regular PCP, we mean a PCP whom a person is able to consult periodically, and 

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who is the same physician seen at the majority of medical non-specialist visits during the previous two 
years(54b). 
 
Despite major increases in funding for PCPs and Patient Enrollment Models of Practice, and 
financial incentives to PCPs for cancer screening, participation in cancer screening has not improved28,32 . 
For those in low income clusters, having to attend a PCP office may be a barrier, because of impaired 
access to PCPs18, which has not been ameliorated by these strategies.  Although community health 
centres target those with multiple morbidities in low income clusters, and although Family Health Teams 
receive funds for preventive activities, these strategies have not had an impact on screening rates 
among low income clusters overall 5,18. 
1.3 
Addressing low overall participation in Chronic Disease Preventive and Screening (CDPS) 
actions by the ‘BETTER’ Prevention Practitioner Intervention 
 
The 'BETTER' prevention practitioner intervention3,22 was designed with several goals: to 
integrate the approach to CDPS actions22, to optimize participation in CDPS actions, in primary care 
practices22, and to create a feasible, evidence-based intervention8 to motivate individual 40 -  64 year-
old persons to undertake CDPS actions for which they are eligible but not currently undertaking22.  The 
'BETTER' prevention practitioner intervention involves assessment of a person’s current participation, or 
lack of participation, among  domains of evidence-based CDPS actions by a research assistant.  The 
assessment is followed several days later by a supportive meeting with a prevention practitioner nurse, 
using principles of shared decision making, health coaching, and brief action planning as mode of 
interviewing, to establish goals for accomplishing CDPS activities of the individual’s choice during the 
subsequent six months.,22,42, 44,55, 57 to develop personal goals and targets for participating in CDPS 
actions during the following six months.  In BETTER HEALTH: DURHAM, the prevention practitioner 
nurse will be a public health nurse from the Durham Region Health Department. 
 
The original 'BETTER' cluster randomized trial compared the outcomes of the 'BETTER' 
prevention practitioner intervention at the individual patient level within allocated clusters of PCPs in 
Family Health Teams, to patients who did not receive the intervention.  In the adjusted analysis, control 
patients met 23.1% (95% CI: 19.2% to 27.1%) of target actions, compared to 55.6% (95% CI: 49.0% to 
62.1%) receiving the patient-level intervention p< 0.001).22 This result was replicated in an 
implementation study42 among primary care practices in communities in Newfoundland and North West 
Territories23,42; that study reveals how contextual factors influence implementation42. 
2.0 
GOAL of BETTER HEALTH: DURHAM 
 
The goal of this proposal is to improve participation in CDPS actions in low income clusters in 
Durham Region, by implementing the ‘BETTER’ prevention practitioner intervention, in the community 
setting rather than in a medical practice setting, among persons in low income clusters characterized by 
the lowest quintile of median household income and with lowest participation rates in cancer screening 
as previously identified by our prior work18. We will adapt promotion, recruitment, delivery of the 
‘BETTER’ prevention practitioner intervention to the needs of residents of these clusters using 

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participatory research methods involving residents as well as stakeholders within the Durham Region, 
and we will also adapt the facilitation of CDPS actions to this scenario of deprivation. 
3.0 
Primary objective and methods 
To detect an absolute increase of 30% or greater in the number of completed or ongoing CDPS actions, 
at an outcome assessment, six months after informed consent and baseline assessment of eligibility for 
each CDPS action, among participants in small areas randomized to be offered immediately the  
‘BETTER’ prevention practitioner intervention, compared to participants in wait-list control small areas 
(6 month waitlist prior to an outcome assessment and then being offered the ‘BETTER’ prevention 
practitioner intervention). 
 
Participants in immediate intervention clusters and in wait-list clusters alike will receive standard 
printed material about chronic disease prevention and screening, as routinely available at the Durham 
Region Health Department (DRHD), following completion of a baseline survey interview administered to 
all participants who have given informed consent. 
3.1 
Study design: BETTER HEALTH: DURHAM 
 
We have planned a cluster randomized trial of the timing of the BETTER prevention practitioner 
intervention in order to test its effectiveness in the community setting of low income clusters, as 
opposed to the highly favourable Family Health Team practices which participated in the original BETTER 
study by Grunfeld et al22.  The clusters are comprised of eligible low income census dissemination areas, 
or groups of eligible low income census dissemination areas.  A total of 10 clusters will be randomly 
selected from a sampling frame of eligible clusters and 5 will be randomly assigned to be offered   
immediate prevention practitioner intervention and 5 will be randomly assigned six-month wait-list 
control (six month wait-list prior to prevention practitioner intervention).  Study activity among the 
randomly selected and assigned clusters will be temporally staggered in order to maximize the efficiency 
of the research assistant prevention practitioner nurses. 
" Pilot study: Prior to commencement of the study among the 10 randomly selected and assigned 
clusters, we will conduct a pilot study involving 6 consenting participants in each of two eligible clusters 
(one of which will be a six-month wait-list cluster) in order to assess the feasibility, appropriateness and 
suitability of promotion, recruitment, consent forms, and data collection instruments prior to the formal 
commencement of recruitment for the definitive study. " 
 
Table 2  
Timeline for intervention clusters and control clusters 
Month >> 
1 
2 
3 
4 
5 
6 
7 
8 
9 
Intervention  
Clusters 
 
Promotion, 
recruitment, 
informed 
consent,  
Intervention, 
CDPS 
activities for 
6 months 
after  
Intervention 
Outcome  
measure 
 
 

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facilitation 
Control 
Clusters 
 
Promotion, 
recruitment, 
informed 
consent,  
Standard 
information 
CDPS 
activities for 
6 months 
after 
INFORMED 
CONSENT 
Outcome 
measure 
and offer 
intervention, 
facilitation*  
* the outcome assessment is at 6 months post informed consent.  For those participants in wait-list 
control clusters there will NOT be a second formal outcome assessment 6 months after the prevention 
practitioner intervention (i.e. no second formal outcome assessment 12 months post informed consent 
among 6 month ‘wait-list delay’ clusters).  
From day 1 in each cluster, there will be three months of recruitment, informed consent, 
assessment, and in the case of intervention clusters, the prevention practitioner intervention. Each 
participant then will have 6 months to participate in CDPS actions prior to outcome assessment.  
Recruitment will be initiated in additional clusters approximately every three months. The rationale for 
control being a six-month ‘wait-list’ control is scientific and ethical:  the challenges and barriers for CDPS 
actions are so great in the eligible low income clusters that a quantitative unbiased outcome comparison 
is required in order to study effectiveness of the prevention practitioner intervention in this context, in 
order to assess the scalability of this intervention outside of medical practices in communities with 
impaired access to primary care.  We will compare participants living in immediate prevention 
practitioner intervention clusters to six-month wait-list control clusters, in order to obtain an unbiased 
estimate of the effect of prevention practitioners in low income clusters. On the other hand, it is 
unethical to request participants in control clusters to participate in outcome assessments but fail to 
provide them the opportunity to receive the prevention practitioner intervention, so those participants 
will be offered this intervention 6 months later, although the outcome of the delayed intervention is not 
a study outcome and will not be formally assessed or analyzed.  
3.2 
‘BETTER HEALTH: DURHAM’: Study population and sampling frame of clusters for 
randomization 
 
The sampling frame of clusters consists of low census dissemination areas (in Durham Region), 
where the lowest quintile of median household income and low cancer screening rates coincide.  Low 
income clusters with low cancer screening rates have been identified by previous work18.  
 
The eligible population for recruitment in these clusters is men and women 40 - 64 years of age.  
Eligibility includes: homeless persons encountered in the low income clusters, persons already affected 
by one of the target cancers, diabetes, and / or circulatory diseases, persons up to date with any 
screening test, already engaged in some risk-reducing behaviours, or already meeting any risk-reducing 
targets at baseline assessment, illiterate English-speaking persons.  Strategies to maintain 
communication with persons who are homeless or have difficult reading will be developed in each 
cluster.   Exclusions: non-English speakers. Only one person per household may participate in the 
study. Recruitment of men will be a challenge as only 30% of the participants in the original BETTER 
randomized trial were men. 

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" We will purposively sample two eligible clusters from the sampling frame for the pilot study. These 
two clusters will then be removed from the sampling frame. " 
 
3.3 
BETTER HEALTH: DURHAM: Random assignment of clusters to immediate prevention 
practitioner intervention versus 6-month wait-list control prior to intervention) 
 We will randomly sample low income clusters from the sampling frame in Durham Region, using the 
"sample( )" function contained in the statistical software package 'R'.  A random number generator in R 
will then be used to randomly allocate clusters to the immediate intervention or to the wait-list control 
(the 6 month delayed intervention).   The allocation process will be concealed from investigators. 
3.4 
‘BETTER’ Intervention - Sample size calculation for effect size and adjusting for clustered 
exposure 
With an equal number of participants in 5 intervention clusters and 5 wait-list control clusters 
(the average number of eligible residents per cluster being 342), a total sample of 120 participants (on 
average 12 participants per cluster) will be required to detect an increase of 30% or greater in the 
composite index score (reflecting additional CDPS actions met among the intervention clusters 
(compared to the control clusters).  The calculation is based on a two-sample comparison of means with 
80% power and a 0.05 alpha. The calculation accounts for the design effect (correction factor 
determined as (1+(m-1)*rho)) arising from the clustered design, and an intracluster correlation 
coefficient (ICC) of rho = 0.23722. The ICC is a measure of the relatedness of the clustered data.  
3.5 
BETTER HEALTH: DURHAM   Promotion and recruitment  
Small area / community based strategies for promotion and recruitment(1,7,15,16,17,25,52)  will be 
constructed  using participatory research methods (see below, secondary objectives) to design cluster 
specific strategies with published evidence of effectiveness(1,7,15,16,17,25,52).  Online methods of 
recruitment, assessment and facilitation might exclude the majority of our target population(44). 
Members of our team have been successful in community based recruitment(33,37). Staff members at the 
DRHD know these clusters well, are experienced in health promotion campaigns among them, and are 
vital in the design and implementation of this strategy.   
3.6 
BETTER HEALTH: DURHAM 
 Informed consent and research ethics  
 
This research will be compliant with the Tri-Council Policy Statement on research ethics and the 
Ontario Personal Health Information Protection Act (2004). 
 
We consider that the study population should be considered vulnerable from the point of view 
of economic deprivation and impaired access to primary care and will ensure to incorporate principles 
From the TCPS on the three core principles of Respect for Persons, Concern for Welfare, and Justice in 
all aspects of contact with potential and consenting participants in BETTER HEALTH: DURHAM 

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"Respect for Persons recognizes the intrinsic value of human beings and the respect and consideration 
that they are due.  It encompassess the treatment of persons involved in research directly as 
participants....Respect for Persons incorporates the dual moral obligations to respect autonomy and to 
protect those with developing, impaired, or diminished autonomy." 
"Autonomy includes the ability to deliberate about a decision and to act based on that deliberation.  
Respecting autonomy means giving due deference to a person's judgment and ensuring that the person 
is free to choose without interference.  Autonomy is not exercised in isolation but is influenced by a 
person's various connections to family, to community, and to cultural, social, linguistic, religious and 
other groups.  Likewise, a person's decisions can have an impact on any of these connections." 
"Concern for Welfare:  The welfare of a person is the quality of that person's experience of life in all its 
aspects.  Welfare consists of the impact on individuals of factors such as their physical, mental, and 
spiritual health, as well as their physical, economic, and social circumstances.  Thus, determinants of 
welfare can include housing, employment, security, family life, community membership, and social 
participation, among other aspects of life.  Other contributing factors to welfare are privacy and the 
control of information about the person....according to the free, informed and ongoing consent of the 
person who was the source of the information or materials.  A person's or group's welfare is also 
affected by the welfare of those who are important to them..." 
"Justice refers to the obligation to treat people fairly and equitably.  Fairness entails treating all people 
with equal respect and concern.  Equity requires distributing the benefits and burdens of research 
participation in such a way that no segment of the population is unduly burdened by the harms or 
research, or denied the benefits of the knowledge generated from it.  Treating people fairly and 
equitably does not always mean treating people in the same way.  Differences in treatment or 
distribution are justified when failures to take differences into account may result in the creation or 
reinforcement of inequities.  One important difference that must be considered for fairneess and equity 
is vulnerability.  Vulnerability is often caused by limited decision-making capacity, or limited access to 
social goods, such as rights, opportunities and power.  ....  People or groups whose circumstances cause 
them to be vulnerable or marginalized may need to be afforded special attention in order to be treated 
justly in research." 
 
From the TCPS on Respect for Persons and consent:   
"Respect for persons implies that individuals who participate in research should do so voluntarily, 
understanding the purpose of the research, and its risks and potential benefits, as fully as reasonably 
possible.  Where a person has the capacity to understand this information, and the ability to act on it 
voluntarily, the decision to participate is generally seen as an expression of autonomy.  The Policy refers 
to the process of seeking consent from prospective participants, which may result in either agreement 
or refusal to participate.  This process is meant to emphasize Respect for Persons.  Under no 
circumstances may researchers proceed to conduct research with anyone who has refused to 
participate." 

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For participants in eligible and randomly selected and allocated low income clusters Informed 
consent will be requested for (1) collection of personal health information and personal contact 
information, and (2) for repeat contact at 6 months for outcome assessment.  The study research 
assistant will obtain informed consent. Informed consent will also be obtained from all participants in 
key informant interviews and focus groups conducted in the course of the application of participatory 
research methods and integrated knowledge translation. 
The study population living in the low income clusters is a vulnerable population. All study staff 
who will have contact with members of the study population will receive additional orientation to 
respectful relationships with this vulnerable population. 
 
 
3.7 
BETTER HEALTH: DURHAM  Baseline Data collection 
 
An entry survey will be administered as an interview by a research assistant, rather than self-
administered as in the original BETTER study, because of literacy issues.  Previous participation in, and 
eligibility for, CDPS actions will be determined by self-report. This will be administered in a variety of 
settings:  for example, community centres and other safe venues identified by the participatory research 
methods in the low income clusters and environs. Data will be entered electronically with privacy / 
confidentiality, backup and data transfer conditions compliant with the requirements of the Office of 
the Privacy Commissioner of Ontario and the Research Ethics Board of Sunnybrook Health Sciences 
Centre.  Data will be securely entered electronically directly to a database application created for 
BETTER HEALTH: DURHAM on a secure server the Applied Health Research Centre, St Michael's Hospital, 
Toronto (AHRC), access to which will be severely limited to the prevention practitioner nurses and to 
staff at AHRC involved in the maintenance and analysis of the study data (see Appendix 9). 
3.8 
BETTER HEALTH: DURHAM   Prevention Practitioners 
 
Prevention Practitioners will be public health nurses employed by the DRHD, who have had 
intensive training in administration of 'BETTER' using effective educational methods 1,7,15,16,17,25,42,52 . 
These methods include interviewing with shared decision making, brief action plans, in the context of 
small group discussions and role-playing. (22,42)  
The process of interviewing and goal setting was described in the publication of the 'BETTER' 
trial: 
"Through motivational interviewing and shared decision-making, a personalized 'prevention 
prescription' was prepared by the Prevention Practitioner during the visit.  This prescription was tailored 
to that patient's chronic disease risk, which also included their family history.  The prevention 
prescription focused on optimum use of existing capacity, tools and community resources that were 
available. "22 
See Appendix 5 for summary of concepts underlying the meeting, and visual aids. 
We will request informed consent from up to 50 participants in immediate intervention clusters 
for audiotaping of the meeting with the prevention practitioner nurse, for purposes of quality assurance 

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assessment of the prevention practitioner nurse’s conduct of the meeting, adapting the methods of 
Miller62 and of Moyer63. 
Prevention practitioners will participate in the participatory research methods and facilitation of 
CDPS. We have allocated an average of 4 hours of public health nurse / prevention practitioner time per 
each of 352 participants; including an average of 3 hours for direct participant contact and follow-up and 
1 hour for other study activities (meetings etc.)  
 
The prevention practitioner nurses will enter a limited amount of self-reported data from 
participants about goals and self-referrals, using the same secure method described above in 3.7 for the 
self-report data collected by the research assistant. 
 
 
3.9. 
‘BETTER HEALTH: DURHAM:  Social determinants of health 
 
Social determinants of health are very powerful predictors of health status and health behaviour 
across the socio-economic spectrum. 
Demographic and socio-economic factors will be collected at 
the baseline survey interview and 6 month outcome survey interview for exploratory analyses of their 
relationship to the accomplishment of CDPS activities.  Such information is highly sensitive as any 
personal health information and will be handled with the same privacy and confidentiality procedures as 
described in 3.7. 
 
Should participants request information from prevention practitioner nurses about how to self-refer to 
agencies that address social determinants of health such as housing, income support, or family services, 
the frequency of such requests will also be computed, having treated such a request for information on 
how to self-refer with the same privacy and confidentiality procedures as described in 3.7. 
3.10 
BETTER HEALTH: DURHAM   Facilitation of CDPS goals 
Facilitation of the achievement of CPDS goals and targets in the prevention prescription in the 
first  ‘BETTER’ trial was by a combination of prevention practitioner activity as well as by Family Health 
Team staff, and self-direction22.  In this study, participants may lack a primary care physician (PCP), at 
least initially, and may face other barriers such as limited access to online information, and have limited 
access to transportation, and limited financial resources, compared to the average participant in 
previous 'BETTER' studies.  The prevention practitioner nurses will have information available to to 
provide to participants who do not have a primary care provider about primary care physicians and 
nurse practitioners nearby who have stated their willingness to accept additional new patients. 
Facilitation will include identification of appropriate services and activities for individual participants 
according to their personally chosen CDPS actions and finding strategies to overcome particular barriers 
encountered by individual participants. 

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Participants with a primary care physician will have any screening tests and their follow-up done 
by that PCP. 
Blood pressure, weight, and waist circumference measures will be performed by the prevention 
practitioner.   
Among participants without a primary care physician blood draws and distribution of stool 
testing kits will be done by nurse practitioner requisition.  We have allocated on average one hour of 
nurse practitioner time per participant.  For women desiring to have a pap smear and who do not have a 
physician to do this, appropriate arrangements will be made by the nurse practitioner. Nurse 
practitioners in Ontario are now authorized to make referrals to specialist physicians based on abnormal 
results received from investigations ordered by the nurse practitioners.  However, as a fail-safe, several 
members of the investigative team are also primary care physicians would be available on a rotating 
basis to deal with situations in which the nurse practitioner has been unable to make a necessary 
referral for abnormal screening results. 
3.11 
BETTER HEALTH: DURHAM 
 Assistance to find PCP 
 
Prior to the commencement of recruitment, the study investigators, in concert with all those 
stakeholders involved in the participatory research methods, will have identified nurse practitioners and 
primary care physicians who have agreed to accept new patients for those participants who do not have 
a primary care provider and who wish to have one.  The participatory research methods for this project 
will include a primary care strategy to engage primary care providers in the adaptation of the 
intervention and in the identification of primary care providers who would be willing to accept as new 
patients participants who do not have one.  
3.12 
BETTER HEALTH:  DURHAM  Followup of normal and abnormal results of screening tests among 
participants who still do not have a primary care provider. 
 
Normal results will be disclosed to the participant by the research assistant. 
Abnormal results will be disclosed to the participant by the nurse practitioner.  
In the case of critical results from specimen examinations for which the nurse practitioner has 
been unable to obtain an appropriate medical consultation, the results will be reported immediately to 
one of the primary care investigators (according to a pre-determined roster of duty) for urgent action 
and / or for referral to specialist.    
In the case of abnormal but less critical results requiring prompt referral (e.g. high grade 
cytology on PAP), for which the nurse practitioner has been unable to obtain an appropriate medical 
consultation the primary care investigators (Lofters, Pinto) will make the appropriate referral for any 
person for whom the prevention practitioner has been unable to link to a primary care physician. 
  
3.13 
BETTER HEALTH: DURHAM:  Outcome ascertainment 
 

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Six months after the baseline survey interview, the research assistant will administer the 
outcome survey on health and CDPS actions to participants in intervention and control clusters alike.  All 
outcomes are self-reports of the completion of CDPS actions.  The data will be securely entered directly 
to the database application on the secure server at AHRC as described above in 3.7. Results of any 
screening tests ordered by the nurse practitioner at the request of the participant are NOT part of the 
study data and will be retained securely only at the DRHD.   
At the time of the six month outcome survey interview, the research assistant will offer each 
participant in wait-list control clusters the opportunity to receive the BETTER intervention with a 
prevention practitioner. Outcome data will NOT be collected for analysis six months after the delayed 
BETTER intervention in wait-list control clusters.   See Appendix 2 for pathway and data collection. 
3.14 
BETTER HEALTH:  DURHAM  Primary Outcome Measure 
 
The primary outcome measure is a composite index, expressed as the ratio (multiplied by 100) 
of the number of eligible CDPS actions at baseline (denominator) that are subsequently met (by self-
report) at follow-up (numerator), measured at the patient level. The composite index is modeled after 
the Summary Quality Index (SQUID) introduced by Nietert for assessing the quality of primary care 
interventions48. As a function of baseline characteristics, certain individuals are ‘eligible’ for certain CDPS 
actions. At follow-up, each patient will be re-evaluated and the number of eligible actions … ‘met’ will be 
enumerated. For example, eligible actions would be smoking cessation in a smoking patient or a 
mammogram in a patient not up-to-date with mammograms. In this case, the actions would be 
designated ‘met’ if the patient had quit smoking and had a screening mammogram at follow-up. If the 
patient had not quite smoking or the mammogram was not completed at follow-up these actions were 
considered ‘not met’. 22 
3.15 
BETTER HEALTH:  DURHAM 
Secondary outcome measures related to primary objective 
We will report the frequency with which individual CDPS actions were completed.  We will 
report the frequency with which self-referrals were reported to the prevention practitioner.  
4.0 
Secondary objectives and methods: 
4.1 
Secondary Objective 4.1. To adapt, revise and tailor ‘BETTER’ to the needs of those in low 
income clusters, using community-based participatory research principles  
 
We will use community-based participatory research (CBPR) principles to guide the adaptation 
of ‘BETTER’. CBPR is a “collaborative research approach that is designed to ensure and establish 
structures for participation by communities affected by the issue being studied…in all aspects of the 
research process to improve health and well-being through taking action including social change.”59. A 
CBPR approach has improved the quality of interventions in a variety of community settings 26, 59. 
Principles of CBPR are consistent with those of community engagement endorsed in Ontario. In 
particular, the Durham Region Health Department (DRHD) has adopted community engagement 
principles38,49.  

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4.11 
Specific Approach to Adapting BETTER:  The specific process of adaption of BETTER will follow 
the ADAPT-ITT steps 61 (Appendix 4a). Stakeholders including Prevention Practitioners, Nurse 
Practitioners, public health, primary care, and community stakeholders will be invited to a meeting to 
introduce elements of BETTER and overall study goals and approach. Subsequently, research team 
members will conduct small group meetings with members of the public who are potentially eligible for 
BETTER, and community stakeholders, to discuss specific needs, and the fit of BETTER to those needs. 
Extensive notes will be taken at the meetings and all suggestions will be documented. Adaptations of 
‘BETTER’ will be made considering the needs of the community, balanced with BETTER fidelity to core 
elements, discussed in follow up meetings, and pilot-tested.  PPs will receive extensive training in the 
adapted BETTER intervention. The adaptation and implementation of ‘BETTER’ will be evaluated using 
qualitative methods (see below).  
4.2 
Secondary Objective 4.2. To conduct a qualitative evaluation of the implementation of the 
adapted ‘BETTER’, considering perceived effectiveness, facilitators and barriers, and benefits and 
disadvantages, and sustainability in low income clusters and among those who have not had a PCP.  
We have adapted the approach taken for the evaluation of ‘BETTER’ in Newfoundland and NWT42. The 
key questions to guide the qualitative evaluation are: 1. How was ‘BETTER’ adapted? 2. What has been 
the impact of ‘BETTER’ as perceived by stakeholders? 3. What barriers and enablers of ‘BETTER’ have 
been encountered? 4. What are the benefits and disadvantages of ‘BETTER’ ‘? , and 5) How can the 
implementation of ‘BETTER’ be sustained? 
4.21 Specific Methods: We will use a qualitative approach based on grounded theory19  and informed by 
the Consolidated Framework for Implementation Research (CFIR) 14 (Appendix 4) to evaluate the 
adaptation and implementation of ‘BETTER’. Grounded theory is a well-known qualitative method suited 
to examining a phenomenon, such as the adaptation and implementation of ‘BETTER’, within the 
context of offering BETTER through the DRHD in low income clusters19.  CFIR was developed from 
existing implementation frameworks and illustrates interrelationships among five different domains14. 
Given the complexity of the adaptation and implementation processes for ‘BETTER’, all potentially 
relevant domains (the intervention, the inner and outer settings, individuals involved, and the process of 
implementation) will be considered. Constructs for each domain of CFIR have been described 14 which 
will facilitate interpretation during data coding and analytic processes (described below). 
4.22 
Data collection: We will collect qualitative data three times during the project place: 1) at the 
beginning of the study to assist with adaptation and start-up issues (Year 1); 2) after the intervention is 
established to understand perceived enablers and barriers, benefits and disadvantages (Years 2-3), and 
3) near the end of the study (Year 4) to explore sustainability and impact. Data collection strategies will 
include focus groups and one-to-one, semi-structured interviews with a range of participants: 1) 
members of the public eligible for BETTER who live in low income clusters that not be participating as 
well as clusters that will be participating; 2) prevention practitioners who are providing the BETTER 
intervention; 3) public health unit (PHU) Medical Officer of Health (MOH) and their staff; 4) primary care 
physicians; 5) nurse practitioners, and 6) other community stakeholders. Interview guides will be based 
on the study objectives and revised periodically to seek contrasting and supporting data. Interviews will 

14 
 
version Jan 30, 2017 
 
be recorded and transcribed verbatim and field notes will be created to document non-verbal and 
contextual information. 
4.23 
Recruitment of participants to participatory research methods:  
Members of the public who are potentially eligible for BETTER (for adaptation component), will 
be nominated by the DRHD, community groups, or community health professionals and by prevention 
practitioners (during implementation phase). Stakeholders will be approached by the research team or 
by staff members of the DRHD. Potential participants will receive a detailed letter of information 
describing the study and inviting them to participate. Residents of low income clusters will be offered 
$25.00 in recognition of their time and their transportation costs (public transit or parking) will be 
reimbursed. Participants will provide informed consent.  The number of interviews or focus groups 
depends on data saturation. Saturation occurs when data categories are dense and no new or relevant 
data are being 6.  
Among residents of eligible low income clusters we estimate up to 20 key informant interviews 
and up to 5 focus groups of approximately 8 persons each. Among residents of low income clusters 
selected by the random process and therefore not clusters for recruitment of participants in the 
comparison of immediate intervention versus 6 month ‘wait-list delay’, we also estimate up to 20 key 
informant interviews and up to 5 focus groups of approximately 8 persons each. 
Among stakeholders we estimate 25 key informant interviews and 10 focus groups of 8 persons 
each. Stakeholders will not be offered payment for their time because of limited funding available for 
this study.   
4.3 
Secondary Objective 4.3. To share the adapted BETTER knowledge products and study results 
with a wide range of stakeholders including policy makers and advisors, public health, primary care, 
community and national organizations, and with a larger research audience, using a KT framework.  
4.31 
Knowledge Translation Plan: Integrated and end-of-grant knowledge translation (KT) activities 
are central to the adaption and implementation of ‘BETTER’. The CIHR Knowledge to Action process is 
one of our guiding frameworks.  It has been adapted taking into consideration the needs of the project 
(Appendix 4. Adapted Knowledge to Action Process) 
4.32 
Integrated KT: Integrated KT will be ongoing throughout all phases of the study including 
adaptation of BETTER, implementation and evaluation. The CBPR approach in 14 this study will facilitate 
integrated KT. For example, staff members of the DRHD have been involved in the creation of this 
proposal. During adaptation of BETTER, we will consult with key stakeholders including members of the 
public who are eligible for CDPS activities in low income clusters, and leaders of community 
organizations who have insights of different facets of the adaptation and implementation process, and 
who will have a stake in the study results. If it proves difficult to engage members of the public in the 
project, we will follow the advice of community leaders who have in-depth knowledge of the low 
income clusters and the optimal methods to seek their participation. We anticipate that bidirectional 
communication will occur in one to one, small and large group interactive face-to-face meetings. 

15 
 
version Jan 30, 2017 
 
Ongoing project updates will be provided via Facebook, and websites. Early communication from 
stakeholders will shape subsequent messages, and avenues for integrated KT to meet needs. The 
effectiveness of integrated KT will be assessed as part of the qualitative evaluation. Early evaluation will 
uncover strengths and weaknesses of the integrated KT approach so that new strategies can be 
developed if needed. 
4.33  
End-of-Grant KT: We will hold a summative KT workshop at the end of the study involving all 
stakeholders. Multiple small group meetings will be held in Durham Region, to reach all stakeholders. 
Study briefing notes will be created and tailored to different stakeholder needs (determined using data 
from the qualitative evaluation). Abstracts for academic presentations and workshops will be submitted 
to the chronic disease prevention annual conference. Manuscripts will be submitted to Implementation 
Science, BMC Public Health, BMC Family Practice, the Journal of Medical Screening, and CMAJOpen. 
 
5.0 
ANALYSIS PLANS:  
5.1 
Primary Objective Analysis of the primary outcome (absolute percent increase in eligible CDPS 
actions completed or engaged in) will account for the correlation among outcomes which may arise 
from individuals within the same cluster. This will be done by implementing a two-level hierarchical 
regression model15. Specifically, a generalized linear random effects regression model will be 
constructed in which a cluster-specific random effect, arising from a normal distribution, will be included 
to account for the dependency among outcomes of individuals within the same cluster46,47,58. The main 
binary exposure in the hierarchical model will be immediate ‘BETTER’ compared to ‘wait-list’ control; 
furthermore any characteristics that were not balanced (between the immediate ‘BETTER’ and ‘wait-list 
controls’) from the randomization process will be adjusted for in the hierarchical regression model. In 
addition to obtaining estimates of the regression parameters, the hierarchical regression model will 
allow us to investigate the percent of residual or unexplained variation attributable to each level of the 
hierarchy. Hierarchical regression analyses will be conducted using the statistical programming package 
MLWIN (Centre for Multilevel Modeling, Bristol, UK).  See Appendix 3 for quantitative analysis plan. 
5.2 
 Secondary objectives   We will use the constant comparative method for data analysis4,19.  
Initially, two team members will code approximately 2-3 transcripts in each data collection phases using 
an editing style of coding13.  From the codes identified during this process, a preliminary coding guide 
will be developed and reviewed with all team members. Subsequently, a research assistant will code the 
remaining transcripts using the coding guide. We will hold periodic analysis meetings with several team 
members to review the codes, sort codes into categories and identify main themes4,19. Team members 
will create memos that will document emerging relationships among the codes and categories. We will 
use data management and analysis software (NVivo 10, QSR International).  An audit trail including 
interview summaries and memos will be used to document all major decisions24.  
5.21 
Rigor of Qualitative Methods. Involvement of several members of the research team during the 
analytic process will ensure that identified themes are consistent with coded data. We will use data 
triangulation in which data from several sources are examined to provide a full description of the 

16 
 
version Jan 30, 2017 
 
themes. As noted above, the use of the audit trail will ensure transparency of major decisions that are 
made during data collection and analysis.  

17 
 
version Jan 30, 2017 
 
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Appendix 1: 
Comparison of the original BETTER study in primary care22, and this project 
 
BETTER in primary care practices 
BETTER HEALTH: DURHAM 
Physical location of 
prevention practitioner (PP) 
In primary care team clinics 
Imbedded in Health Department, 
Durham Region, for community 
outreach 
Identification of participants 
From electronic medical record 
Community-based recruitment 
strategies in low income clusters 
Informed consent 
For collection of personal health 
information, by prevention 
practitioner 
For collection of self-reported 
personal health information, by 
research assistant 
Identification of completed 
and current behaviours and 
activities 
From electronic medical record, 
and from self-report in self-
administered survey 
From self-report responses to 
survey administered by research 
assistant 
Data collection 
Paper 
Electronic 
Identification of risk factors 
Lab tests , survey, electronic 
medical record 
Self report 
Brief action plan interview 
by PP and goal-setting by 
participants 
By prevention practitioner in 
primary care team clinics 
By prevention practitioners at 
various community locations  
Height, weight, waist 
circumference, blood 
pressure 
EMR PP / PCP 
Prevention practitioner 
Specimen collection for 
laboratory-based screening 
In primary care clinics 
 Various locations, by nurse 
practitioners for those without MD 
Facilitation of goal 
achievement 
Clinic staff, prevention 
practitioner, links, and self 
Prevention practitioners, links, and 
self 
Strategy to find primary care 
physician for participants. 
Not applicable 
Prevention practitioners supported 
by primary care strategy engaging 
physicians and clinics near the 
clusters. 
Followup of abnormal results By primary care physician 
By prevention practitioner, nurse 
practitioner, primary care 
physicians, with back up by primary 
care physician-investigators in the 
event of results requiring urgent 
action for those without primary 
care physicians. 
Primary outcome measures 
“composite index, expressed as the ratio (multiplied by 100) of the 
number of eligible CDPS (chronic disease prevention and screening) 
actions at baseline (denominator) that were subsequently met at follow-
up (numerator), measured at the patient level.”(G*) 
Ascertainment of outcomes 
Abstraction from EMR and self-
report responses at repeat self-
administered survey by prevention 
practitioner 
Biometrics collected by prevention 
practitioner and self-report 
responses to survey administered 
by research assistant  
 

24 
 
version Jan 30, 2017 
 
Appendix 2: 
BETTER HEALTH: DURHAM 
Pathway of participants and their quantitative data 
 
 
 
 
Community based promotion and recruitment 
 
 
 
Potential participant gives name, sex, age, address and phone number to research assistant  
 
 
 
 
Research assistant (1) determines if potential participant is eligible by age and address, (2) notifies 
potential participant and (3) offers to meet to explain study and explain informed consent form. 
 
 
 
Potential participant meets research assistant to consider whether to participate and whether to 
consent or not 
 
 
 
If decides to consent, Study ID assigned to participant by research assistant and research assistant 
determines cluster identifier.    
Study ID linked to name, address and phone number in confidential secure file at the Durham Region 
Health Department (DRHD)   
Informed consent form kept in confidential secure file at the DRHD.   
If decides to withhold consent, name, address, and phone number deleted; sex and age retained for 
statistics on refusals to consent. 

25 
 
version Jan 30, 2017 
 
 
 
 
 
Consenting participant completes baseline survey via interview with research assistant.   
 
 
 
Responses entered electronically by research assistant securely and directly online into database on 
secure server at Applied Health Research Centre, St Michael’s Hospital (AHRC) ; responses identified 
only by study ID, age, sex, cluster identifier (letter code), and cluster category (immediate intervention 
versus wait-list control) .   
 
 
 
 
Eligible Chronic Disease Prevention and Screening activities and prevention prescription are electronically 
computed for future use by prevention practitioner nurse and for final analysis. 
 
 
 
 
In preparation for, and during, meeting with prevention practitioner nurse, the prevention practitioner 
nurse securely accesses the data directly on the secure server at AHRC. 
 
Participants living in immediate intervention clusters: 
If participant lives in an immediate intervention cluster, prevention practitioner nurse contacts 
participant to set up appointment for brief action planning / shared decision making.    

26 
 
version Jan 30, 2017 
 
Prevention practitioner nurse refers to information remotely and securely via computer, and follows 
prompts from computer as well as interests and wishes of participant.  
Participant and prevention practitioner nurse work together to create prevention prescription and goals 
for chronic disease prevention and screening activities.   
Prevention practitioner nurse enters biometrics (for calculation of BMI and achievement of certain CDPS 
actions), individual goals, and referrals for prevention prescription directly and securely into the 
database application on the server at AHRC. 
Participant receives paper document of prevention prescription and goals.  
Prevention practitioner nurse facilitates goal setting and completion of Chronic Disease Prevention and 
Screening Activities.   
 
 
 
 
  
Electronic prompt to research assistant to contact participant in order to schedule 6 month outcome 
survey. 
Research assistant contacts participant 6 month's later to conduct outcome survey and enters responses 
securely and remotely into database application on secure server at AHRC.   
OR 
Participants living in wait-list control clusters: 
 
 
 
Electronic prompt to research assistant to contact participant in order to schedule 6 month outcome 
survey. 
If participant lives in wait - list control cluster, research assistant contacts participant 6 months later to 
conduct outcome survey and enters responses electronically into tablet computer.   
 

27 
 
version Jan 30, 2017 
 
 
 
 
Prevention practitioner nurse contacts participant to set up appointment for brief action planning / 
shared decision-making.    
Prevention practitioner nurse refers to information securely and remotely by computer and follows 
prompts from computer as well as interests and wishes of participant.   
Participant and prevention practitioner nurse work together to create prevention prescription and goals 
for chronic disease prevention and screening activities.     
Participant receives paper document of prevention prescription and goals.   
Prevention practitioner nurse enters biometrics (for calculation of BMI and achievement of certain CDPS 
actions), individual goals, and referrals for prevention prescription. 
Prevention practitioner nurse facilitates goal setting and completion of Chronic Disease Prevention and 
Screening Activities.    
 
 
 
 
 
 
 
 
 
 
 

28 
 
version Jan 30, 2017 
 
Appendix 3: 
BETTER HEALTH:  DURHAM Quantitative analysis plan 
 
 

29 
 
version Jan 30, 2017 
 
Consort table  
Number of self-referrals for potential participation N = 
Number assessed for eligibility (age 40 - 64, residence in eligible cluster, no other member of 
household already participating) 
N = 
Number to whom the study is offered 
N = 
Number offered who consent 
N = 
Those who consent and who reside in immediate intervention clusters 
Number who consent who complete baseline survey 
N = 
Number who complete baseline survey in immediate intervention clusters who attend 
prevention practitioner interview 
N = 
Number in immediate intervention clusters who complete six month outcome survey after 
prevention practitioner interview 
N = 
Those who consent and who reside in wait-list control clusters 
Number who consent who complete baseline survey 
N = 
Number in wait-list control clusters who complete six month outcome survey  
N = 
 
Number in wait-list control clusters who attend prevention practitioner interview after six 
month outcome survey 
N = 
 
 

30 
 
version Jan 30, 2017 
 
Table 1 a 
Baseline characteristics of participants by cluster  
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
Age 
mean, SD 
median, IQR 
 
 
 
Sex 
male 
female 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Minority race or ethnic group 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Education 
>= 1 year post secondary education 
yes 
no 
 
 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
Marital status 
Married / common law 
Other 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Income 
>= 60,000 CAD 
< 60,000 CAD 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Current smoker 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Current alcohol consumption 
< 4 times per month 
>= 2 times per week 
other 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
Exercise status 
<= mildly active 
Other 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
BMI 
mean, SD 
median, IQR 
 
 
 
Obesity 
yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
MOS social support score* 
mean, SD 
median, IQR 
 
 
 
Followup time (days) 
 
 
 

31 
 
version Jan 30, 2017 
 
mean, SD 
median, IQR 
 
*MOS social support scale is included in Baseline Survey Interview 
 
 

32 
 
version Jan 30, 2017 
 
Table 1b 
Additional description of study population 
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
Age 
 
40 - 44 
45 - 49 
50 - 54 
55 - 59 
60 - 64 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
Caucasian / white  
Southeast Asian 
East Asian 
South Asian 
Black 
Aboriginal 
-first nations 
-metis 
-inuit 
Other: 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
Education 
 
Elementary school or less 
Some high school 
Completed high school 
Some college or technical school 
Completed college or technical school 
Some university 
Completed bachelor's degree 
Graduate or professional degree 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 

33 
 
version Jan 30, 2017 
 
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
Employment status 
 
>= 30 hours per week 
>0 < 30 hours per week 
unable to work because of sickness or 
disability 
looking after home and / or family 
Student 
retired 
unemployed 
 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
Marital status 
Married / common law 
Other 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Number of persons in household 
(including self) 
mean, SD 
median, IQR 
 
 
 
Household income before taxes from all 
sources 
< 10,000 
10,000 - 19,999 
20,000 - 39,999 
40,000 - 59,999 
>= 60,000 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
History of diabetes 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
History of hypertension 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
History of stroke 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
History of coronary heart disease 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
 
 

34 
 
version Jan 30, 2017 
 
Table 1c 
Baseline lifestyle and nutrition items 
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
EXERCISE 
 
 
 
Exercise >= once weekly 
Yes 
No 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
Minutes spent excercising weekly 
mean, SD 
median , IQR 
 
 
 
Physical activity involved in work 
-not employed 
-mostly sitting 
-mostly standing or walking 
-definite physical effort 
-vigorous physical activity 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
Usual walking pace 
-slow pace 
-steady average pace 
-brisk pace 
-fast pace 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
NUTRITION 
 
 
 
Fast food and snacks per week 
0 
1 - 3 times 
>= 4 times 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
Servings of fruit per day 
<= 2 
3 - 4 
>= 5 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
Servings of vegetables per day 
<= 2 
3 - 4 
>= 5 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
Sweetened beverages per day 
0 
1 - 2 
>= 3 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
Snack chips or crackers per week 
<= 1 
2 - 3 
>= 4 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
 

35 
 
version Jan 30, 2017 
 
Desserts and other sweets per week 
<= 1 
2 - 3 
>= 4 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
ALCOHOL 
 
 
 
Drinks containing alcohol 
-never 
-monthly or less 
-2 - 4 times per month 
-2 - 3 times per week 
->= 4 times per week 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
How often do you have >= 6 drinks 
on one occasion? 
-never 
-less than monthly 
-monthly 
-weekly 
-daily or almost daily 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
 
 
n, cell % 
n, cell % 
n, cell % 
n, cell % 
n, cell % 

36 
 
version Jan 30, 2017 
 
Table 2 
 
Baseline eligibility of participants for chronic disease prevention and 
screening (CDPS) actions  by randomization cluster (count and %) 
Original SQUID 
ID number 
Action descriptor Immediate 
intervention 
clusters 
Wait-list control 
clusters 
Overall 
1 
fasting blood 
sugar or HgB A1C 
screen 
n, cell % 
n, cell % 
n, cell % 
3 
Blood pressure 
screen 
n, cell % 
n, cell % 
n, cell % 
4 
Blood pressure 
monitor 
n, cell % 
n, cell % 
n, cell % 
6 
Measurement of 
low density 
lipoproteins 
n, cell % 
n, cell % 
n, cell % 
10 
Screening 
mammography 
n, cell % 
n, cell % 
n, cell % 
11 
Colorectal 
screening 
n, cell % 
n, cell % 
n, cell % 
12 
Cervical screening 
/ pap smear 
n, cell % 
n, cell % 
n, cell % 
13 
BMI screening 
n, cell % 
n, cell % 
n, cell % 
14 
Waist 
circumference 
measurement 
n, cell % 
n, cell % 
n, cell % 
15 
Weight control 
n, cell % 
n, cell % 
n, cell % 
16 
Referral for BMI > 
25 
n, cell % 
n, cell % 
n, cell % 
18 
Smoking cessation 
n, cell % 
n, cell % 
n, cell % 
19 
Smoking cessation 
referral 
n, cell % 
n, cell % 
n, cell % 
21 
Alcohol control 
n, cell % 
n, cell % 
n, cell % 
22 
Alcohol control 
referral 
n, cell % 
n, cell % 
n, cell % 
24 
Physical activity >= 
90 minutes 
n, cell % 
n, cell % 
n, cell % 
25 
Physical activity 
referral 
n, cell % 
n, cell % 
n, cell % 
27 
Healthy diet score 
n, cell % 
n, cell % 
n, cell % 
28 
Nutrition referral 
n, cell % 
n, cell % 
n, cell % 
 
 
 

37 
 
version Jan 30, 2017 
 
Table 3 
 
Percent of eligibles, by cluster, who complete eligible CDPS actions  
Original SQUID 
ID number 
Action descriptor Immediate 
intervention 
clusters 
Wait-list control 
clusters 
Overall 
1 
fasting blood 
sugar or HgB A1C 
screen 
n, cell % 
n, cell % 
n, cell % 
3 
Blood pressure 
screen 
n, cell % 
n, cell % 
n, cell % 
4 
Blood pressure 
monitor 
n, cell % 
n, cell % 
n, cell % 
6 
Measurement of 
low density 
lipoproteins 
n, cell % 
n, cell % 
n, cell % 
10 
Screening 
mammography 
n, cell % 
n, cell % 
n, cell % 
11 
Colorectal 
screening 
n, cell % 
n, cell % 
n, cell % 
12 
Cervical screening 
/ pap smear 
n, cell % 
n, cell % 
n, cell % 
13 
BMI screening 
n, cell % 
n, cell % 
n, cell % 
14 
Waist 
circumference 
measurement 
n, cell % 
n, cell % 
n, cell % 
15 
Weight control 
n, cell % 
n, cell % 
n, cell % 
16 
Referral for BMI > 
25 
n, cell % 
n, cell % 
n, cell % 
18 
Smoking cessation 
n, cell % 
n, cell % 
n, cell % 
19 
Smoking cessation 
referral 
n, cell % 
n, cell % 
n, cell % 
21 
Alcohol control 
n, cell % 
n, cell % 
n, cell % 
22 
Alcohol control 
referral 
n, cell % 
n, cell % 
n, cell % 
24 
Physical activity >= 
90 minutes 
n, cell % 
n, cell % 
n, cell % 
25 
Physical activity 
referral 
n, cell % 
n, cell % 
n, cell % 
27 
Healthy diet score 
n, cell % 
n, cell % 
n, cell % 
28 
Nutrition referral 
n, cell % 
n, cell % 
n, cell % 
 
 
 

38 
 
version Jan 30, 2017 
 
Table 4: 
Percent of eligibles who complete CHOSEN SQUID actions (set as goals by 
participant) (cell denominators will be smaller than for Table 4:  not all eligible, but all eligible 
who set action as a goal) 
Original SQUID 
ID number 
Action descriptor Immediate 
intervention 
clusters 
Wait-list control 
clusters 
Overall 
1 
fasting blood 
sugar or HgB A1C 
screen 
n, cell % 
n, cell % 
n, cell % 
3 
Blood pressure 
screen 
n, cell % 
n, cell % 
n, cell % 
4 
Blood pressure 
monitor 
n, cell % 
n, cell % 
n, cell % 
6 
Measurement of 
low density 
lipoproteins 
n, cell % 
n, cell % 
n, cell % 
10 
Screening 
mammography 
n, cell % 
n, cell % 
n, cell % 
11 
Colorectal 
screening 
n, cell % 
n, cell % 
n, cell % 
12 
Cervical screening 
/ pap smear 
n, cell % 
n, cell % 
n, cell % 
13 
BMI screening 
n, cell % 
n, cell % 
n, cell % 
14 
Waist 
circumference 
measurement 
n, cell % 
n, cell % 
n, cell % 
15 
Weight control 
n, cell % 
n, cell % 
n, cell % 
16 
Referral for BMI > 
25 
n, cell % 
n, cell % 
n, cell % 
18 
Smoking cessation 
n, cell % 
n, cell % 
n, cell % 
19 
Smoking cessation 
referral 
n, cell % 
n, cell % 
n, cell % 
21 
Alcohol control 
n, cell % 
n, cell % 
n, cell % 
22 
Alcohol control 
referral 
n, cell % 
n, cell % 
n, cell % 
24 
Physical activity >= 
90 minutes 
n, cell % 
n, cell % 
n, cell % 
25 
Physical activity 
referral 
n, cell % 
n, cell % 
n, cell % 
27 
Healthy diet score 
n, cell % 
n, cell % 
n, cell % 
28 
Nutrition referral 
n, cell % 
n, cell % 
n, cell % 
 
 
 

39 
 
version Jan 30, 2017 
 
Table 5: 
CDPS and SQUID by randomization group overall and by age stratum and by 
sex stratum 
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
OVERALL 
 
 
 
Number of eligible 
CDPS actions 
 
mean, SD 
median, IQR 
 
 
 
Number of eligible 
CDPS actions 
completed 
 
mean, SD 
median, IQR 
 
 
 
Unadjusted SQUID 
 
 
 
Adjusted SQUID 
 
 
 
 
 
 
 
Age stratum 40 - 49 
 
 
 
Number of eligible 
CDPS actions 
 
mean, SD 
median, IQR 
 
 
 
Number of eligible 
CDPS actions 
completed 
 
mean, SD 
median, IQR 
 
 
 
Unadjusted SQUID 
 
 
 
Adjusted SQUID 
 
 
 
 
 

40 
 
version Jan 30, 2017 
 
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
Age stratum, 50 - 64 
 
 
 
Number of eligible 
CDPS actions 
mean, SD 
median, IQR 
 
 
 
Number of eligible 
CDPS actions 
completed 
mean, SD 
median, IQR 
 
 
 
Unadjusted SQUID 
 
 
 
Adjusted SQUID 
 
 
 
 
 
 
 
Females 
 
 
 
Number of eligible 
CDPS actions 
mean, SD 
median, IQR 
 
 
 
Number of eligible 
CDPS actions 
completed 
mean, SD 
median, IQR 
 
 
 
Unadjusted SQUID 
 
 
 
Adjusted SQUID 
 
 
 
 
 
 
 
Males 
 
 
 
Number of eligible 
CDPS actions 
mean, SD 
median, IQR 
 
 
 
Number of eligible 
CDPS actions 
completed 
mean, SD 
median, IQR 
 
 
 
Unadjusted SQUID 
 
 
 
Adjusted SQUID 
 
 
 
 
 
 

41 
 
version Jan 30, 2017 
 
Computation of unadjusted and adjusted SQUID  
The primary outcome measure is a composite index, expressed as the ratio (multiplied by 100) of the 
number of eligible CDPS actions at baseline (denominator) that are subsequently met at follow-up 
(numerator), measured at the patient level. The composite index is modeled after the Summary Quality 
Index (SQUID) introduced by Nietert for assessing the quality of primary care interventions48. As a 
function of baseline characteristics, certain individuals are ‘eligible’ for certain CDPS actions. At follow-
up, each patient will be re-evaluated and the number of eligible actions … ‘met’ will be enumerated. For 
example, eligible actions would be smoking cessation in a smoking patient or a mammogram in a patient 
not up-to-date with mammograms. In this case, the actions would be designated ‘met’ if the patient had 
quit smoking and had a screening mammogram at follow-up. If the patient had not quite smoking or the 
mammogram was not completed at follow-up these actions were considered ‘not met’. 22 
22. 
Grunfeld E, Manca D, Moineddin R, Thorpe KE, Hoch JS, Campbell-Scherer D, Meaney C, Rogers 
J, Beca J, Krueger P, Mamdani M for the BETTER Trial Investigators 
Improving chronic disease prevention and screening in primary care:  results of the BETTER pragmatic 
cluster randomized controlled trial.   
BMC Family Practice 2013, 14: 175. 
 
48. 
Nietert PJ, Wessell AM, Jenkins RG, et al:  
Using a summary measure for multiple quality indicators in primary care: the summary QUality InDex 
(SQUID).  
Implementation Sci 2007, 2:11. 
 
Analysis of the primary outcome (absolute percent increase in eligible CDPS actions completed or 
engaged in) will account for the correlation among outcomes which may arise from individuals within 
the same cluster. This will be done by implementing a two-level hierarchical regression model56. 
Specifically, a generalized linear random effects regression model will be constructed in which a cluster-
specific random effect, arising from a normal distribution, will be included to account for the 
dependency among outcomes of individuals within the same cluster46,47,58. The main binary exposure in 
the hierarchical model will be immediate ‘BETTER’ compared to ‘wait-list’ control; furthermore any 
characteristics that were not balanced (between the immediate ‘BETTER’ and ‘wait-list controls’) from 
the randomization process will be adjusted for in the hierarchical regression model. In addition to 
obtaining estimates of the regression parameters, the hierarchical regression model will allow us to 
investigate the percent of residual or unexplained variation attributable to each level of the hierarchy. 
Hierarchical regression analyses will be conducted using the statistical programming package MLWIN 
(Centre for Multilevel Modeling, Bristol, UK). 
46. 
Murray DM, Rooney BL, Hannan PJ, et al.  
Intraclass correlation among common measures of adolescent smoking.  
Am J Epidemiol. 1992;140:1038-1050. 
 
47. 
Murray, D. M. (1998).  
Design and analysis of group-randomized trials.  

42 
 
version Jan 30, 2017 
 
Oxford: Oxford University Press. 
 
48. 
Nietert PJ, Wessell AM, Jenkins RG, et al:  
Using a summary measure for multiple quality indicators in primary care: the summary QUality InDex 
(SQUID).  
Implementation Sci 2007, 2:11. 
 
56. 
Snijders TAB, Bosker RJ.  
Multilevel analysis: an introduction to basic and advanced multilevel modeling.  
London: Sage Publications; 1999. 
 
 

43 
 
version Jan 30, 2017 
 
Table 6: 
Change in smoking, exercise, nutrition, BMI from baseline to 6 month survey 
 
Immediate 
intervention clusters 
Wait-list control 
clusters 
Overall 
SMOKING 
 
 
 
Change in % 'Yes' 
 
 
 
EXERCISE 
 
 
 
Exercise >= once weekly 
Change in % 'Yes' 
 
 
 
Change in minutes spent excercising 
weekly 
mean, SD 
median , IQR 
 
 
 
NUTRITION 
 
 
 
Change in Fast food and snacks per 
week 
mean, SD 
median , IQR 
 
 
 
Change in Servings of fruit per day 
mean, SD 
median , IQR 
 
 
 
Change in Servings of vegetables per 
day 
mean, SD 
median , IQR 
 
 
 
Change in Sweetened beverages per 
day 
mean, SD 
median , IQR 
 
 
 
Change in Snack chips or crackers per 
week 
mean, SD 
median , IQR 
 
 
 
Change in Desserts and other sweets 
per week 
mean, SD 
median , IQR 
 
 
 
BMI 
 
 
 
Change in BMI 
mean, SD 
median, IQR 
 
 
 
 
 
 

44 
 
version Jan 30, 2017 
 
Appendix 4: 
Table of eligible Chronic Disease Prevention and Screening actions / activities 
Original identification number in BETTER SQUID 
table* 
CDPS action / activity 
1 
fasting blood sugar or HgB A1C screen 
3 
Blood pressure screen 
4 
Blood pressure monitor 
6 
Measurement of low density lipoproteins 
10 
Screening mammography 
11 
Colorectal screening 
12 
Cervical screening / pap smear 
13 
BMI screening 
14 
Waist circumference measurement 
15 
Weight control 
16 
Referral for BMI > 25 
18 
Smoking cessation 
19 
Smoking cessation referral 
21 
Alcohol control 
22 
Alcohol control referral 
24 
Physical activity >= 90 minutes 
25 
Physical activity referral 
27 
Healthy diet score 
28 
Nutrition referral 
* some actions / activities which were eligible in the original BETTER trial are deleted because they 
consisted of entry of information into electronic medical records, or depended on access to such 
records.  We are preserving the original numeric identification of the remaining actions / activities. 
 
 

45 
 
version Jan 30, 2017 
 
Appendix 5: 
Brief Action Planning as the approach to prevention practitioner nurse BETTER 
intervention delivered in meeting with participant, incorporating information self-reported by 
participant, using principles of health coaching, shared-decision making, and motivational 
interviewing 
 
 

46 
 
version Jan 30, 2017 
 
 
 
 

47 
 
version Jan 30, 2017 
 
 
 
 

48 
 
version Jan 30, 2017 
 
 
 
 

49 
 
version Jan 30, 2017 
 
 

50 
 
version Jan 30, 2017 
 
Appendix 6: 
Steps to Adapt the BETTER Intervention (adapted from the ADAPT-ITT Model: Phases 
and Methods (Wingood and DiClemente, 2008)) 
Phase 
Method 
1. Assessment 
(Who is the new target 
population?) 
1. Conduct small group meetings with: 
a) Members of the public potentially eligible for BETTER 
b) Prevention Practitioners (PPs), Nurse Practitioners (NPs) 
c) Community stakeholders 
2. Summarize results of meetings 
2. Decision to Adapt 
(Which (if any) components of 
BETTER will be adapted?) 
1. Convene methods working group to review evidence from updated 
literatures searches and review results of interviews 
2. Decide BETTER components to be adapted (if any) 
3. Administer pilot test of 
existing BETTER tools  
(How do members of the public 
who are eligible for BETTER 
perceive the tools and 
intervention?) 
1. Assessed during small group meeting in Phase 1 (above) 
4. Production 
(How will the adapted BETTER 
intervention be produced and 
who will document adaptions?) 
1. Develop adaptation plan 
2. Produce adapted BETTER (first draft) 
3. Document adaptation 
5. Topic Experts 
(Who can help adapt BETTER? 
1. Identify additional experts if needed 
2. Involve experts in the adaptation plan 
6.  Integration 
(What will be included in the 
adapted BETTER? 
1. Second iteration of  adapted BETTER (if needed) 
7. Training 
(Who needs to be trained in 
BETTER?) 
1. Train Prevention Practitioners and Nurse Practitioners in adapted 
BETTER 
8. Testing  
(Was the adaptation successful?) 
1. test adapted BETTER 
2. Assess success of adaptation via focus groups or interviews and 
analyze results 
 
 
 

51 
 
version Jan 30, 2017 
 
 
Appendix 7: 
Team of investigators 
Lawrence Paszat, PI, Senior Scientist, Institute for Clinical Evaluative Sciences (ICES), Associate Professor, 
Dalla Lana School of Public Health, University of Toronto.  
Aisha Lofters, Co-PI, CCSRI Junior Investigator, Co-PI for primary care and BETTER intervention,  Assistant  
Professor of Family and Community Medicine, University of Toronto 
Andrew Pinto, Co-PI for primary care and for public health (jointly qualified) Assistant Professor of 
Family and Community Medicine, and Dalla Lana School of Public Health, University of Toronto.  
Mary Ann O’Brien, co-PI for participatory research methods, qualitative data collection and analysis, and 
KT.  Assistant Professor of Family and Community Medicine, University of Toronto 
Rinku Sutradhar, co-PI for biostatistics, Senior Scientist at ICES, and assistant professor of biostatistics, 
Dalla Lana School of Public Health. University of Toronto 
Peter Selby, co-PI for smoking cessation and participatory research methodology, Professor, University 
of Toronto 
Eva Grunfeld, co-investigator for methodology of BETTER, mentor to Aisha Lofters and Mary Ann 
O’Brien, Professor, University of Toronto 
Donna Manca, co-investigator for primary care and for methodology of BETTER, participatory research 
methodology, qualitative analysis. Associate Professor, Department of Family Medicine, University of 
Alberta.  
Frank Sullivan, mentor to Andrew Pinto, Professor of Community and Family Medicine, University of 
Toronto 
Rick Glazier, mentor to Andrew Pinto, Senior Scientist (ICES). Professor of Family and Community 
Medicine, University of Toronto  
Peter Donnelly, co-investigator Chief Executive Officer, Public Health Ontario 
Linda Rabeneck, co-investigator, Senior Scientist ICES, Vice-president, Prevention and Cancer Control, 
Cancer Care Ontario.   
Nancy Baxter, co-investigator, Senior Scientist ICES, Professor of Surgery, and Professor, Dalla Lana 
School of Public Health, University of Toronto.  
Jill Tinmouth, co-investigator Scientist ICES, Assistant Professor, U Toronto, Lead Scientist, Colorectal 
Screening Program, Cancer Care Ontario.   
Nicolette Sopcak, co-investigator, University of Alberta. 
Robert Kyle, co-investigator Commissioner and Medical Officer of Health, Durham Region Health 
Department, Vice Chair of the Board, Public Health Ontario. 
Mary-Anne Petrusiak, co-investigator Epidemiologist, Durham Region Health Department.  
Jean Nesbitt, co-investigator Director, Public Health Nursing and Nutrition, Chief Nursing Officer, 
Durham Region Health Department. 
Betty Wall, co-investigator, Program Manager, Durham Region Health Department.  
Regina Elliott, co-investigator, Program Manager, Durham Region Health Department. 
 
 

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Appendix 8: 
Required contracts 
Required contracts: 
1. 
Sunnybrook Research Institute and Durham Region Health Department 
-primary contact: 
 Ms Becky Wall, becky.wall@durham.ca 
-data custodian: 
Medical Officer of Health Dr Robert Kyle, robert.kyle@sympatico.ca 
 
2. 
Sunnybrook Research Institute and Applied Health Research Centre, St Michael's Hospital  
-primary contact for all issues:  Ms Judith Hall,  hallju@smh.ca 
 
3. 
Sunnybrook Research Institute and Department of Family Medicine, St Michael’s Hospital 
-primary contact for all issues:  Dr Aisha Lofters MD PhD, aisha.lofters@utoronto.ca 
 
4. 
Sunnybrook Research Institute and Department of Family and Community Medicine, 
University of Toronto  
-primary contact for all issues:  Julia Baxter, family.healthcare@utoronto.ca 
 
 
 

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APPENDIX 9: 
Data security, privacy, confidentiality and management at the Applied Health 
Research Centre, St Michael's Hospital                                                             
 
Database Security Summary  
The Applied Health Research Centre (AHRC), an academic research organization based at St Michael's 
Hospital, will create web-based electronic database application. Data will be entered into encrypted 
tablet computers by research staff of the Health Department, Durham Region, and securely transfered 
by WIFI to AHRC.   All study data will be securely stored on local servers at St Michaels Hospital 
throughout the duration of the study and for up to 10 years after the study is complete. All study 
subjects will be identified in the database by a unique study ID number. Linkages between the patient 
name/contact information and the study ID will be retained at Health Department, Durham Region, and 
not shared. Data will only be accessible by authorized study site personnel and authorized central AHRC 
personnel, and the following research staff at the Health Department, Durham Region:  research 
assistant, prevention practitioners, nurse practitioner. Authorized personnel receive a username and 
password which is unique, and database access is controlled by AHRC in collaboration with the Principal 
Investigator. 
 
Physical Access and Security  
The application will be hosted locally in St. Michael’s Hospital’s secure data centers and has dedicated 
IT, database, application, and build support personnel. The SMH data centre infrastructure has several 
features in place to enhance the security of data, prevent data loss and mitigate downtime. These 
include: 
• 
Duplicate Internet Service Providers (ISPs) 
• 
Infrastructure distributed between two data centres 
• 
Virtualization for added redundancy 
• 
Data centres are accessible by designated IT staff only  
• 
Access is logged through RFID card access and through a physical sign in page  
• 
Redundant UPS’ for backup power  
• 
Redundant cooling systems  
• 
Inert gas based fire suppression system  
 
Data will be stored on the local Storage Area Network (SAN) and will be backed up regularly and stored 
off-site. The data centre is designed such that there are daily backups made of all critical data. In 
addition, the backups are stored both locally, as well as at a remote off-site location, in the case of 
catastrophic failure at one location. With limited access privileges, 24 hour security, and around-the-
clock monitoring, the data centre is highly secure.  
 
Logical Access and Security  
The servers are accessible by designated IT staff only for administration and maintenance purposes. The 
database application will be accessible by registered (through AHRC) users only, who are restricted to 
accessing the projects that they are assigned to. The database application will use standard 
authentication mechanisms to ensure only registered users can access the system, will ensure only 
explicitly specified users can access any particular project and data, and furthermore, will provide 
customizable user access for each project with controls that can be used to restrict users to write or 
read-only privileges on a form-by-form basis. Transmitted data will be secured through the use of TLS 
certificates that encrypt all data sent to and from the server.