Assessing Outcomes of Enhanced Chronic Disease Care Through Patient Education and a Value-based Formulary Study
Phase: NA
Conditions: Coronary Disease, Stroke, Chronic Kidney Disease, Heart Failure, Diabetes Mellitus, Hypertension, Hypercholesterolemia
Sponsor: University of Calgary

 
ASSESSING OUTCOMES OF 
ENHANCED CHRONIC DISEASE 
CARE THROUGH PATIENT 
EDUCATION AND A VALUE-
BASED FORMULARY STUDY 
(ACCESS) 
Study Protocol and Statistical Analysis Plan 
  
 
 
NCT02579655 
Date: Sept 15, 2022 

NCT02579655 
 
Date: Sept 15, 2022 
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Author: 
Dr. David Campbell 
Role: 
Lead 
investigator 
 
Affiliation: 
Assistant 
Professor, 
University of 
Calgary 
Signature: 
 
Date: 
 
Sept 15, 
2022 
Principal 
Investigator: 
Dr. Braden Manns 
Role: 
Principal 
investigator 
 
Affiliation: 
Professor, 
University of 
Calgary 
Signature: 
 
 
Date: 
Sept 15, 
2022 
Principal 
Investigator: 
Terry Saunders-Smith 
Role: 
Project 
coordinator 
 
Affiliation: 
Research 
Coordinator, 
University of 
Calgary 
Signature: 
 
 
Date: 
Sept 15, 
2022 
This statistical plan has been approved by: 
Approver: 
Jianguo Zhang 
Role: 
Study 
Statistician 
 
Affiliation: 
Statistical 
Associate, 
University of 
Calgary 
Signature: 
 
 
Date: 
Sept 16, 
2022 
 
SAP Version 
Date Approved 
Section # 
Changed 
Description and 
Reason for 
Change 
Approver 
1.0 
 
 
 
 
2.0 
Sept 15, 2022 
Section 6.1 (table 
only) 
Minor changes to 
Admin codes 
used to define 
primary outcome 
(see justification 
provided). These 
changes were 
made prior to any 
analyses by 
group, and prior 
to unblinding of 
the data. 
Braden Manns 
on behalf of 
the individuals 
noted above 
 
 
 
 
 
 
 
 
 
 

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Abbreviations and Definitions: 
Abbreviation/Acronym 
Meaning 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1. Introduction 
 
Chronic diseases such as heart attack, stroke, and diabetes lead to significant morbidity, and 
cost the Canadian healthcare system over $93B per year (1). Although medications and 
lifestyle changes can improve outcomes, many do not receive them and therefore do not 
benefit. We have worked closely with Alberta health care decision makers since 2009 to 
design and implement a research program to optimize care for people with chronic disease. 
We first identified gaps in care for people with these chronic diseases in Alberta, including 
noting that 50% do not receive recommended cholesterol-lowering drugs (2). Secondly, with 
Statistics Canada, we surveyed nearly 2000 Western Canadians with chronic disease regarding 
barriers to optimal care. The most important barriers identified included financial barriers and 
lack of patient knowledge. We identified that 20% and 30% of participants had financial and 
knowledge barriers, respectively (3,4) and that the odds of identifying a financial barrier were 
3-fold higher in those whose out-of-pocket expenditures were >2% of household income 
(implying household income <$50,000). Patients with these barriers were 70% more likely to 
be hospitalized for their chronic disease and 50% less likely to use statins, suggesting these 
barriers are highly relevant. 
 
We confirmed the need for the current study after presenting this evidence to Alberta Health 
and after completing systematic reviews of interventions to address these barriers.  The 
systematic reviews assessed: 1) the effectiveness of reducing copayments for high-value drugs 
in people with chronic diseases and determined that reducing copayments may result in 
improved adherence to medical therapy (5); and 2) the impact of 11 quality improvement 
strategies for diabetes and determined that the most effective strategies were patient education 
and facilitated relay of information by patients to clinicians (6). Neither review could 
determine the impact of the interventions on clinical outcomes or costs – because available 
evidence consisted mostly of observational studies, or because trials were short-term and used 
surrogate endpoints.  
 
 

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2. Background and Rationale 
 
What is the problem to be addressed? 
Cardiovascular disease is the leading cause of death in Canada: Cardiovascular disease 
claimed over 69,500 lives in Canada in 2008 (7), most as a result of cardiovascular events, 
such as strokes and myocardial infarctions. Cardiovascular disease is also the leading cause of 
hospitalization for Canadians (8). Important risk factors for cardiovascular events include prior 
stroke or myocardial infarction, chronic kidney disease, heart failure, diabetes or hypertension 
(henceforth referred to simply as “chronic diseases”). These conditions lead to severe 
morbidity and cost the Canadian healthcare system over $93 billion per year (1). 
 
Many patients with chronic diseases do not receive guideline-recommended therapy: Lifestyle 
modification (i.e. weight loss, exercise, healthy diet, and smoking cessation) as well as 
adherence with pharmacologic management are particularly important in the management of 
patients with chronic diseases (9–13). In Alberta, our group has documented that 50% of 
Albertans with these chronic diseases are not receiving guideline-recommended drugs 
(statins), due to a combination of patient, provider and health system level barriers (3). Our 
recent survey of nearly 2,000 Western Canadians suggests that patient-borne cost of 
prescription drugs as well as patient and provider-level knowledge gaps, are major barriers to 
optimal medical management (3). 
 
Cost-related barriers to medication adherence are relevant in Canada: While the Canadian 
publicly funded health care system funds hospital and physician care, patients are responsible 
for many of the direct costs related to outpatient management of their chronic conditions. 
Canada is unique among OECD countries given that not all citizens have insurance coverage 
for outpatient medications (14,15), and even those with supplemental insurance are usually 
subject to cost-sharing (i.e., copayments or deductibles) (16). This requires that patients pay a 
portion of the cost at the time of medication dispensing; typically a copayment of 20-30% of 
the total cost of the prescription (17) or deductibles which may be as high as 5-20% of 
household income (18). This may represent a substantial financial barrier, since our recent 
survey noted that 64% of patients with chronic diseases had an annual household income 
<$55,000 (16). Furthermore, we found that the average self-reported annual out of pocket 
expenses on medications was $782 (95%CI $668-$897). In Alberta, government-sponsored 
drug insurance is provided to people on social insurance and those aged over 65. People who 
do not meet age or income criteria can pay a premium to receive the government insurance. 
All Albertans over age 65, and those who pay a premium to receive Blue Cross coverage pay 
30% copayment for drugs. 
 
While copayments are meant to reduce inappropriate medication use, they may have important 
negative consequences. Being faced with costs at the time of medication dispensing may 
discourage patients from filling prescriptions, including for important preventive medications. 
Recent Canadian studies estimated that up to 10% of people experienced cost-related 
medication non-adherence (19), and that 23% of Canadians with chronic conditions have 
either skipped medication or failed to fill a prescription due to cost (20). In our recent survey 
of Western Canadians with chronic diseases, 8-20% identified financial barriers to drugs; and 
patients with these barriers were 30-55% less likely to use statins (16). Studies suggest that 

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reducing copayments can improve adherence, though the impact on clinical outcomes is 
uncertain.  One way to reduce the impact of these cost barriers is to provide full coverage for 
“high-value” medications (i.e., defined as those medications that have been shown to confer 
important benefits in high quality studies, and/or provide good value for money).  
 
Other patient and provider-level barriers may also reduce appropriate medication use: Optimal 
management of chronic disease requires patient knowledge and a substantial degree of self-
management (21) including adherence to outpatient medication regimens and lifestyle 
behaviour modifications. Our recent survey of Western Canadians suggested that 40% of 
chronic disease patients did not recall receiving counseling regarding lifestyle behavior 
modification, and of those who did, up to 70% of them did not follow the advice given (3). 
Part of this may relate to the format of the message, which (when crafted and delivered by 
health care workers) may be factually correct without effectively changing behaviour. 
Alternatively, patients might receive and understand messages about the value of certain 
health behaviors but require assistance from a health provider to implement the needed 
changes. A prior systematic review examining the impact of 12 different chronic disease 
management strategies for patients with diabetes noted that two of the most effective strategies 
for improving glycemic control and blood pressure were patient education, and facilitated 
relay of information to clinicians (22). Facilitated relay is defined as clinical information 
transmitted by patients to clinicians by means other than the existing medical record (22); the 
expectation is that clinicians act on the information to change patient management. In the 
ACCESS trial, the facilitated relay will involve patients providing personalized 
recommendations on the medications that should be used to treat their chronic diseases 
(provided to the patient within study educational materials) to their regular health care 
provider. Recommendations are based on contemporary clinical practice guidelines and use 
strategic marketing practices targeting patients not receiving guideline-concordant medications 
at study baseline.  
 
3. Trial Objectives 
 
Primary Objective: 
To determine the effect of two novel interventions; (i) a value-based formulary which 
eliminates copayment for select high-value medications (proven to prevent heart attacks, 
stroke, and hospitalizations); and (ii) a comprehensive patient education program aimed at 
lifestyle modification and optimal drug use, combined with relay of information on medication 
use, on the risk of adverse clinical outcomes (mortality, heart attack, stroke, need for coronary 
revascularization, and chronic disease related hospitalizations) over three years. 
 
Secondary Objectives: 
To determine the effect of two novel interventions on medication adherence, overall quality of 
life, and health care costs. 
 
4. Trial Methods 
4.1. Trial Design 
 

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The ACCESS trial is a parallel, open-label factorial 2X2 randomized controlled trial with 
blinded end-point evaluation. Given the nature of these interventions, patient blinding is 
not possible. 
 
4.2. Trial Interventions 
 
1. Elimination of copayments: (reduction from 30% to 0%) for select high-value preventive 
medications by enrolling patients in a new Alberta Blue Cross drug coverage plan which 
was designed by the study team, including leaders at Alberta Health specifically for 
ACCESS (see letter of support, Evans). Since these patients either have or are at high risk 
of cardiovascular disease, the medications include statins, beta blockers, ACE-inhibitors, 
angiotensin receptor blockers, calcium channel blockers, diuretics, anti-platelet agents, 
anticoagulants, oral hypoglycemics, insulin and smoking cessation aids (23)  (see 
Appendix II). These formulary changes will be operationalized by transferring patients 
allocated to this intervention to a new government-sponsored Alberta Blue Cross 
formulary plan with updated payment rules.  
 
2. Patient education strategy on lifestyle modification and medication use designed in 
collaboration with a leading marketing firm (see letter of collaboration, Emergence) and 
based on our recent survey of Western Canadians (3,4,24). The education program will be 
combined facilitated relay of information on patient medication use to their health care 
provider. Patients who are not using guideline-recommended therapy (including statins) at 
baseline will be given personalized recommendations to take to their usual clinician, 
aiming to increase the appropriate use of beneficial medications. Dissemination will occur 
by email invitations to access the personalized study website portal or regular post, 
depending on preferences of the participant (elicited at baseline). Personal health 
information will not be sent within emails. For any educational messages tailored to a 
specific patient’s health information (as provided by them within baseline information 
forms), patients will be directed to log on to a secure password protected website.  
 
Control patients for the two intervention arms will continue to receive all medications as 
per their usual coverage and/or will receive annual mailings of general educational 
materials about chronic disease.  
 
4.3.  Primary Outcomes 
 
Outcomes of relevance to patients with chronic disease: When management of chronic 
disease is suboptimal for extended periods, patients may be hospitalized for complications 
(25). While myocardial infarction, stroke, and the need for revascularization are the 
classical complications (and are components of our primary outcome), hospitalization for 
chronic disease-related ambulatory care sensitive conditions are also relevant, since these 
complications can serve as a proxy for poor control of chronic disease. Ambulatory care 
sensitive conditions (ACSC) are those for which “timely and effective outpatient care, 
including use of appropriate medications, can help to reduce the risk of hospitalization by 
preventing the onset of an illness or condition, or managing a chronic disease” (26). 
While several agencies have examined what constitutes an ACSC for the chronic diseases 

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under study, the most applicable definition of ACSC for cardiovascular diseases, diabetes 
and hypertension using a Canadian context is from CIHI (2009). Since this does not 
include hospitalizations for CKD-related ACSC, this list was taken from a recent 
Canadian study (27). Importantly, these potentially preventable hospitalizations are 
clinically relevant to patients and represent an important economic burden for the system 
(28), which might be avoided through interventions which optimize patient management 
(29,30). 
 
Primary Outcome: The primary outcome is the composite rate of any of the following: 
all-cause mortality, myocardial infarction, stroke, coronary revascularization (coronary 
artery bypass grafting, angioplasty, or coronary stenting), and hospitalizations for chronic 
disease-related ambulatory care sensitive conditions. Given that an individual may have 
multiple events during the follow-up period, we have chosen to use a rate outcome rather 
than a simple binary composite outcome. 
 
4.4. Secondary Outcomes 
 
1) each individual component of the primary endpoint 
2) medication adherence for selected medications as defined below 
3) overall quality of life as measured by the Euroqol EQ5D-5L 
4) the cost of the interventions and other costs relevant to the health care system assessed 
in a concurrent economic evaluation. 
 
4.5.  Tertiary Outcomes (some to be reported in secondary manuscripts) 
 
A) Body-mass index  
B) Smoking status 
C) Prescription of indicated medications 
D) Treatment satisfaction 
E) Medication Needs-Concerns 
F) General barriers to care 
G) Financial barriers to care 
H) Health literacy 
I) Self-reported general health 
J) A1C (among those with diabetes) 
K) Albuminuria 
L) Serum Creatinine/estimated GFR 
M) LDL-cholesterol 
 
4.6.  Timing of outcomes as assessments 
 
The primary outcome data was ascertained through administrative health data held within 
the province of Alberta, including linked datasets such as: laboratory data, pharmacy 
dispensation data, acute care data, outpatient physician claims data, and demographic 
data. Participants were followed for a minimum of 2.5 years from the time of 
randomization through until March 30, 2021. This data will be available as of early 2022. 

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Self-reported outcome measures were obtained via electronic and paper-based surveys 
which were sent to participants 6-, 18-, and 33-months after randomization. 
 
4.7.  Randomization 
 
The University of Calgary Clinical Research Unit developed the computer-generated 
randomization scheme within REDCap. 
Randomization was completed using random small (<8) variable permuted blocks. This 
method will ensure robust allocation concealment.  Randomization was stratified based 
on: age (</≥70 years); gender (man/woman); and low household income status (defined 
by household-size specific Low-Income Cut-offs). 
 
4.8.  Sample Size 
 
Using the cohort of 170,000 Albertans described above, we estimated the annual event 
rate for this study reflecting the expected age distribution of enrollees. We found that this 
cohort of patients at high cardiovascular risk had a 26% risk of myocardial infarction, 
stroke, death or chronic disease-related hospitalization over a three-year observation 
period (Appendix VII). However, the endpoint in our primary outcome is not a binary 
event (many individuals had multiple events during the observation period), and so we 
have chosen to include recurrent events in our sample size calculation. In our 
administrative dataset, the rate of the primary outcome (MI, stroke, revascularization, 
death, or chronic disease-related hospitalization) is 14 per 100 participant years 
(Appendix VII).  
 
We estimated the sample size required for Poisson regression analysis (31), using the 
following parameters:  an annual event rate of 14 per 100 participant years, a minimal 
clinically important difference of a 12% relative risk reduction, α=5%, 80% power, 
allocation ratio of 1:1, average follow up of 3 years and presumed 1 percent per year loss 
to follow-up (32). The estimated total sample size required for the study was 4714 
patients (4667 +1% of individuals who are expected to move out of province during the 
study) (Appendix VIII). Our sample size calculation assumed that there will not be an 
important interaction between our two interventions. To verify this assumption, we 
generated simulations using interactions of 25% and 50%. At both of these levels of 
interaction, with our current projected sample size, the effect on study power was 
negligible. 
 
4.9.  Framework 
 
Two conceptual frameworks influenced the design of this study. First, the health belief 
model summarizes how and why people make health behaviour choices (33) and includes 
the constructs of perceived: susceptibility, severity, benefit, and barriers—many of which 
can be targeted by SMES. Second, the framework proposed by Campbell et al postulates 

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how financial barriers may affect clinical outcomes. These frameworks were used to 
inform the development of the interventions. 
 
The overarching purposes of the interventions in the ACCESS study will be to: 
(1) Increase initiation and adherence to medications that have been proven to reduce the 
risk of cardiovascular events in this population of high-risk patients, including HMG-CoA 
reductase inhibitors (Statins) (34) and renin-angiotensin-aldosterone system inhibitors 
(ACE inhibitors [ACEi] and angiotensin receptor blockers [ARBs]) (35). 
 
(2) Encourage participants to make positive health behavior changes, including healthy 
dietary choices, engagement in physical activity, cessation of tobacco use, and increased 
adherence to all preventive medications. 
 
4.10. 
Interim Analyses and Stopping Guidance 
 
The trial interventions were deemed low risk to cause adverse events, so no DSMB or 
interim analyses were planned, or were possible given the use of administrative data to 
assess outcomes. 
 
4.11. 
Timing of Final Analysis 
 
The final analysis will be started in the summer of 2021, which will focus on the cleaning 
and preparation of the self-reported outcome data which were collected through the 
survey data. 
 
We anticipate receipt of the administrative data from Alberta Health in early 2022, with 
data cleaning to follow and the final analysis to begin March 1, 2022, to be completed by 
July 1, 2022. 
 
4.12. 
Timing of Other Analysis 
 
Secondary analyses will be completed through the summer and fall of 2022, with further 
sub-analyses to be conducted thereafter.  
 
4.13. 
Trial Comparisons 
 
The impact of the two interventions will be conducted independently of one another. We 
will compare each intervention arm to those who did not receive that specific 
intervention.  
 
5. Statistical Principles 
5.1.  Confidence Intervals and P-values 
 
In all our analyses, we will calculate 95% Confidence intervals around all point estimates 
and will calculate p-values using appropriate statistical tests. We will consider our alpha 
value to be 0.05. 

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5.2.  Adjustments for Multiplicity 
 
For the secondary outcomes that are the individual components of the primary outcome, 
we will adjust for multiplicity using the Benjamini-Hochberg procedure (36). We will 
initially sort the primary outcome individual component secondary outcomes by p-value. 
Ranks will be assigned in ascending order of p-value. The Benjamini-Hochberg critical 
value will be calculated using the formula (i/m)Q, where: 
• i = the individual outcome’s p-value’s rank, 
• m = total number of tests, 
• Q = the false discovery rate (25%). 
 
We will then compare our original p-values to the critical value calculated. The largest p-
value that is smaller than the critical value will be the last individual outcome that is 
considered statistically significant. 
 
Given that the other secondary outcomes are exploratory, we will not make adjustments 
for multiplicity in these analyses (37). 
 
6. Outcome Definitions 
6.1. Primary outcome 
 
Record of any of the following identified within the Discharge Abstract Database will be 
counted as one primary outcome. The number of composite outcomes will be used; 
therefore, any one individual may have multiple outcomes within the observation 
window. 
 
The rates of the composite outcome will be calculated by generating the observation 
time, defined as the period from randomization to study completion, taking into 
consideration censoring for death or outmigration. 
 
Changes made to this table are highlighted in red, with justification provided in last 
column 
 
Outcome 
Description 
Classificati
on Source 
Codes 
include
d 
Exclusio
ns 
Source 
Justificati
on for 
change 
 
Myocardial 
Infarction 
 
 
 
 
 
 
 
Acute myocardial 
infarction 
Subsequent 
myocardial infarction 
ICD-10 
I21.X 
I22.X 
None 
Quan et 
al. (38), 
Austin 
et al. 
(39) 
No change 
 
Stroke 
 
 
Central retina artery 
occlusion 
 
ICD-10 
 
H34.1 
I63.X 
 
None 
 
No change 

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Cerebral infarction 
Stroke, not specified 
as hemorrhage or 
infarction 
Intracerebral 
hemorrhage 
Subarachnoid 
hemorrhage 
Transient cerebral 
ischemic attacks 
 
I64.X 
I61.X 
I60.X 
G45.X 
 
Kokotai
lo and 
Hill (40) 
 
Coronary 
Revascularizati
on 
 
 
Coronary 
Angioplasty 
Coronary 
endarterectomy/excis
ion 
Coronary local 
pharmacotherapy 
Coronary Artery 
Bypass 
Canadian 
Classificatio
n of Health 
Intervention
s (CCI) 
 
1.IJ.50 
1.IJ.57 
1.IL.35 
1.IJ.76 
 
 
CIHI 
No change 
 
Death 
 
 
Death (all-cause) 
 
Vital 
Statistics 
 
 
 
No change 
 
Ambulatory 
Care-Sensitive 
Hospitalization 
for 
Diabetes 
 
Type 1 DM with 
coma 
 
 
E10.0^
^ 
 
None 
 
CIHI 
10.64, 
11.64, 
13.64, 
&14.64 
added to 
CIHI 
definition 
of ACSC 
for 
diabetes 
since 2015 
Type 1 DM with 
acidosis 
 
E10.1^
^ 
 
 
 
Type 1 DM with 
hypoglycaemia 
 
E10.63 
E10.64 
 
 
 
Type 1 DM without 
(mention of) 
complication 
 
E10.9^
^ 
 
 
 
Type 2 DM with 
coma 
 
E11.0^
^ 
 
 
 
Type 2 DM with 
acidosis 
 
E11.1^
^ 
 
 
 

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Type 2 DM with 
hypoglycaemia 
 ICD-10 
E11.63 
E11.64 
 
 
 
Type 2 DM without 
(mention of) 
complications 
 
E11.9^
^ 
 
 
 
Other specified DM 
with coma 
 
E13.0^
^ 
 
 
 
Other specified DM 
with acidosis 
 
E13.1^
^ 
 
 
 
Other specified DM 
with hypoglycaemia 
 
E13.63 
E13.64 
 
 
 
Other specified DM 
without (mention of) 
complication 
 
E13.9^
^ 
 
 
 
Unspecified DM 
with coma 
 
E14.0^
^ 
 
 
 
Unspecified DM 
with acidosis 
 
E14.1^
^ 
 
 
 
Unspecified DM 
with hypoglycaemia 
 
E14.63 
E14.64 
 
 
 
Unspecified DM 
without (mention of) 
complication 
 
E14.9^
^ 
 
 
 
 
 
Outcome 
Description 
Classificatio
n Source 
Codes 
include
d 
Exclusion
s 
Sourc
e 
Justificatio
n for 
change 
 
Ambulatory 
Care-Sensitive 
Hospitalizatio
n for 
Chronic 
Kidney 
Disease 
 
Type 2 diabetes 
mellitus with 
ketoacidosis 
Type 1 diabetes 
mellitus with 
ketoacidosis 
Type 2 diabetes 
mellitus with 
ketoacidosis with 
lactic acidosis 
Type 1 diabetes 
mellitus with 
ketoacidosis with 
lactic acidosis 
 
 
E11.10 
E10.10 
E11.12 
E10.12 
 
None 
 
Gao et 
al. (41) 
No change 
Type 2 diabetes 
mellitus with 
coma 
 
E11.00   
 
 

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Type 1 diabetes 
mellitus with 
coma 
ICD-10 
E10.00 
 
 
 
Hyperosmolality 
and 
hypernatremia 
 
E87.0 
 
 
 
Hyperkalemia 
 
E87.5 
 
 
 
Fluid overload 
 
E87.7 
 
 
 
Malignant 
hypertension 
 
I10.1 
 
 
 
Heart failure 
 
I50.x 
 
 
 
Hypertensive 
heart disease 
 
I11.0 
 
 
 
Hypertensive 
heart and renal 
disease 
 
I13.0 
 
 
 
Hypertensive 
heart and renal 
disease 
 
I13.2 
 
 
 
 
Ischaemic 
cardiomyopathy 
 
I25.5 
 
 
 
Dilated 
cardiomyopathy 
 
I42.0 
 
 
 
Other restrictive 
cardiomyopathy 
 
I42.5 
 
 
 
Alcoholic 
cardiomyopathy 
 
I42.6 
 
 
 
Cardiomyopathy 
due to drugs and 
other external 
agents 
 
I42.7 
 
 
 
Other 
cardiomyopathie
s 
 
I42.8 
 
 
 
Cardiomyopathy, 
unspecified 
 
I42.9 
 
 
 
 
Ambulatory 
Care-Sensitive 
Hospitalizatio
n for 
Hypertension 
 
Benign 
hypertension 
 
 
I10.0 
 
See below 
 
CIHI 
No change 
Malignant 
hypertension 
 
I10.1 
 
 
 
Hypertensive 
heart disease 
 
I11 
 
 
 
 
 

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Outcome 
Description 
Classification 
Source 
Codes 
included 
Exclusions Source Justification 
for change 
 
Ambulatory 
Care-Sensitive 
Hospitalization 
for 
Heart Failure 
 
Rheumatic 
heart disease, 
unspecified 
 
 
I109.9 
 
See below 
CIHI 
excludes 
procedures 
when 
estimating 
rates of heart 
failure.  
 
Two of the 
most common 
administrative 
codes for 
heart failure 
were 
inadvertently 
left off our 
list - J81 
I50. These 2 
codes are part 
of the CIHI 
definition of 
heart failure 
Hypertensive 
Heart Disease 
 
I11.0 
 
CIHI  
Hypertensive 
Heart and 
Renal Disease 
and stage 1 
through stage 
4 chronic 
kidney 
disease, or 
unspecified 
chronic 
kidney 
disease 
 
I13.0 
 
 
Hypertensive 
heart and 
chronic 
kidney 
disease 
without heart 
failure, with 
stage 1 
 
I13.10 
 
 

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through stage 
4 chronic 
kidney 
disease, or 
unspecified 
chronic 
kidney 
disease 
Hypertensive 
Heart And 
Renal Disease 
and with 
stage 5 
chronic 
kidney 
disease, or 
end stage 
renal disease 
 
I13.2 
 
J81 
I50 
 
 
 
Ambulatory 
Care-Sensitive 
Hospitalization 
for 
Coronary 
artery disease 
 
Angina 
pectoris 
 
 
I20 
 
See below 
 
Other current 
complications 
following 
acute 
myocardial 
infarction 
 
I23.82 
 
CIHI No change 
Acute 
coronary 
thrombosis 
not resulting 
in myocardial 
infarction 
 
I24.0 
 
 
Other forms 
of acute 
ischemic 
heart disease 
 
I24.8 
 
 
Acute 
ischemic 
heart disease, 
unspecified 
 
I24.9 
 
 
 
* Excludes hospitalizations with one of the following cardiac procedure codes:  
CCP: 47^^, 480^–483^, 489.1, 489.9, 492^–495^, 497^, 498^ 
ICD-9-CM: 336, 35^^, 36^^, 373^, 375^, 377^, 378^, 379.4–379.8  
CCI: 1HA58, 1HA80, 1HA87, 1HB53, 1HB54, 1HB55, 1HB87, 1HD53, 1HD54, 

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1HD55, 1HH59, 1HH71, 1HJ76, 1HJ82, 1HM57, 1HM78, 1HM80, 1HN71, 1HN80, 
1HN87, 1HP76, 1HP78, 1HP80, 1HP82, 1HP83, 1HP87, 1HR71, 1HR80, 1HR84, 
1HR87, 1HS80, 1HS90, 1HT80, 1HT89, 1HT90, 1HU80, 1HU90, 1HV80, 1HV90, 
1HW78, 1HW79, 1HX71, 1HX78, 1HX79, 1HX80, 1HX83, 1HX86, 1HX87, 1HY85, 
1HZ53 rubric (except 1HZ53LAKP), 1HZ55 rubric (except 1HZ55LAKP), 1HZ56, 
1HZ57, 1HZ59, 1HZ80, 1HZ85, 1HZ87, 1IF83, 1IJ50, 1IJ55, 1IJ57, 1IJ76, 1IJ86, 
1IJ80, 1IK57, 1IK80, 1IK87, 1IN84, 1LA84, 1LC84, 1LD84, 1YY54LANJ 
 
6.2. Definition of Medication Adherence 
 
Data extracted from the Pharmacy Information Network will be used to calculate the 
Proportion of Days Covered (PDC): “number of days dispensed”/“number of days 
between prescription renewals” (42,43) using drug data in the ICDC Chronic Disease 
Repository. Patients with medication supplies to cover ≥80 % of observed treatment days 
are considered adherent (44,45). 
 
We will calculate PDC for each participant in the initial 12-month period that they were 
in the trial for both Statins and ACEi/ARBs. For those switching between statins or agents 
within the ACE/ARB class, ANY dispensed medication in the class will be included in 
the analysis.  
 
6.3. Definition of Quality of Life 
 
The Canadian-specific EQ5D index score will be the primary quality of life measure. For 
patients who die during the study, subsequent index scores will be given a score of 0, 
consistent with usual EQ-5D convention (scale is anchored at zero and 1, with zero being 
dead and 1 being perfect health).  
 
Mixed models will be used to compare EQ-5D index scores over time between the 
treatment groups for each intervention.  
 
In secondary analyses of EQ-5D index scores, to assess the impact of missing data, we 
will consider two alternate methods using imputation. Firstly, we will impute missing 
variables using multiple imputation as noted in section 10.3. We will then use this data to 
compare the area under the curve of index scores across each intervention group 
(equivalent to comparing a “QALY” profile for each participant). Secondly, we will used 
last observation carried forward to impute missing values, and then similarly compare the 
area under the curve of index scores. Both approaches will test the impact of missingness 
of data.  
 
7. Trial Population 
7.1.  Recruitment 
 
Throughout the 30-month enrollment period, a variety of recruitment strategies were used 
to identify eligible participants for the ACCESS trial, which we have classified into five 
overarching strategies and 14 sub strategies (Table 1). Participants who called the survey 

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unit were asked an open-ended question to determine how they learned about the study. 
Responses to this question were allocated to one of the 14 sub strategies. 
 
From November 2015 until study completion a total of 12,342 people called the survey 
unit (Fig. 1). Of these potential participants, 4768 were randomized. 
 
Recruitment for ACCESS was time-consuming and costly, but ultimately successful. We 
used 14 sub strategies to recruit the first 4013 participants into the study, at a cost of 
$354,330 CAD, which was approximately 20% of the overall study operating 
expenditures during this period. Despite eventual success, there was a lack of adequate 
planning and budgeting at the beginning of the study to successfully reach the target 
number of participants. Initial planning set out 12 months for recruitment, with only 
$20,000 CAD set aside as a dedicated recruitment budget to contact pharmacies, which 
was to be the sole recruitment strategy. As recruitment continued, the timeline had to be 
extended and considerably more resources had to be put towards recruitment—all the 
other strategies developed once it became clear that the initial plans for recruitment were 
going to be inadequate. No single strategy appeared to succeed in recruiting typically 
under-represented groups; rather, the strategies differed in their ability to recruit various 
types of people (46,47). 
 
7.2.  Baseline Characteristics 
 
Baseline characteristics will be reported in a Table using descriptive summary statistics. 
These will be stratified by group assignment, and t-tests/Chi2 tests will be used to 
examine for inter-group differences in these characteristics. 
 
8. Data Sources 
 
The main data source for the trial will be the ACCESS trial RedCAP database. This contains 
participant group assignments and all patient reported measures from baseline, 6-month, 18-
month, and 33-month surveys. It also contains individuals’ unique provincial health number 
which will permit deterministic linkage to administrative health databases held by Alberta 
Health (including Alberta Precision Laboratory data, Pharmacy Information Network Data, 
Practitioner Claims data, Discharge Abstract Database, National Ambulatory Care Reporting 
System data). All PHNs were verified at the time of study enrollment to be sure that they were 
accurate and active. In cases where a deterministic linkage is not possible, probabilistic 
linkage using name, date of birth, and postal code will be used. 
 
9. Interventions (s) 
9.1.  Handling of Withdrawn Participants 
 
A small proportion of ACCESS trial participants contacted the study coordinating centre 
and requested to be withdrawn from the trial.  
 

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Where individuals requested full withdrawal of all study data, they will be excluded 
entirely. However, in most cases, those requesting withdrawal simply wanted to receive 
no further contact from the study but did not specifically ask to have their data withdrawn. 
 
For the outcomes relying upon administrative data, we will use outcome data for all 
withdrawn participants. For the patient-reported outcomes we will use the responses 
provided to the time of withdrawal and will using multiple imputation methods for the 
missing datapoints thereafter (See 10.3). 
 
10. Analysis Methods 
10.1. 
 Covariate Adjustment 
 
Consistent with usual practice, we will only adjust the primary analysis for stratified 
variables (age (</>70; sex and low-income status) (48). 
 
Assuming that randomization has worked, and we have approximately equal distribution 
of sociodemographic and clinic characteristics between randomization groups, we will not 
adjust our analyses for covariates, as this should have been dealt with through the process 
of randomization. If there is imbalance in important covariates between randomization 
groups, a sensitivity analysis will be conducted statistically adjusting for these imbalanced 
covariates through the modelling process. 
 
10.2. 
 Distributional Assumptions and Outlying Responses 
 
There is the potential that some participants will have had repeated hospital encounters 
and excessive costs. If the number of these participants is imbalanced between the 
randomization groups, these outliers have the potential to skew results. In order to address 
this potential concern, we plan to conduct a secondary analysis in which the participants 
in the top 1% of encounters/costs will be excluded. 
 
10.3. 
 Handling Missing Data 
 
Given that our primary outcome is determined using administrative data, we anticipate 
minimal missing data for our principal analysis. Therefore, we will use intention-to-treat 
principles for this analysis. 
 
However, our secondary outcomes rely on self-reported data and therefore there may be 
considerable missing data points. For the analyses of these outcomes, we will undertake 
the following procedures for handling missing data: 
 
We will first check the degree and patterns of missingness in the data, based on which we 
will consider optimal ways of handling missing data to minimise the bias potential. In the 
presence of missing at random (MAR) given the observed data, we will use methods such 
as multiple imputation or full information direct maximum likelihood to analyze the 
missing data.   
 

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If the MAR assumption is in doubt, then we will also conduct sensitivity analyses to assess 
the potential impact that missing not at random (MNAR) may have on the analytic results.  
Exploration of the sensitivity of conclusion to the MAR assumption may include models 
which allow for missingness that is not random. These models will include variables that 
we find to be related to missingness as well as those potentially related to the outcome 
(including age, sex, baseline medication adherence, comorbidities, household income). 
  
If loss to follow-up is related to the level of the outcome being analyzed, then results 
obtained under the assumption of independent loss to follow-up may be biased; and in this 
situation, we will investigate the magnitude of this problem by using measurements taken 
at previous visits to predict loss to follow-up, and include variables that are determined to 
predict loss to follow-up in our predictive models in order to satisfy the conditions for the 
data to be considered MAR, with maximum likelihood techniques being used to estimate 
parameters. If necessary, we will also examine other approaches in consideration of how 
robust the results will be and whether they provide appropriately conservative estimates for 
the outcome analyses. 
 
10.4. 
 Analysis Methods- Primary Outcome 
 
 
Consistent with an intention-to-treat analysis, we will categorize all participants by their 
randomization group, regardless of compliance (intention-to-treat), in our primary 
analysis. For those participants who are no longer actively receiving the self-management 
intervention or completing follow-up surveys, as permitted by CHREB, we plan to use 
available health administrative data until the time of death or outmigration from the 
province.  
 
A Poisson model will test the main effects of the impact of the interventions on the rate of 
the primary outcome. This technique was chosen as individuals may experience multiple 
outcomes prior to the end of the study period, therefore we will account for both number 
of events and varying observation time within the Poisson model (by using an offset to 
account for losses to follow-up). The likelihood ratio test will test a negative binomial 
regression model within the Poisson model to examine for the presence of overdispersion. 
If present, negative binomial models will be used. 
 
10.5. 
 Analysis Methods- Secondary Outcomes 
 
Medication adherence is a binary variable and will be analyzed using log binomial 
regression (generalized linear models with a log link)—given the likely high prevalence 
of non-adherence.  
 
EQ5D index scores are continuous and will be analyzed using linear regression. 
 
Medication self-efficacy and concerns will be dichotomized and analyzed using logistic 
regression. 
 
10.6. 
Analysis Methods- Exploratory Outcomes and Analyses 

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In addition to the primary analysis, we will conduct time-to- first event analysis and will 
use cox regression models to calculate Hazards Ratios separately for each of these 
outcomes: 
 
- 
Mortality 
- 
MACE: Non-fatal MI, Non-fatal Stroke, CV death 
- 
ACSC hospitalization 
 
10.7. 
 Safety 
 
Given the low-risk nature of our interventions, and since our outcomes will be assessed 
using administrative data (with a one-year lag to receipt of data), there will be no external 
data safety and monitoring board. This study is considered low risk since patients’ 
physicians remain ultimately responsible for managing patients’ medical treatments and 
any complications that may arise as part of their treatment. 
 
10.8. 
 Planned Subgroup Analyses 
 
We have a priori specified particular subgroup analyses, considering the biological 
plausibility for subgroups. We will conduct analyses stratified by subgroup, as described 
in the primary and secondary outcomes sections. We will provide effects with confidence 
intervals for subgroups (rather than p-values), as tests of interactions for subgroups can 
fail to detect important effects. These will be presented in a forest plot (49,50). 
 
The subgroups of interest include: 
 
Gender: Men vs Women (self-reported), likely exclude gender diverse 
Age: >70 years vs 65-69 years 
Income group: <30,000 vs >30,000 
Financial barriers: Present vs Absent 
Condition type: Diabetes // CKD // ASCVD // Risk factors only 
Multimorbidity: 1-2 vs 3-4 indicated conditions 
Primary Care Relational Continuity: Low/Medium vs High 
Specialist Involvement in the year prior to randomization: Yes vs No 
On statin at baseline: Yes vs No 
On ACE/ARB at baseline: Yes vs No 
Living environment: supported living vs. Independent living 
Baseline medication adherence: >80% PDC for all covered meds vs <80% for any 
covered med 
 
10.9. 
Sensitivity Analyses 
 
Sensitivity analyses will be conducted as follows: 
- Excluding those who have admissions/costs in the top 1% of all participants 
- Excluding participants who resided in the same household as another participant. 

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- We will also conduct a complete case analysis to examine the effects of the missing   
     data, where relevant (ie. Self-reported outcomes). 
 
11.  Interaction between intervention arms 
 
Given the factorial design of the trial, we will assess for multiplicative interaction. Because 
each of our two interventions was designed to address very different patient barriers to 
medication adherence (financial barriers vs. Knowledge/motivation), we don’t anticipate 
major multiplicative interaction effects.  
 
12.  Health Economic Analysis 
 
The primary outcome will be mean total 3-year in-study health care costs for patients receiving 
copayment elimination (or not) and those receiving MOXIE (or not), adjusting for co-
intervention received. We will include costs for hospitalization and ED visits, physician claims 
(specialist and primary care physician visit and procedure billing costs), prescription 
medications (including those subject and not subject to copayment elimination), nonphysician 
ambulatory costs (day medicine and day surgery clinics), and outpatient diagnostic imaging 
and laboratory costs. The total costs will be calculated as the sum of these costs. Alberta 
Health uses Canadian Institute of Health Information case-mix grouper methods to estimate 
hospital costs and ambulatory-case costing methods to estimate outpatient costs.  
  
Physician claims will be based on the amount paid by Alberta Health.  The cost of medications 
will be estimated by combining a database containing a comprehensive list of medications 
dispensed to all Alberta residents with a price list from Alberta Blue Cross, including 
dispensing fee. Diagnostic imaging and laboratory costs will be based on estimates provided 
by Alberta Health Services. All costs will be reported in 2021 Canadian dollars. 
  
Assuming a non-gaussian distribution of costs, we will use established methods to enable 
comparisons of mean total costs, as these are easily interpretable and relevant to health care 
payer. We will use non-parametric bootstrap estimates to derive standard deviations and 95% 
confidence interval (95% CI) and mean cost differences between the treatment arms as we 
have done. We will use 1000 bias-corrected bootstrap replications, and sample with 
replacement from the original data, we will estimate the distribution of a sampling statistic to 
derive 95% confidence intervals.  To allow us to control for stratified variables (age (</>70; 
sex and low-income status), we will also use generalized linear models to compare total costs 
across groups, using a gamma distribution and log-link function.  
 
13.  Statistical Software 
 
We will conduct all the analyses using SAS software, version 9.4 (SAS Institute Inc, Cary, North 
Carolina), and Stata, version 17 (Stata Corp, College Station, Texas). 
 
14.  Differences to the protocol 

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None 
 
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