Correlates of Calcium Supplement Use in Older · perceived osteoporosis susceptibility, perceived calcium benefits, natural health product use, residing in Toronto and general osteoporosis

Correlates of Calcium Supplement Use in Older

Community-Dwelling Ontario Women

by

Mary N. Elias

A thesis submitted in conformity with the requirements

for the degree of Masters of Science

Graduate Department of Pharmaceutical Sciences University of Toronto

© Copyright by Mary N. Elias 2011

ii

Correlates of Calcium Supplement Use in Older Community-

Dwelling Ontario Women

Mary N. Elias

Masters of Science

Graduate Department of Pharmaceutical Science

University of Toronto

2011

Abstract

Background: Older Canadian women are not meeting recommended calcium intake levels

and therefore require calcium supplementation to maintain bone mass. Objective: To

examine factors associated with calcium supplementation among older community-dwelling

women, using the Health Belief Model (HBM) as a conceptual framework. Methods: Data

previously collected from Ontario community-dwelling women aged 65 to 90 years (n=798)

were analyzed. Multivariable logistic regression was utilized to determine HBM factors

associated with calcium supplement use. Results: About half (54%) of women reported

taking calcium supplements. Positive correlates of calcium supplementation included:

perceived osteoporosis susceptibility, perceived calcium benefits, natural health product use,

residing in Toronto and general osteoporosis management factors (discussion with a

physician or pharmacist, osteoporosis screening, falls history and preventive health check-

ups); a negative correlate included: use of etidronate therapy. Conclusion: Only half of older

women were taking calcium supplements. Discussions with healthcare practitioners may help

to improve recommended calcium intake levels.

iii

Acknowledgements

I dedicate this thesis to my late grandmother Angel N. Abdelmassih. Being a wise woman,

she taught me that the most important things in life come not by pen and paper but from the

human heart. You are greatly missed Teta but your love and kindness continues to live on

and touches my life each and every day. I miss and love you very much!

To my supervisor, Dr. Suzanne M. Cadarette, I cannot begin to thank you enough for all your

invaluable guidance throughout my MSc training. You have taught me how to effectively set

and meet goals and have continuously encouraged me to strive to become a better researcher.

You have always advised me to be open to opportunities and most importantly you have

shared my philosophy that every experience is a learning opportunity. I will always look

back at the vast amount of skills and knowledge I have gained through my experience with

you.

To my committee members Dr. Heather Boon, Dr. Linda MacKeigan and Dr. Thomas

Brown, thank you for all your expertise and guidance throughout my thesis work. Your

insights have shaped my thesis project and have helped me “think outside the box”.

A special thanks to my colleagues Andrea Burden, Milica Nikitovic and Mina Tadrous. We

have shared many laughs in the CG office and will always embrace the friendship that has

developed among us. Teresa Tsui, thank you for your expertise on Natural Health Product

classification.

Last but definitely not least, to my parents and my brothers, I thank you for your continuous

support and encouragement throughout my MSc education. Throughout you have ensured me

of my end goal. To my father, thank you for always teaching me that an education is vital to

my success and being my example of persistence and perseverance. To my mother, you have

always taught me to approach things with an open heart and mind and to never take for

granted all the wonderful opportunities that have come my way. To both of my brothers, on

the most difficult of days, you have always reminded me that “tomorrow will be another day”

and that I can achieve whatever I set my mind to.

I would also like to acknowledge funding sources that supported my graduate training:

Canadian Institute of Health Research (CIHR) Frederick Banting and Charles Best Canada

Graduate Scholarship MSc Award, CIHR Institute of Health Services and Policy Research Travel

Award, and the Leslie Dan Faculty of Pharmacy, University of Toronto Student Experience Fund

Travel Award. This research was also supported by two grants awarded to Dr. Cadarette:

CIHR Catalyst Grant and Ontario Ministry of Research and Innovation Early Researcher

Award.

Sep`hmot `ntotk Vnou paiwt.

Mary Elias

iv

Table of Contents

Abstract .......................................................................................................................................... ii

Acknowledgements ..................................................................................................................... iii

List of Tables ................................................................................................................................ vi

List of Figures ............................................................................................................................. vii List of Appendices ..................................................................................................................... viii

List of Acronyms .......................................................................................................................... ix

Chapter 1: Introduction ................................................................................................................ 1

1.1 Epidemiology of Osteoporosis in Canada................................................................................. 1 1.2 Calcium Intake, Bone Mass and Osteoporosis ......................................................................... 2

1.3 Statement of the Problem .......................................................................................................... 2

1.4 Overview of Thesis ................................................................................................................... 3

Chapter 2: Literature Review and Conceptual Framework ................................................... 4 2.1 Search Strategy and Inclusion/Exclusion Criteria for Studies .................................................. 4 2.2 Individual-Level Health Behaviour Conceptual Frameworks .................................................. 5

2.2.1 Conceptual Frameworks Used in Previous Studies to Examine Calcium Intake ...............5 2.2.2 Conceptual Frameworks Used In Literature to Examine Individual-level Health

Behaviour .....................................................................................................................................7

2.3 Conceptual Framework: The Health Belief Model (HBM) ...................................................... 8 2.4 Factors Associated with Calcium Intake: Evidence from Previous Studies ........................... 11

2.5 Chapter Summary ................................................................................................................... 19

Chapter 3: Methods ................................................................................................................... 21 3.1 Study Objective ....................................................................................................................... 21

3.2 Overview of Study Sample and Reanalysis of Data from CSOFT ......................................... 21 3.3 Data Source: CSOFT .............................................................................................................. 22

3.3.1 CSOFT Study Objectives ..................................................................................................22 3.3.2 CSOFT Sampling Frame...................................................................................................22 3.3.3 CSOFT Web-Tracing Feasibility Study............................................................................23 3.3.4 CSOFT Sample Size Estimate ..........................................................................................23 3.3.5 CSOFT Study Sample .......................................................................................................24

3.3.6 CSOFT Questionnaire .......................................................................................................26

3.4 Dependent Variable: Calcium Supplement Use ..................................................................... 26

3.4.1 Understanding Association Between Calcium, Vitamin D and Multivitamin Users ........27 3.4.2 Understanding Calcium Users ..........................................................................................27

3.5 Independent Variables ............................................................................................................ 28 3.5.1 Perceived Susceptibility to Osteoporosis ..........................................................................29 3.5.2 Perceived Seriousness to Osteoporosis .............................................................................30

3.5.3 Self-Efficacy .....................................................................................................................30 3.5.4 Personal Factors ................................................................................................................30 3.5.5 Cues to Action...................................................................................................................35

v

3.5.6 Perceived Benefits of Calcium Supplementation .............................................................38

3.5.7 Perceived Barriers to Calcium Supplementation ..............................................................38 3.6 Statistical Analyses ................................................................................................................. 38

3.6.1 Descriptive Statistics .........................................................................................................39

3.6.2 Multi-item Scale Reliability ..............................................................................................39 3.6.3 Logistic Regression Model Building Strategy ..................................................................39

3.7 Study Power and Type I/ Type II Error .................................................................................. 46 3.8 Ethical Considerations ............................................................................................................ 46

Chapter 4: Results ..................................................................................................................... 47 4.1 Sample Characteristics ............................................................................................................ 47 4.2 Internal Consistency................................................................................................................ 48 4.3 Regression Model Diagnostics ............................................................................................... 49

4.3.1 Examination of Nominal and Ordinal Variables ..............................................................49 4.3.2 Examination of Continuous Variables ..............................................................................49

4.3.3 Correlations .......................................................................................................................51 4.4 Logistic Regression Model Building ...................................................................................... 52

4.4.1 Bivariate Analyses ............................................................................................................52 4.4.2 Multivariable Analysis ......................................................................................................53

4.5 Chapter Summary ................................................................................................................... 56

Chapter 5: Discussion ............................................................................................................... 70

5.1 Main Thesis Findings .............................................................................................................. 70 5.2 Generalizability of Findings ................................................................................................... 71

5.3 Comparing of Study Results to Prior Research ...................................................................... 72 5.3.1 Perceived Susceptibility to Osteoporosis ..........................................................................73

5.3.2 Perceived Seriousness of Osteoporosis .............................................................................73 5.3.3 Personal Factors ................................................................................................................74

5.3.4 Cues to Action...................................................................................................................78 5.3.5 Perceived Benefits and Barriers of Calcium .....................................................................81

5.4 Using the HBM to Examine Factors Associated with Calcium Supplement Use .................. 82

5.5 Limitations and Strengths ....................................................................................................... 83 5.6 Recent Controversies of Calcium Supplement Use ................................................................ 86

5.7 Recommendations for Clinical Practice.................................................................................. 88 5.8 Recommendations for Future Research .................................................................................. 90 5.9 Conclusions ............................................................................................................................. 91

References .................................................................................................................................. 93

Appendices ................................................................................................................................ 100

vi

List of Tables

Table 1. Osteoporosis susceptibility domain of the OHBS in CSOFT ................................... 29

Table 2. Osteoporosis seriousness domain of the OHBS in CSOFT ...................................... 30

Table 3. General health motivation items ............................................................................... 32

Table 4. Osteoporosis knowledge items CSOFT .................................................................... 34

Table 5. Calcium benefits domain of OHBS in CSOFT questionnaire .................................. 38

Table 6. Correlation coefficients ............................................................................................. 42

Table 7. Descriptive characteristics of study sample (n=798) ................................................ 57

Table 8. Ten largest correlations between predictor variables ............................................... 58

Table 9. Descriptive statistics and odds ratio estimates for bivariate analyses ...................... 59

Table 10. Multivariable odds ratio estimates for calcium supplement users and non-users ... 62

Table B 1. 3x3 Table for calcium and vitamin D supplement use (n=871) .......................... 101

Table B 2. 3x3 Table for calcium and multivitamin supplement use (n=871) ..................... 101

Table C 1. Comparison of current and past calcium supplement users and non-users ......... 102

Table D 1. List of CSOFT variables not examined in this study .......................................... 106

Table E 1. Independent variables examined ......................................................................... 107

Table F 1. Item response frequency of osteoporosis seriousness items in CSOFT .............. 110

Table H 1. Physical Functioning Score: composite score comprised of scores for each of the

SF-36v2 scales ...................................................................................................................... 112

Table H 2. Mental Functioning Score: composite score comprised of scores for each of the

SF-36v2 scales ...................................................................................................................... 112

Table I 1. Item response frequency of general health motivation items in CSOFT ............. 113

Table L 1. List of chronic health conditions ......................................................................... 116

vii

List of Figures

Figure 1. Adaption of Health Belief Model (HBM) for calcium supplement use.....................9

Figure 2. CSOFT participant selection flow diagram ............................................................. 25

Figure 3. Study sample flowchart ........................................................................................... 64

Figure 4. Histogram and box-and-whisker plot of perceived susceptibility to osteoporosis

scores (n=798) ......................................................................................................................... 65

Figure 5. Pearson residual plot of perceived susceptibility to osteoporosis scores (n=798) .. 65

Figure 6. Histogram and box-and-whisker plot of perceived calcium benefits scores (n=798)

................................................................................................................................................. 66

Figure 7. Pearson residual plot of perceived calcium benefits scores (n=798) ...................... 66

Figure 8. Histogram and box-and-whisker plot of perceived calcium benefits scores with

removed influential observations (n=776) .............................................................................. 67

Figure 9. Pearson residual plot of perceived calcium benefits scores with removed influential

observations (n=776) .............................................................................................................. 67

Figure 10. Histogram and box-and-whisker plot of SF-36v2 physical functioning composite

scores (n=798) ......................................................................................................................... 68

Figure 11. Pearson residual plot of SF-36v2 physical functioning composite scores (n=798)

................................................................................................................................................. 68

Figure 12. Histogram and box-and-whisker plot of SF-36v2 mental functioning composite

scores (n=798) ......................................................................................................................... 69

Figure 13. Pearson residual plot of SF-36v2 mental functioning composite scores (n=798) . 69

Figure A 1. Flow chart of literature search strategy ............................................................. 100

Figure G 1. Frequency distribution of study sample’s age ................................................... 111

Figure J 1. Coding sheet for use of NHPs other than calcium/vitamin D/multivitamins ..... 114

Figure K 1. Coding sheet for level of physical activity ........................................................ 115

Figure L 1. Frequency distribution of chronic health conditions .......................................... 116

viii

List of Appendices

Appendix A- Flow Chart of Literature Search Strategy ....................................................... 100

Appendix B- Study Participants Reporting Calcium, Vitamin D and Multivitamin Use ..... 101

Appendix C- Characteristics of Current, Never and Past Calcium Supplement Users ........ 102

Appendix D- CSOFT Variables Not Examined in This Study ............................................. 106

Appendix E- Independent Variables in This Study .............................................................. 107

Appendix F- Osteoporosis Seriousness Items ....................................................................... 110

Appendix G- Frequency Distribution of Age ....................................................................... 111

Appendix H- SF-36v2 Multi-Item Scales ............................................................................. 112

Appendix I- General Health Motivation Items ..................................................................... 113

Appendix J - Coding Sheet for CSOFT Participants’ Intake of NHPs ................................. 114

Appendix K- Coding Sheet for CSOFT Participants’ Level of Physical Activity ............... 115

Appendix L- Chronic Health Conditions .............................................................................. 116

ix

List of Acronyms

BMD- Bone Mineral Density

CSOFT- Community Study of Osteoporosis Fracture and Treatment

HBM- Health Belief Model

OHBS- Osteoporosis Health Belief Scale

OTC- Over-the-counter

NHP- Natural Health Product

NPHS- National Population Health Survey

RCT- Randomized Controlled Trial

ROC- Receiver Operating Characteristic

SAYC- Study of Arthritis in Your Community

SAS- Statistical Analysis System

SF-36v2- Short Form36 Health Survey version 2

Chapter 1: Introduction

1

Chapter 1: Introduction

This introductory chapter describes the research problem. The chapter begins by

describing the epidemiology of osteoporosis in Canada. The importance of calcium for

maintaining bone mass is then explained. The chapter concludes with a statement of the

research problem and an overview of the thesis content.

1.1 Epidemiology of Osteoporosis in Canada

Osteoporosis is a chronic asymptomatic skeletal disease characterized by micro-architectural

deterioration of bone tissue and low bone mass, that increases the risk for fracture [1]. About

25% of Canadian women and 13% of men over 50 years of age have osteoporosis [1], and

the prevalence of the disease increases with age [2]. Osteoporosis is diagnosed through the

measurement of bone mineral density (BMD), yet often remains undiagnosed until the

experience of a fracture [1]. Older individuals, especially women, are at highest risk for

osteoporosis related fracture due to increased muscular weakness, visual decline and age-

related bone loss [3]. The most common types of fracture attributed to osteoporosis include

forearm or wrist, vertebral and hip. Vertebral fractures can lead to chronic back pain,

deformity, height loss, decreased mobility and reduced pulmonary function [4]. Hip fractures

cause chronic pain, disability and increased morbidity and mortality [5, 6], with about 20%

of women dying and about 50% becoming functionally dependent within one year after a hip

fracture [7]. As our population ages, it is projected that more Canadians will suffer from

fractures related to this debilitating disease [8, 9], and therefore osteoporotic fracture

prevention is currently the main focus of osteoporosis management [1].

Chapter 1: Introduction 2

1.2 Calcium Intake, Bone Mass and Osteoporosis

Bone mass contains a high amount of calcium and maintaining adequate calcium

intake can reduce age-related bone loss [10]. With older age, both passive calcium absorption

and active transport of calcium become less efficient [10, 11]. Calcium supplementation is

considered a first-line over-the-counter (OTC) therapeutic option for preventing osteoporosis,

either alone or in combination with bone-building therapies, and is especially important for

individuals with low dietary calcium intake [1, 12-14]. Recent data from a meta-analysis has

shown that calcium supplementation is associated with a decreased risk of fracture [15].

According to the 2010 Canadian osteoporosis guidelines, individuals are encouraged

to use calcium supplementation in combination with vitamin D supplementation, because of

the role of vitamin D in optimizing calcium absorption [1, 14, 16, 17]. The guidelines

recommend a total daily intake of 1200 mg of elemental calcium from diet and supplements

for individuals over the age of 50 years [1]. However, Canadian national and provincial data

show that most Canadian women are deficient in dietary calcium intake levels [16, 18, 19].

About 94% of Canadian women 50 years and older are not meeting the recommended

calcium levels of (1200 mg/day) from diet alone, and 69% are not meeting these levels from

a combination of diet and supplements [19]. As a result, current guidelines recommend that

older Canadian women regularly use calcium supplements to help meet recommended

calcium intake levels, maintain healthy bones and decrease risk for fracture [1].

1.3 Statement of the Problem

Osteoporotic fractures result in considerable morbidity and shortened survival [1].

Adequate calcium intake can help reduce age-related bone loss and fracture risk.

Chapter 1: Introduction 3

Postmenopausal women are at high risk for fracture [1] and are not meeting adequate calcium

intake levels [19, 20]. The regular use of calcium supplements is thus important for helping

reduce fracture risk related to inadequate calcium intake [10, 15]. A better understanding of

the factors associated with calcium supplement use among older women may help inform

health promotion programs targeted at improving calcium supplementation and ultimately

reducing fracture risk. The work of this thesis sought to identify factors associated with

calcium supplement use among older community-dwelling women.

1.4 Overview of Thesis

This thesis is organized into five chapters. This chapter, Chapter 1, reviewed the

importance of adequate calcium intake to reducing osteoporotic fracture risk among older

Canadian women and introduced the research problem. Chapter 2 covers the conceptual

framework chosen and literature review used to inform the thesis. The study objective, data

source, variables and statistical analyses used for this project are then outlined in Chapter 3.

Results are presented in Chapter 4, and findings of the thesis and implications are discussed

in Chapter 5.

Chapter 2: Literature Review and Conceptual Framework

4

Chapter 2: Literature Review and Conceptual Framework

This chapter begins with a summary of the search strategy used to identify prior

studies that examined factors associated with calcium supplement use among older women.

This is followed by a summary of the main conceptual frameworks in the literature that

explain individual-level health behaviour. Justification for the choice of the conceptual

framework chosen to guide the work of this thesis is then provided, followed by a summary

of the factors associated with dietary calcium intake and/or calcium supplement use from the

identified studies. This chapter concludes with a summary of the gaps in previous research

and an explanation of how this study will address those limitations.

2.1 Search Strategy and Inclusion/Exclusion Criteria for Studies

The EMBASE, HealthStar, Health and Psychosocial Instruments, International

Pharmaceutical Abstracts, MEDLINE and PsycINFO databases were searched from database

development to July 2011, to identify previous research that has examined factors associated

with dietary calcium intake or supplement use1 (Appendix A). A total of 479 individual

references resulted from the search. Abstracts, commentaries, letters, news articles, and

review papers were excluded. Papers were also excluded if factors associated with dietary

calcium intake and/or supplement use in older women were not examined. After exclusion

based on title, 109 articles remained and after review of abstracts, 26 eligible articles were

remaining for detailed review.

1 Preliminary searches indicated few studies when the search was limited to calcium supplement use and

therefore the search was extended to include general calcium intake.

Chapter 2: Literature Review and Conceptual Framework 5

After detailed review, 17 of the 26 identified articles were excluded: nine articles did

not examine factors associated with dietary calcium intake and/or supplement use [21-29];

seven did not examine dietary calcium intake and/or supplement use as separate from general

osteoporosis prevention behaviour [12, 30-35]; and one study only examined the association

between dietary calcium intake and specific nutrients consumed (e.g., magnesium and zinc)

[36]. Thus, nine articles remained and each was examined for the conceptual frameworks

used, as well as factors found to be associated with dietary calcium intake and/or calcium

supplement use.

2.2 Individual-Level Health Behaviour Conceptual Frameworks

2.2.1 Conceptual Frameworks Used in Previous Studies to Examine Calcium Intake

Calcium supplementation is considered an osteoporosis prevention health behaviour

[1] and therefore individual-level health behaviour conceptual frameworks were examined to

determine a framework most suitable for the examination of factors associated with calcium

supplement use among older women.

Four of the nine eligible studies identified from the literature search used conceptual

frameworks to guide the investigation of factors associated with dietary calcium intake

and/or supplement use. One study used the Stages of Change Model to explain a woman’s

motivation and readiness to change milk intake, by assessing five different stages of

readiness: 1) precontemplation, 2) contemplation, 3) decision, 4) action and 5) maintenance

[37]. According to the Stages of Change Model, in order for an individual to modify a

prevention behaviour, he/she is required to move from one stage of readiness to the other.

This model, however, was less applicable to examining factors associated with calcium


supplement use because it focuses on explaining an individual’s “stage of change” in relation

to a health behaviour, rather than explaining general factors associated with health behaviour.

Another study used the Health Promotion Model to examine the relationship between

calcium intake and estrogen/hormone therapy in postmenopausal women 50 years of age or

older [38]. The Health Promotion Model attempts to explain health behaviour as the result of

an individual’s motivation to increase his/her well-being [39]. The major assumption of this

model is that individuals take an active role in managing their health, and change behaviour

as a result of knowledge regarding the benefit of the behaviour change [39]. However, most

older women have knowledge deficits regarding osteoporosis prevention, treatment and

consequences [40], and thus the assumption of the Health Promotion Model that focuses on

knowledge may be inadequate to examine calcium supplement use among older women.

The two other studies that have examined factors associated with calcium intake used

the Health Belief Model (HBM) to guide the research [41, 42]. In brief, the HBM suggests

that health behaviour is directly influenced by health beliefs, and indirectly influenced by

personal characteristics as well as experiences that may modify the health beliefs. The HBM

is more comprehensive in scope compared to the Health Promotion Model or Stages of

Change Model and therefore appeared to be suitable to examine correlates of calcium

supplement use among older women. However, it was possible that another conceptual

framework, not previously used in the studies related to calcium intake may have been even

more useful. Thus a broader literature review was initiated.


2.2.2 Conceptual Frameworks Used In Literature to Examine Individual-level Health

Behaviour

We broadened the search to examine review articles investigating other individual-

level health behaviour conceptual frameworks, and therefore identified other commonly used

conceptual frameworks. This led to the identification of two additional conceptual

frameworks: the Precaution Adoption Process Model and the Theory of Planned Behaviour.

The Precaution Adoption Process Model explains an individual’s journey from lack of

awareness to action and maintenance of the behaviour through seven stages: 1) being

unaware of the issue, 2) being aware of the issue but not personally engaged, 3) being

engaged and deciding what to do, 4) planning to act but not yet having acted, 5) having

decided not to act, 6) acting, and 7) maintenance [43]. This model would be appropriate for

examining women’s awareness of the benefits of calcium supplement use in osteoporosis

management, but not appropriate to determining factors associated with the action- calcium

supplement use.

Lastly, the Theory of Planned Behaviour explains an individual’s attitude towards a

behaviour by measuring behavioural intention, attitude, subjective norms and perceived

behavioural control [43]. The theory considers an individual's intention to perform a

behaviour as the most important predictor of his/her actual behaviour and does not account

for demographic or environmental factors that may possibly influence behaviour. A woman’s

decision to use calcium supplements, however, may not only be dependent on personal

intention and can be influenced by external factors such as BMD testing. Thus, this theory

was deemed less relevant to studying factors of association to calcium supplement use.


Therefore, after review of possible conceptual frameworks explaining individual-

level health behaviour, from both the research related to calcium intake and/or supplement

use and the general literature, the HBM was deemed most appropriate for use in this thesis to

examine factors associated with calcium supplement use among older community-dwelling

women. A description and critique of the HBM follows.

2.3 Conceptual Framework: The Health Belief Model (HBM)

The HBM was developed by social psychologists in the 1950s and has been widely

used in the field of psychology to explain and predict individuals’ preventive health

behaviours based on health beliefs, as well as personal factors and experiences [44, 45]

(Figure 1). The underlying premise of the HBM is that individuals who perceive a threat

from a disease and perceive greater benefits than barriers towards taking preventive action

against the disease, will be more likely to engage in preventive health behaviour to avoid

undesirable consequences of the disease. Key to the HBM is understanding of how personal

characteristics and experience modify a patient’s: a) perceived threat of disease and b)

benefit to barrier ratio of the health behaviour. The HBM consists of seven main components

that seek to define an individual’s likelihood of engaging in preventive behaviour [45, 46].

These can be grouped into three main categories.


Figure 1. Adaption of Health Belief Model (HBM) for calcium supplement use [45, 46]

1) Individual perceptions: encompass the level of importance of health to an individual

and affect the perception towards osteoporosis. Factors include:

a) Perceived susceptibility: one’s personal estimate of risk for being diagnosed with

osteoporosis.

b) Perceived seriousness: one’s belief in the severity of having osteoporosis or

leaving it untreated, and includes evaluations of medical, clinical and social

consequences.

Perceived susceptibility and perceived seriousness together constitute perceived threat to

osteoporosis.

c) Self-efficacy2: one’s belief in his/her ability to successfully carry out the

preventive health behaviour.

2 The HBM was extended in the late 1980s to include self-efficacy as a separate component predicting an

individual’s likelihood to engage in a health behaviour.

1) Individual Perception 2) Modifying Factors 3) Likelihood of Action

Personal Factors

Demographics e.g., Age, Education

Sociopsychological Variables e.g., Health Status, Health Motivation

Structural Variables e.g., Knowledge, Lifestyle

Perceived threat of

osteoporosis

Cues to action e.g., osteoporosis diagnosis

Perceived benefits

to calcium supplementation

minus

Perceived barriers to calcium supplementation

Likelihood of taking

calcium supplements

Perceived susceptibility

of osteoporosis

Perceived seriousness of osteoporosis

Self-efficacy to take

calcium supplementation


2) Modifying factors: consist of an individual’s characteristics (personal factors) and

experiences (cues to action) which may modify or influence individual perceptions

towards osteoporosis, as well as perceived benefits of and barriers to the health

behaviour.

d) Personal factors are individual characteristics that influence personal perceptions

of threat to osteoporosis, as well as perception of benefits/barriers to the

preventive health action, and include:

i) Demographics such as age, education and income.

ii) Sociopsychological variables, such as health status and health motivation,

which are psychological factors of an individual that may be influenced by

the individual’s external environment [45].

iii) Structural factors are personal factors that are neither demographic nor

completely psychological, and include knowledge of osteoporosis and

lifestyle factors influencing preventive behaviour.

e) Cues to action are external or internal “triggers,” which can include events or

people that an individual encounters that motivate him/her to take action and may

influence perception of threat to osteoporosis. According to the HBM, cues to

action only directly influence perceived threat to osteoporosis. However, in this

thesis, cues to action were also considered to directly affect personal factors. For

example, having a BMD test is a cue to action which may also alter health

motivation or knowledge about osteoporosis.


3) Likelihood of action: encompasses the probability that the individual will take the

action, based on an assessment of the benefits of and barriers to the preventive health

behaviour.

f) Perceived benefits and

g) Perceived barriers represent an individual’s perception of the benefits and

barriers to action and are considered determining factors to action. If an individual

perceives that there are greater barriers than benefits to taking action, then he/she

will not take action regardless of the threat he/she perceives.

The HBM focuses on individuals’ beliefs in taking preventive action and is aimed at

avoidance of disease. Being a psychological model, it does not take into consideration

environmental or economic factors, which may influence health behaviour and does not

incorporate the influence of social norms on individuals’ health behaviour. However, the

broad definition of the cues to action component, allows for certain environmental factors or

social norms to be accounted for. For example, discussion with others about osteoporosis can

be considered an environmental factor, as well as social norm factor that can be categorized

as a cue to action in triggering preventive health behaviour.

2.4 Factors Associated with Calcium Intake: Evidence from Previous Studies

As noted earlier, the nine identified studies were examined for factors associated with

dietary calcium intake and/or supplement use. The data were summarized to identify a list of

predictor variables focused around the HBM to be tested for association to calcium

supplement use in this thesis. The nine eligible studies are summarized below and


categorized according to whether the study examined factors associated with dietary calcium

intake only, total calcium intake (dietary and supplemental) or calcium supplement use only.

Factors Associated with Dietary Calcium Intake

Two of the nine studies examined factors associated with dietary calcium intake only.

Gulliver and Horwath examined readiness to change milk product consumption in 1224

women aged 25 to 70 years (mean age not provided), recruited through New Zealand

electoral polls (80% response rate) [37]. Participants were given a questionnaire that included

a food frequency component and questions on demographics, health conditions, self-reported

height and weight and changes in milk product consumption. Women who reported seven-

day calcium intake of < 800 mg/day (as identified through the food frequency questions)

were classified as low calcium consumers. Multivariate analysis of variance was then

completed to compare benefits and barriers in women across the different stages of readiness

(precontemplation, contemplation, decision, action and maintenance). Results indicated

positive association between perceived benefits to increasing milk product consumption and

calcium intake, although no explanation for the measurement of perceived benefits was

provided.

In another cross-sectional study, Winzenberg et al., examined factors associated with

dietary calcium intake in 467 randomly selected healthy women between the ages of 25 to 44

years (mean age=37.8, SD=5.4 years), selected from a predominantly Caucasian population

(proportion Caucasian not reported), using Tasmanian electoral polls (63% participation rate)

[47]. Participants were provided with a questionnaire inquiring about calcium-specific food

frequency (used to measure calcium intake in mg), demographics, lifestyle factors,


osteoporosis risk factors, knowledge and self-efficacy. Although the study reports that 2.1%

of the study sample used calcium supplements, measurement of calcium supplementation is

not provided and it is unclear whether calcium levels from supplementation were included

when calculating calcium intake. Multiple linear regression was used to identify factors

associated with daily calcium intake (in mg), and logistic regression analysis was used to

identify factors associated with meeting Australian recommended dietary calcium intake

levels (800 mg from dietary sources). Results from linear regression showed that calcium-

specific knowledge, calcium-specific osteoporosis self-efficacy and education after grade

10 were positively associated with daily calcium intake. No association was found between

daily calcium intake and cues to action (i.e., personal history of fracture), or lifestyle factors

(such as hours of employment or having ever breast fed). Odds for achieving the

recommended calcium intake level also increased with greater calcium-specific self-

efficacy and knowledge, but decreased among smokers and those with low income.

Factors Associated with Dietary and Supplemental Calcium Intake

Four studies examined factors associated with total calcium intake (dietary and

supplemental). Ali et al. conducted a cross-sectional study of 100 Caucasian women between

the ages of 52 to 99 years (mean age=74.2 years), who were recruited from seven meal

centers in urban communities within an American midwestern state [48]. Calcium intake was

measured by 24-hour recall of dietary intake of milk, yogurt and calcium-rich foods, as well

as by type and amount of calcium supplement consumed. Calcium intake scores (in mg) were

calculated for each source of intake (i.e., milk, yogurt, calcium rich foods and calcium

supplements). A total summated score (in mg) of intake from all sources was also calculated.


Participants were categorized into two groups: high and low dietary calcium intake (cut-off

value for categorization was not provided). Perceptions of benefits and barriers to calcium

intake were measured by the Calcium Barriers/Benefits Scale, which consisted of 17 Likert-

scale items (11 of which addressed perceptions of barriers and six addressed perceptions of

benefits). Chi-square analysis and t-tests were calculated and results indicated that on

average those with high calcium intake perceived fewer barriers to calcium intake than

those with low calcium intake.

Researchers from the previous study also conducted a similar cross-sectional study, in

which they examined the effectiveness of variables related to the Health Promotion Model in

predicting total calcium intake [38]. The study sample consisted of a convenience sample of

100 women aged 50 to 88 years (mean age=66.7 years), identified from three churches in a

midwestern American state. Participants were provided with a questionnaire, inquiring about

calcium intake, exercise participation, hormone therapy usage, osteoporosis health beliefs,

demographics and personal characteristics. Calcium intake was categorized as high or low,

yet specifics were not provided in this study. Multivariable logistic regression analysis was

utilized to predict total calcium intake. Women with greater calcium intake had better

perceived health status and general self-efficacy (i.e., belief in general abilities to perform

life activities), as well as greater perceived benefits and fewer perceived barriers to

calcium intake.

A third cross-sectional study used the HBM to examine the association between

osteoporosis health beliefs, demographics and personal risk factors for osteoporosis, to

calcium intake among postmenopausal women [42]. One-hundred and eighty-seven women

aged 65 to 95 years (mean age=75.4, SD=6.5), were recruited from a community pharmacy


and senior nutrition program in two Texan cities and asked to complete a questionnaire. A

total measure of calcium intake was determined from a quantitative food frequency scale and

self-reported calcium supplement use (although details regarding measurement of

supplementation was not provided). Hierarchical multiple linear regression was utilized to

examine factors associated with calcium intake. Osteoporosis health beliefs (i.e., perceived

susceptibility to osteoporosis, perceived seriousness to osteoporosis, health motivation,

benefits of calcium intake, barriers to calcium intake and self-efficacy of calcium intake),

were included as the first block and demographics (age, race/ethnicity, education, income),

lifestyle factors (weight, height change, smoking, alcohol consumption) and cues to action

(family history) were added as the second block. The study found that only self-efficacy of

calcium intake contributed positively and significantly to calcium intake. Personal factors

(i.e., race/ethnicity, education, income, weight, height change, smoking, alcohol

consumption) and cues to action (family history) were not significantly associated with

calcium intake.

A Canadian focus group study by French et al., sought to determine factors

preventing postmenopausal women (i.e., 50 years and older) with low BMD (i.e., t-score ≤-

1.0) from meeting calcium recommendations of 1500 mg/day (based on the 2002 Canadian

guidelines) [49]. A random sample of women who had attended a multidisciplinary

osteoporosis treatment program in an urban city were invited to participate. Thirty (29%

response rate) postmenopausal women (mean age=67.4, SD=10.1) participated, of which

96% were Caucasian, 43% were diagnosed with osteopenia and 57% with osteoporosis.

Three major barriers to adequate dietary calcium intake were identified: insufficient

knowledge, self-efficacy to follow the dietary calcium recommendations and impeding


lifestyle factors (i.e., shift-work and lactose intolerance). Forgetfulness was noted as

especially hindering participants from regularly consuming calcium supplements. Lastly,

women were also concerned about weight-gain from calcium-rich foods and not tolerating

the side effects of calcium supplements.

Factors Associated with Supplemental Calcium Intake

Three studies examined factors associated with calcium intake only from

supplements. A pilot study by Hseih et al. was conducted to determine the association

between osteoporosis preventive behaviours and health beliefs among 60 English-speaking

women 40 to 95 years (20 patients in each age group: 40-55 years, 56-70 years and 71-95

years), who were recruited from an urban academic family practice and retirement

community [41]. Osteoporosis preventive behaviour was defined based on a composite of:

weight-bearing exercise, the use of hormone replacement therapy, and calcium and/or

vitamin D supplement use. Correlations were calculated to determine the association between

demographic variables (i.e., age, race and education), osteoporosis health beliefs (i.e.,

motivation, barriers, active participant in health care, frustration and benefits) and other

selected non-health belief items to osteoporosis preventive behaviour. Behaviours to prevent

osteoporosis were not associated with demographics or any osteoporosis health beliefs.

Furthermore, the study reported that although women perceived high seriousness to

osteoporosis, the majority did not perceive personal susceptibility to the disease.

The study by Cline and Worsley examined associations between osteoporosis health

beliefs and calcium/vitamin D/soy supplement use [50]. The study sample consisted of 990

community-dwelling women aged 45 years and older, who were recruited via a commercial


mailing list in Minnesota (61% response rate). Hierarchical agglomerative cluster analysis

was completed to determine whether certain subgroups existed with regards to osteoporosis

health beliefs and calcium/vitamin D/soy supplement use. Three clusters were identified:

Cluster 1 consisted of women who had greater perceived susceptibility to osteoporosis and

also greater perceived benefits but lower perceived barriers to the use of OTC products.

Women in this cluster were also more likely to have experienced cues to action (i.e., BMD

testing and osteoporosis diagnosis) and more likely to have been using calcium

supplements. Cluster 2 consisted of women that perceived high susceptibility and

seriousness to osteoporosis but believed that there were greater barriers than benefits to

using OTC products. Members of this cluster were the least educated compared to the other

two clusters and more likely to smoke and exercise (greater than three times per week).

Lastly, cluster 3 consisted of women who perceived little susceptibility and seriousness to

osteoporosis and had a strong belief in health promoting behaviours, but perceived less

benefits to taking OTC products. Participants who belonged to this cluster were also the least

likely out of the other two clusters to have had a family history of osteoporosis.

Tyler et al., sought to identify predictors of calcium supplement use compared to non-

use and understand barriers to calcium supplementation [51]. They surveyed 185 women

(95% participation rate) aged 20 to 64 years (mean age=43 years) from six suburban

community-based family medicine practices in Cleveland. Associations between calcium

supplement use and demographics as well as health-related items were determined using

logistic regression. Bivariate results indicated that calcium supplement users were older,

better educated, had higher health motivation (were twice as likely to take daily

multivitamins and more frequently scheduled physical exams), and had a greater number of


cues to action (i.e., higher rates of family history and personal risk for osteoporosis). Results

from the multivariable logistic regression indicated that only greater health motivation

(measured through multivitamin use), older age, and self-rated risk for osteoporosis were

significantly associated with calcium supplement use.

Summary of Prior Research

In summary, prior research identified that dietary calcium intake and/or supplement

use was more likely among those who perceived greater benefits of calcium [37, 38, 50]

and fewer barriers to calcium intake [38, 48, 50]. In addition, although greater perceived

susceptibility to osteoporosis was not related to calcium supplement use in a small

convenience sample (n=60) [41], results from a large randomly selected sample (n=990)

identified a positive association between greater perceived susceptibility and calcium

supplement use [50]. One study identified no association between perceived seriousness of

osteoporosis and calcium supplement use [41], while another study identified that those

perceiving that osteoporosis was more serious were also more likely to perceive greater

benefits to OTC product use [50]. Self-efficacy for taking calcium [42, 47] and general self-

efficacy [38] were both positively associated with dietary calcium intake [47], as well as total

calcium intake [38, 42], but no study examined the association between self-efficacy and

calcium supplement use only. Results differed with regards to associations between cues to

action and dietary and/or supplemental calcium intake. A study focusing on supplementation

reported greater odds of BMD testing and osteoporosis diagnosis among calcium supplement

users [50], yet a study examining dietary calcium intake reported no association between

personal fracture history and dietary calcium intake [47]. Some studies reported no


association between personal factors (such as race, education, income and smoking status)

and calcium intake [41, 42]. Yet, other studies reported positive association between dietary

calcium intake and education [47], older age and calcium supplement use [51], perceived

health status and total calcium intake [38]; as well as between health motivation and dietary

calcium intake [38] and calcium supplement use [51].

Limitations of Previous Studies

The generalizability of results from the nine identified studies is limited. Most did not

focus on the population at highest risk for osteoporosis-- postmenopausal women aged 65

years and older and/or did not use sampling techniques to capture a generalizable sample of

older women. Five studies examined convenience samples [38, 41, 42, 48, 52] such as

recruited from churches [38] or urban medical centres [41]. Furthermore, none of the nine

studies examined a comprehensive list of predictor variables related to calcium intake. The

one study that came close to having a comprehensive list did not calculate measures of

association between predictors and calcium supplement use, but instead examined total

calcium intake [42]. Lastly, most studies did not explain how calcium supplement use was

measured and/or collected, and when explained, the validity of the measurement was not

assessed.

2.5 Chapter Summary

A review of the literature ascertains that a study focused on postmenopausal women

and examining the simultaneous effect of a comprehensive list of validated predictor

variables is required for developing a better understanding of factors associated with calcium


supplement use among postmenopausal women. Of prior research examining factors

associated with dietary calcium intake and/or supplement use, only one study examined

correlates of calcium supplement use specifically, yet studied women who were younger than

65 years of age. In general, previous studies examining dietary calcium intake and/or

supplement use often had one or more of the following limitations: 1) study samples that are

not generalizable to older women, 2) no theory driven identification or examination of

variables, 3) a limited number of independent variables considered, and/or 4) little to no

information on the validity or reliability of outcome measures or predictor variables. Lastly, a

review of prior research and general health behaviour literature, ascertained that the HBM

was the most suitable individual-level health behaviour model for examining calcium

supplement use among older women.

Chapter 3: Methods

21

Chapter 3: Methods

This chapter summarizes the methods used to investigate the research problem

introduced in this thesis. First, the study objective, study design and data source are

presented. The study measures are then described and organized according to the components

of the HBM. A description of statistical analyses, study power and ethical considerations

complete the chapter.

3.1 Study Objective

The objective of this thesis was to identify factors associated with calcium

supplement use in a cohort of older community-dwelling women in Ontario, using the HBM

as a conceptual framework.

3.2 Overview of Study Sample and Reanalysis of Data from CSOFT

The Community Study of Osteoporosis Fracture and Treatment (CSOFT) was a

cross-sectional study that sampled women 65 years and older via standardized telephone

interview from May 2003 to 2004 and collected data on osteoporosis risk factors, health

beliefs and osteoporosis management. The study sample for this thesis was comprised of

participants from CSOFT. Data from CSOFT were analyzed using multivariable logistic

regression to determine factors associated with calcium supplement use.

Chapter 3: Methods 22

3.3 Data Source: CSOFT

3.3.1 CSOFT Study Objectives

CSOFT had two main objectives: 1) estimate the proportion of community-dwelling

women aged 65 years and older who were: a) being investigated for osteoporosis by BMD

testing, and b) being treated for fracture prevention; as well as 2) identify the barriers and

facilitators to BMD testing and osteoporosis treatment through the use of a health services

utilization framework [53]. CSOFT utilized Anderson’s behavioural model of medical access

and health services use as the conceptual framework to identify barriers and facilitators to

BMD testing and osteoporosis treatment [54]. Anderson’s model focuses on identifying

factors facilitating or impeding health services utilization by proposing that the behaviour

(health services use) is influenced by the environment and population or individual

characteristics [54]. This model was not suitable to guide the work of this thesis because the

behaviour under examination (calcium supplement use) is not a health service, but an

individual-level health behaviour.

3.3.2 CSOFT Sampling Frame

The sampling frame for CSOFT was obtained from a list of individuals who

completed a screener questionnaire between 1995 and 1997, as part of another study entitled

the Study of Arthritis in Your Community (SAYC). The SAYC screener was essentially a

census of all residents aged 55 years and older, based on 1994 tax records. The purpose of

the screener was to obtain an estimate of osteoarthritis prevalence among residents aged 55

years and older in two regions of Ontario: Oxford Country (southwestern rural Ontario) and

East York (borough of Toronto); and identify adults with moderate to severe osteoarthritis


who were eligible for longitudinal follow-up [55]. The two regions were chosen based on 3

criteria: 1) rate of knee and hip arthroplasty (low and high), 2) proximity to study center (3-

hour driving time from Toronto) and 3) at least 2000 people in each age group: 55-64, 65-74

and >75 years within each region [56]. Of 27,745 women identified, 16,521 participated in

the SAYC screener and 2,358 were identified to have moderate to severe osteoarthritis and

thus were eligible for SAYC longitudinal follow-up [57]. The CSOFT study sample was

selected from the subset of women ineligible for SAYC longitudinal follow-up (i.e.,

n=14,163; the 86% who did not have moderate to severe osteoarthritis between 1995-1997)

(Figure 2).

3.3.3 CSOFT Web-Tracing Feasibility Study

First, the practicality of locating women using the SAYC list as a sampling frame,

was determined by a web-tracing feasibility study. A random sample of 750 women from the

14,163 ineligible for SAYC, was selected for the web-tracing feasibility study. City and web

directories were searched to identify telephone numbers and current addresses, but women

were not contacted during this feasibility study. The web-tracing study located 79% of

women, suggesting that the sampling frame was useful for CSOFT [58]. These data were

used to inform the CSOFT sample size by estimating that 21% of women may not be located.

3.3.4 CSOFT Sample Size Estimate

Two methods were utilized to identify CSOFT eligibility: vital statistics linkage and

SAYC screener data abstraction. The 14,163 women potentially eligible for CSOFT were

linked to vital statistics at Cancer Care Ontario, and 1,575 (11%) were identified as deceased


[58]. The SAYC screener data were then used to exclude women who did not meet CSOFT

eligibility criteria: age younger than 65 years or older than 89 years (as of January 2003),

non-English speaking, had a hearing disability or lived in long-term care3. A total of 9,722

women were eligible for CSOFT after exclusion based on vital statistics linkage and SAYC

screener information [58]. Among these, 507 (n=254 Oxford County, n=253 East York) were

part of the web-tracing feasibility study and were included in CSOFT. The sample of 5074

[58] was then supplemented with a stratified random sample of 993 women (Oxford County

n=496, East York n=497), for a total study sample size of 1,500 (n=750 from each region)

[58]. A sample size of 1,500 was estimated based on a required minimum of 384 participants

(for each region), estimating 21% would not be located (due to being unable to determine

address), and 8% would be ineligible [58].

3.3.5 CSOFT Study Sample

Of the 1,500 women sampled in CSOFT, 1,042 (69%) were deemed eligible, and 871

participated in the study (participation rate=84%) [53] (Figure 2. ). Based on responses from

the SAYC screener questionnaire, CSOFT participants were similar to individuals who

refused to participate, but significantly younger than women who were not contacted or were

ineligible [53]. The proportion of CSOFT participants self-reporting a diagnosis of

osteoporosis, body mass index, and use of etidronate and hormone therapy on the SAYC

screener was similar to CSOFT non-respondents [53].

3 Non-English speakers and those with a hearing disability were excluded because the data collection method

for CSOFT was a telephone interview. 4Traced from the web-tracing feasibility study and alive based on vital statistics linkage and eligible by SAYC

screener data.


Figure 2. CSOFT participant selection flow diagram CSOFT- Community Study of Osteoporosis Fracture and Treatment

SAYC- Study of Arthritis in Your Community

OA- Osteoarthritis a Moderately severe hip or knee complaints based on SAYC screener questionnaire.

b 21% could

not be located via web-tracing; all 750 were assessed for CSOFT eligibility.

c Ineligibility criteria based on data from 1995-1997 SAYC screener questionnaire: hearing impairments (i.e.,

unable to do an interview over the phone), language barriers, residence in long-term care, age younger than 65

or older than 89 years as of January 2003; linkage to vital statistics (excluded deceased).

Toronto and Oxford County Residents ≥ 55 years

1995-1997

SAYC Screener Questionnaire Participants

(n=16,521 women)

n=14,163 women ineligible for SAYC

follow-up

n=2,358 women with moderate

to severe OA eligible for

SAYC follow-upa

n=9,722 women eligible

for CSOFTc

n=4,441 women ineligible

for CSOFT • deceased (vital statistics linkage)

• not between the ages of 65 and 90

• hearing disability

• non-English speaking

• long-term care resident

Study sample n=1,500 (Oxford County: n=750, East York n=750)

Eligible for CSOFT from web-

tracing feasibility pilot study n=507 (Oxford County: n=254, East York: n=253)

New random sample n=993 (Oxford County: n=496, Toronto: n= 497)

14.3%

31.4%

n=458 ineligible • 35% deceased

• 34% moved out of eligible

regions

• 16% could not speak English

• 8% hearing impairment/dementia

• 7% severe life threatening

illness/recent life-saving surgery

CSOFT participants n=871

Eligible for CSOFT n=1,042

Radom sample for web-tracing

feasibility pilot study n=750b

n=243

women

ineligible for

CSOFT


3.3.6 CSOFT Questionnaire

The CSOFT questionnaire collected information on participants’ sociodemographics,

personal and family history of osteoporosis, health services use, chronic health conditions,

health status, medication and supplement use, as well as health beliefs. Health beliefs were

measured using different scales, including: the Osteoporosis Health Belief Scale (OHBS)5

[59], the osteoporosis drug treatment benefits and barriers scale [60], and an osteoporosis

knowledge scale [40]. General health status was measured by the Canadian English Short

Form 36 version 2 (SF-36v2) Health Survey [61]. Data collected via the CSOFT

questionnaire were used to inform this thesis.

3.4 Dependent Variable: Calcium Supplement Use

A single question in the CSOFT questionnaire addressed whether participants were

taking calcium supplements, vitamin D supplements or multivitamins -“Have you ever taken

calcium/vitamin D/multivitamins regularly (i.e., most days)?”. The response options available

were: “never,” “past,” and “now.” Four-hundred thirty-four participants reported current use

of calcium supplements, 364 reported never having used calcium supplements and 73

reported using calcium supplements in the past.

5 CSOFT included a 43-item OHBS scale. The OHBS is a validated scale developed based on the HBM and

measures osteoporosis-related health beliefs under the domains of: 1) osteoporosis susceptibility, 2)

osteoporosis seriousness, 3) exercise benefits, 4) exercise barriers, 5) calcium benefits, 6) calcium barriers and

7) general health motivation. Each of the items were measured on a 5-point Likert scale from strongly disagree

to strongly agree.


3.4.1 Understanding Association Between Calcium, Vitamin D and Multivitamin Users

In an effort to better understand the relationship between calcium supplement use and

vitamin D and/or multivitamins, frequency tables comparing calcium supplementation with

vitamin D and multivitamin use were examined, in a preliminary analysis (Appendix B).

This analysis identified high correlation between calcium and vitamin D (r=0.85), suggesting

that calcium users were taking vitamin D concurrently. Indeed, of the 434 reporting current

use of calcium supplements, 389 (90%) were also taking vitamin D. The association with

multivitamins was also high, yet not as prominent with 247 out of 434 calcium supplement

users also reporting regular multivitamin use. We therefore suspected that correlates of

calcium supplement use will be similar for vitamin D and/or multivitamin use.

3.4.2 Understanding Calcium Users

To guide our analytical plan, we sought to better understand the characteristics of

current, past and never users by comparing characteristics between the three groups

(Appendix C). Multinomial logistic regression may have been an appropriate approach to

examine three different categories of use as: current, past or never. However, past users did

not seem to be a distinct group but had similar characteristics to both current and never users.

For example, the proportion of past users self-reporting osteoporosis diagnosis (30%) was

similar to current users (30%), but different from those who reported to have never used

calcium supplements (11%). Yet, the proportion of past users who reported having a

postsecondary education (16%) was similar to those who had never used calcium

supplements (18%) and different from those who reported current use of calcium

supplements (27%). Furthermore, the CSOFT questionnaire did not clarify the length of time


since stopping calcium supplementation.. However, given the challenge of mapping

characteristics of past users to either current or never users and the unknown length of time

since participants had used calcium supplements in the past, past users were excluded from

the analysis, and only participants reporting current use and those reporting to have never

used calcium supplements were included. Current use of calcium supplements was used as

the dependent (yes/no) variable in this thesis.

A literature search identified two studies that examined the validity or reliability of

self-reported calcium supplement use among older women [62, 63]. One study found that the

one week test-retest reliability of self-reported calcium supplement use in women 65 years

and older was high (r=0.88) [62], based on a single question inquiring about the frequency of

current calcium supplement use (every day, 4-6 days/week, 1-3 days/week, 1-3 days/month,

less than 1 day/month). Given that the CSOFT question inquiring about calcium supplement

use contained broader response options, the reliability of the CSOFT self-report question

about current usage of calcium supplements was expected to be similarly high. Another study

examined the validity of self-reported daily intake of calcium supplements among individuals

aged 50 to 75 years. The study identified moderate correlation (r=0.69, 95% CI=0.60-0.77)

between self-reported daily intake level of calcium from supplements and calculated intake

level from supplement bottle label transcriptions [63]. These data provide support for the

validity of self-reported calcium supplement use.

3.5 Independent Variables

A total of 46 independent variables were chosen from those available in the CSOFT

dataset, based on evidence from prior research and compatibility with the HBM. CSOFT data


collected yet not examined are summarized in Appendix D. We strategically focused on

variables related to the HBM as logical factors that may be associated with calcium

supplementation in an effort to base our analysis on prior evidence, to maximize study power

and to reduce potential chance findings. Including all variables without thoughtful

examination would have increased the potential for type I error (i.e., identifying statistically

significant correlates by chance alone). The independent variables of this study were chosen

and grouped under the HBM categories by the author and based on consensus from the full

thesis committee including Drs. Boon, Brown, Cadarette and MacKeigan. Independent

variables were grouped according to the seven main components of the HBM: 1) perceived

susceptibility to osteoporosis, 2) perceived seriousness to osteoporosis, 3) self-efficacy, 4)

personal factors, 5) cues to action, 6) perceived benefits to calcium, and 7) perceived

barriers to calcium (Appendix E).

3.5.1 Perceived Susceptibility to Osteoporosis

A woman’s perceived susceptibility to osteoporosis was measured using the

osteoporosis susceptibility subscale score of the OHBS. This 5-item subscale (Table 1) had

high internal consistency in CSOFT (Cronbach’s alpha= 0.90)6 [59].

Table 1. Osteoporosis susceptibility domain of the OHBS in CSOFT Items*

Your chances of getting osteoporosis are high. Because of your body build, you are more likely to develop osteoporosis. It is extremely likely that you will get osteoporosis. You are more likely than the average person to get osteoporosis. Your family history makes it more likely that you will get osteoporosis.

*Response options were measured on a 5-point Likert scale from strongly disagree=1 to strongly agree=5.

6 A Cronbach’s alpha between 0.80-0.90 is considered very good (78).


3.5.2 Perceived Seriousness to Osteoporosis

The perceived seriousness subscale of the OHBS consisting of six items was included

in the CSOFT questionnaire. However, the subscale had a low Cronbach’s alpha (=0.66) in

CSOFT. Therefore the six items of the subscale (Table 2) were utilized as separate items in

this thesis study. Each item was re-coded as a dichotomous variable: yes (strongly

agree/agree/neutral) and no (disagree/strongly disagree) (Appendix F).

Table 2. Osteoporosis seriousness domain of the OHBS in CSOFT Items*

The thought of having osteoporosis scares you. If you had osteoporosis you would be crippled. Your feelings about yourself would change if you got osteoporosis. It would be very costly if you got osteoporosis. When you think about osteoporosis you get depressed. It would be very serious if you got osteoporosis.


3.5.3 Self-Efficacy

Self-efficacy was not measured in CSOFT and therefore was not studied in this thesis.

3.5.4 Personal Factors

Demographic Variables

Six demographic variables were examined as independent variables in this study:

1. Age group: 65-69, 70-74, 75-79 and 80-90 years7,

2. current living arrangements: living alone or not,

3. ethnicity: Caucasian or not,

7 Coding determined by data distribution, Appendix G.


4. highest level of education: low: <high school, mid: at least some high school,

or high: post-secondary,

5. income: <$30000, $30000-$49999, $50000, or missing, and

6. metropolitan region of residence: Toronto8 or not (Oxford County).

Sociopsychological Variables

Health Status

Health status was measured using the SF-36v2 and three variables were used as

indicators of health status:

1) General perceived health status: The item: “In general, would you say your health

is excellent, very good, good, fair or poor?”, has been shown to have good

reliability [64] and to be as effective as the complete SF-36v2 in predicting general

health status [65, 66]. The item was used as a measure of general perceived health

status and coded as dichotomous: excellent/very good or good/poor/fair.

2) SF-36v2 physical functioning composite score: This summated score is comprised of

scores from four scales within the SF-36v2 (physical functioning, role-physical,

bodily pain and general heath) and has high reliability=0.92 [67] (Appendix H). It

measures an individual’s perceived physical health status.

3) SF-36v2 mental functioning composite score: This summated score is comprised of

scores from four scales within the SF-36v2 (vitality, social functioning, role-

emotional and mental health) and has high reliability=0.88 [67] (Appendix H). It

measures an individual’s perceived mental health status.

8 Specifically, residence in East York, a borough of Toronto.


Health Motivation

Five categories of health motivation were used in this study: 1) general health

motivation, 2) preventive health check-ups 3) natural health product (NHP) use (other than

calcium/vitamin D/multivitamins), 4) level of physical activity, and 5) smoking status.

1) General health motivation: The health motivation subscale of the OHBS consisting of six

items was used to measure general health motivation in CSOFT. However, the subscale

had a low Cronbach’s alpha (0.64) in CSOFT, indicating that the items of the subscale

were not closely related in measuring a single domain of general health motivation.

Therefore five of the six items of the subscale were utilized as separate items in this

study (Table 3). The other item was used as an indicator for having preventive health

check-ups (see #2 below). Each item was re-coded as a dichotomous variable: yes

(strongly agree/agree) and no (neutral/disagree/ strongly disagree) (Appendix I).

Table 3. General health motivation items Items

You eat a well-balanced diet. You look for new information related to your health. Keeping healthy is very important for you. You try to discover health problems early. You follow recommendations to keep you healthy.


2) Preventive health check-ups (yes/no): Responses to the item “You have a regular health

check-up even when you are not sick,” belonging to the general health motivation

subscale of the OHBS, were coded to create a dichotomous variable: yes (strongly

agree/agree) and no (neutral/disagree/strongly disagree).

3) NHP use other than calcium, vitamin D or multivitamin (yes/no): The CSOFT

questionnaire included an open-ended question inquiring about non-prescription product


use: “Are you currently taking any other supplement (other than calcium, vitamin D or

multivitamin), over-the-counter product or health food store preparation on most days of

the week?”. Responses to this question were not evaluated in CSOFT. For this thesis

study, responses were coded by the author according to the Canadian Natural Health

Products Regulations9 definition of NHPs [68]. The coding sheet was developed in

consultation with a naturopathic doctor (Appendix J). Only NHP use was examined and

products included were: chondroitin, glucosamine, methylsulfonylmethane, vitamins A-

C and E, iron, magnesium, potassium and zinc [69]. NHP use was coded as a

dichotomous variable: NHP use or not.

4) Level of physical activity: Three questions in the CSOFT questionnaire addressed

physical activity: a) “How many city blocks or their equivalent do you normally walk each

day?”, b) “What is your usual pace of walking?”, and c) “Please list any sports,

recreational or activities that you have actively participated in during the past year. Please

remember seasonal sports or events, and include use of self-propelled wheelchair,

walking, gardening, chores, etc.” Responses to these questions were also not evaluated in

CSOFT. For this study physical activity was coded by the author according to Canada’s

Physical Activity Guide to Healthy Active Living for Older Adults [70]. Type of

physical activity was grouped by the author as one of three categories: endurance,

strength/balance or flexibility; and the level of activity was coded as: target, moderate or

none (Appendix K). For this thesis, a single categorical variable was then created based

on the level and type of activity: 1) none (having no activity), moderate (meeting

9 NHPs were defined as OTC products that do not require a prescription and include vitamins and minerals,

herbal remedies, homeopathic medicines, traditional medicines (such as traditional Chinese medicines),

probiotics and other products (i.e., amino acids and essential fatty acids).


moderate level of activity or less in at least one type of activity), and target (meeting

target activity in at least one type of activity).

5) Smoking status (yes/no): In CSOFT, participants reported whether they had never

smoked or whether they were current or past smokers. In this study, current smoking

status was used as an indicator of health motivation and was coded as a dichotomous

(yes/no) variable.

Structural Variables

Knowledge

Osteoporosis knowledge in CSOFT was measured using the “Osteoporosis and You”

questionnaire which contained ten knowledge items [71]. In CSOFT, six items were excluded

because of a low index of difficulty, with 75% of participants responding correctly (Table 4)

[40]. Internal consistency calculations in CSOFT precluded the use of the four items as a

multi-item scale [40]. Therefore, in this study the four items were utilized as separate items

and each coded as dichotomous variables: correct response vs. incorrect response.

Table 4. Osteoporosis knowledge items CSOFT Items*

There is no way to prevent osteoporosis.a

Bones can be rebuilt once they thin from osteoporosis.a

If a woman has osteoporosis, something as simple as lifting a bag of groceries can break a bone.b

The health problems caused by osteoporosis can be life-threatening. b

*Response options were measured on a 5-point Likert scale from strongly disagree=1 to strongly agree=5. a Correct response: false (strongly disagree/disagree/neutral)

b Correct response: true (strongly agree/agree)

Lifestyle

Three lifestyle variables were examined:


1) Lactose intolerance (yes/no): Self-reported lactose intolerance was coded as a

dichotomous (yes/no) variable.

2) Adequate dietary calcium intake: In CSOFT, total dietary calcium intake (mg/day) was

calculated using the calcium calculator [72], based on responses regarding the amount of

milk, cheese and yogurt consumed. For this thesis, adequate dietary calcium intake was

coded as a dichotomous variable: met the recommended 2002 Canadian osteoporosis

guidelines daily calcium intake level of 1500 mg/day for postmenopausal women or not.

We also examined dietary calcium intake based on the 2010 recommended calcium

intake level. This variable was coded: met recommended 1200 mg/day or not. However,

this variable was not considered in regression modeling and was only used for

descriptive characteristics when examining the study sample, in order to better

understand whether the study sample was meeting current recommended calcium intake

levels.

3) Competing health conditions: The number of chronic conditions being treated by a

physician was categorized as none, one, or two or more conditions, based on data

frequency (Appendix L).

3.5.5 Cues to Action

Three categories of cues to action were considered: 1) osteoporosis risk factors, 2)

prior osteoporosis management, and 3) discussion with others about osteoporosis.

Osteoporosis Risk Factors

Five measures of risk factors related to osteoporosis were included:


1) Maternal history (yes/no): of osteoporosis, osteopenia, low bone mass, stooping,

excessive height loss, kyphosis or any adult fracture.

2) Fall history (yes/no): Any self-reported fall in the past year.

3) Low trauma fracture history (yes/no): Fractures after the age of 40 years - coded as low

trauma fractures based on consensus by CSOFT investigators [53].

4) Height loss > 4 cm (yes/no): Self-reported height loss greater than 4cm, after the age of

25 years [73].

5) Early menopause (yes/no): Stop of menstruation before 45 years of age.

Prior Osteoporosis Management

Four measures of prior osteoporosis management were studied:

1) Osteoporosis diagnosis (yes/no): Based on responses to the CSOFT question “Has a

doctor ever told you that you have osteoporosis?”

2) Previous BMD test (yes/no): Self-reported BMD testing measured as a dichotomous

(yes/no) variable was used in this study as an indicator of prior osteoporosis management.

Validation studies in CSOFT identified that self-report of having had a BMD test is very

good (positive predictive value=93%, 95% CI=90.6-95.7; sensitivity=98%, 95%

CI=95.9-99.1; specificity=93%, 95% CI=89.8-95.4) [74, 75].

3) Current osteoporosis treatment: self-reported use of osteoporosis treatment was

considered. A recent linkage study identified very good agreement (kappa=0.81, 95%

CI=0.76-0.86) between self-reported osteoporosis pharmacotherapy and Ontario

pharmacy claims data [75]. In particular, etidronate therapy was the only prescription

drug available as a combination therapy with calcium during the period of CSOFT data


collection [76]. Indeed, a validation study of CSOFT found that women reporting use of

etidronate therapy were using cyclical etidronate therapy (i.e., combination therapy with

calcium supplementation) -- high validity (kappa statistic=0.86, 95% CI=0.80-0.92) [76].

To differentiate between women treated for osteoporosis and receiving calcium from

those not taking combination calcium supplementation, the data were coded as a

categorical variable: no osteoporosis treatment, cyclical etidronate, or other osteoporosis

treatment (alendronate, risedronate, calcitonin and/or raloxifene).

4) Hormone therapy use (yes/no): Hormone therapy may be used as first-line therapy for the

prevention of hip, nonvertebral and vertebral fractures in menopausal women requiring

treatment of osteoporosis and vasomotor systems [1]. For this study, current hormone

therapy use was coded as a dichotomous (yes/no) variable.

Discussion with Others about Osteoporosis

Three variables were used to measure discussions about osteoporosis:

1) One CSOFT question asked whether participants had talked to family, friends, or health

care professionals about osteoporosis. For this study, responses to this question were

coded as two separate variables:

a. Talked with a pharmacist in the past year about osteoporosis (yes/no).

b. Talked to family or friends in the past year about osteoporosis (yes/no).

2) Another CSOFT question inquired about whether participants had discussed the

importance of calcium for bones or joints with a physician. Responses to this question

were coded as a dichotomous (yes/no) variable.


3.5.6 Perceived Benefits of Calcium Supplementation

The calcium benefits subscale of the OHBS was used to measure a woman’s

perceived benefits to calcium intake in CSOFT. In CSOFT, the 5-item subscale (Table 5)

had high internal consistency with a Cronbach’s alpha value of 0.89 [59].

Table 5. Calcium benefits domain of OHBS in CSOFT questionnaire Items*

Taking in enough calcium prevents problems from osteoporosis. You have lots to gain from taking in enough calcium to prevent osteoporosis. Taking in enough calcium cuts down on your chances of broken bones. You feel good about yourself when you take in enough calcium to prevent osteoporosis. Taking in enough calcium cuts down the chances of getting osteoporosis.


3.5.7 Perceived Barriers to Calcium Supplementation

No items in CSOFT were direct measures of perceived barriers to calcium

supplementation. As a proxy, two items from the CSOFT questionnaire that inquired about

barriers to medications were included in this thesis: “You are taking too many medications”

and “You have stomach problems that limit your ability to take drug treatment.” Responses to

these two items were categorized dichotomously: yes (strongly agree/agree) or no

(neutral/disagree/strongly disagree). Since these two items are related to medication rather

than supplementation, they were flagged as being potentially poor measures of perceived

barriers to calcium supplementation.

3.6 Statistical Analyses

All analyses were performed using SAS (Statistical Analysis System) version 9.2

[77]. Descriptive statistics were used to describe the study sample, multi-item scale reliability


was calculated to measure internal consistency of multi-item scales used in this thesis and

logistic regression modelling was completed to determine factors associated with calcium

supplement use. A detail of statistical analyses methods follows.

3.6.1 Descriptive Statistics

Descriptive characteristics summarized for the study sample included demographics,

dietary calcium intake and use of osteoporosis therapy. Categorical variables were

summarized as counts and proportions. Continuous variables were summarized as means and

standard deviations.

3.6.2 Multi-item Scale Reliability

The internal consistency of multi-item scales used in this study was assessed.

Cronbach’s alpha is a measure of internal consistency (reliability) for multi-item scales and

determines if items of the multi-item scale are measuring the same construct [78]. A

Cronbach’s alpha value between 0.65 and 0.70 is considered minimally acceptable, between

0.70 and 0.80 is considered respectable, greater than 0.80 is considered very good [78].

Although Cronbach’s alpha values were determined in CSOFT, the values were also

calculated in this study to account for the different study sample size since women reporting

past use of calcium supplements were excluded.

3.6.3 Logistic Regression Model Building Strategy

Several preliminary analyses were completed to inform the multivariable regression

model building. Regression diagnostics were examined to confirm that assumptions of


logistic regression were being met. This included examining the frequency of nominal and

ordinal variables and the distribution of continuous variables in relation to the outcome

variable of calcium supplement use. Correlations between independent variables were also

determined to ensure collinearity was not present in the final logistic regression model.

Bivariate logistic regression was then utilized to examine the association between each

independent variable and the dependent variable, and determine variables to include in the

multivariable regression model.

3.6.3.1 Regression Model Diagnostics

3.6.3.1.1 Examination of Nominal and Ordinal Variables

Logistic regression analysis requires that at least one case be present for the possible

combinations between each nominal or ordinal variable and the dichotomous outcome

variable. That is, in a contingency table of the independent variable versus the outcome, no

zero cell should be present. A zero cell is problematic because it yields a point estimate of

either zero or infinity for the odds ratio of the variable in question, once in the regression

model [79]. Contingency tables were examined for each nominal and ordinal variables versus

the dichotomous outcome variable, to determine if any table resulted in a zero cell. Variables

with zero cells were excluded from the regression analysis.

3.6.3.1.2 Examination of Continuous Variables

Logistic regression analysis requires that continuous data fit the logit of the model

and that no influential observations exist in the data [80]. Including influential observations

biases coefficient estimates and can result in very large standard errors associated with the


effect estimates, which may lead to invalid statistical inferences. Thus, the data distribution

for the four continuous variables (perceived susceptibility to osteoporosis score, perceived

benefits to calcium score, SF-36v2 physical functioning composite score and SF-36v2 mental

functioning composite score) were examined. Histograms, box-and-whisker and Pearson

residual plots were plotted. Histogram and box-and-whisker plots were examined to

determine kurtosis and skewness values and whether outliers were present in the data. The

Pearson residual plot, however, provides the most accurate evaluation of fit to the logit model

and identifies influential observations10

[81]. The residuals in the Pearson plot are the

difference between the observed and fitted values for the logit model. No pattern in the

Pearson residual plot indicates that the data fit the logit model and do not require

transformation. Residuals outside the range of ±3 are considered influential observations and

should be removed [81]. The Pearson residual plots were examined to determine whether

continuous data fit the logit and whether any influential observations were present in the

data.

3.6.3.2 Correlations Between Independent Variables

Logistic regression modeling assumes that collinearity does not exist in the

multivariable model [79]. Collinearity exists when there is high correlation between two or

more predictor variables in the multivariable model and can lead to large standard errors and

therefore an increase in type II error [81]. To avoid potential collinearity in the multivariable

model, the strengths of associations between independent variables were examined prior to

10

Influential observations are those that if removed substantially change the estimate of regression coefficients

in the output of the regression model.


logistic regression modeling, by calculating correlation coefficients between independent

variables (Table 6). Correlations between 0.5-0.8 were considered moderate correlations and

those between 0.8-1.0 were considered high correlations [82]. Any pair of variables found to

have a correlation of 0.8 or greater was flagged and only one variable out of the pair was

included in the multivariable model. Decisions regarding which variable to include were

made based on the variable’s relevance to the conceptual framework. In addition, pairs of

variables with correlation coefficients between 0.5 and 0.8 were flagged – then during

regression modeling, standard errors11

of the variables’ effect estimates were examined to

determine if collinearity was present in the regression model and therefore whether one of the

variables required removal from the regression model.

Table 6. Correlation coefficients Measure of Independent Variable

Continuous Categorical Dichotomous

Continuous Pearson Categorical Polyserial Phi Dichotomous Point-Biserial Polychoric Tetrachoric

3.6.3.3 Bivariate Logistic Regression Analyses

To select variables for multivariable regression analysis, each independent variable

was first tested to determine if there was an association with calcium supplement use (the

dependent variable) or not. Bivariate logistic regression was first used to examine the

association between each independent variable and the outcome of interest- calcium

supplement use (Equation 1). All variables with p-value <0.25 in bivariate analysis were

considered in the multivariable analysis [83, 84]. P<0.25 is considered the appropriate cut-off

11

Large standard errors indicate possible collinearity.


value for considering variables into a multivariable regression analysis, leading to the best

predictive multivariable model [83, 84].

Equation 1: Logistic regression equation that examines the association between an

independent variable and calcium supplement use ca*- calcium supplement use (1=yes, 0=no)

3.6.3.4 Multivariable Logistic Regression Analysis

Multivariable logistic regression was used to estimate the probability of using calcium

supplements, given a woman’s perceptions of osteoporosis susceptibility and seriousness,

personal factors, cues to action, and perceptions of calcium benefits and barriers to calcium

supplement use. The regression diagnostics, correlations and bivariate analyses were used to

inform a theory-driven manual backward stepwise approach, in order to find the best reduced

model that explained the data [81]. The following steps were applied:

1) All variables with a p-value <0.25 associated with the regression coefficient, from the

bivariate analyses were considered in the multivariable model. In addition, it was

important that at least one variable from each of the five main HBM components12

was

included in the multivariable model: perceived susceptibility, perceived seriousness,

personal factors (demographics or sociopsychological or lifestyle factors), cues to action

and perceived benefits (Equation 2). If no variable from one or more of the five main

HBM components had a p<0.25 for the regression coefficient from bivariate results, then

the variable with the closest level of significance was included (forced) in the

12

As explained earlier, the self-efficacy component of the HBM was not examined because no variables were

available as a measure of the component. The two items used as proxies for the perceived barriers component

were not forced into the model because it was determined a priori that the items may not be good measures for

the HBM component.

logit(ca*) = β0 + β1(independent variable)1


multivariable regression model regardless of statistical significance level. These variables

constituted the preliminary multivariable model.

Equation 2: Example preliminary multivariable logistic regression model for

analysis of calcium supplement users vs. non-users ca*- calcium supplement use (1=yes, 0=no)

2) A manual backward stepwise elimination method was then utilized to determine the

final main effects model based on exclusion criteria outlined in steps 2a and 2b, which

were used concurrently:

a. Statistical significance of regression coefficient. The statistical significance of

each variable in the multivariable model was examined and variables were

removed in sequence starting with the largest associated p-value, until only

statistically significant variables (p<0.05) remained. The only exception was that

a minimum of one variable for each of the five main HBM components (other

than self-efficacy and perceived barriers) was required in the final regression

model.

b. Level of relevance to the HBM. If two variables had similar p-values, the one less

relevant to the HBM was eliminated first. Similarly, if a variable’s regression

coefficient had an associated p-value on the borderline of statistical significance

(i.e., close to p=0.05), the variable’s relevance to the HBM was assessed to

determine whether it should be removed from the model or not.

A stepwise approach was also used, by which variables at the borderline of

significance were placed back into the regression model, once other variables were removed

logit(ca*) = β0 + β1(susceptibility)1 + β2(seriousness)2 + β3(personal factor)3 + β4(cues to action)4 + β5(calcium benefits)5 + p<0.25 variables from bivariate analyses


to determine if significance was reached. If the variable did not reach the significance level,

once placed back into the regression model then the variable was removed.

To determine the predictive ability of the logistic regression model, the c-statistic was

examined. In logistic regression, the c-statistic is equivalent to the area under the receiver

operating characteristic (ROC) curve. The ROC curve is a plot of sensitivity vs. 1-specificity,

and the area under this curve (c-statistic) quantifies the logistic regression model’s power to

discriminate between calcium supplement users and non-users [85]. The c-statistic ranges

from 0.5-1, where a value of 0.5 indicates that the discriminating power is no better than

random chance, and a value of 1 indicates perfect discriminating power [85].

The variance explained by the model was also examined. In logistic regression,

several pseudo-R2

values can be calculated to determine the variance explained by the model

[86]. The pseudo-R2 calculated by the statistical software used, SAS, is the Nagelkerke’s R

2.

When comparing two logistic regression models, the model with the higher Nagelkerke’s R2

is the model that better predicts the outcome [86]. Nagelkerke’s R2 was used to compare the

preliminary multivariable model to the final main effects multivariable model.

3.6.3.4.1 Sensitivity Analysis

A sensitivity analysis was completed to determine if any possible HBM factors

associated with calcium supplement use were lost significance when building the model

based on the HBM (as per step 2b). A second logistic multivariable regression model was

constructed using steps 1 and 2a above, but relevance of the variables to the HBM was not

considered. Variables were removed in sequence starting with variables having the largest


statistically non-significant regression coefficients, regardless of representation of the HBM

components.

3.7 Study Power and Type I/ Type II Error

Study power was calculated to minimize type II error. To ensure adequate study

power when using logistic regression, a 10:1 ratio of number of responses to number of

variables [87, 88] is required. Data were available on 364 “never users” and 434 “current

users.” Therefore, based on the smaller group (never users) and the 10:1 ratio, there was

enough study power to identify 36 predictors in the final main effects regression model.

Furthermore, our careful consideration of variables based on the conceptual framework

helped reduce the potential to find statistical significance by chance alone, that may

otherwise have occurred by including all variables in a model without effort to restrict to

those with conceptual plausibility.

3.8 Ethical Considerations

Ethical approval for the original CSOFT project was granted by the Sunnybrook and

Women’s College Health Science Research Ethics Board (Research Ethics Board project

identification #: 087-2003). Anonymous data are now stored on a password-protected secure

server at the Leslie L. Dan Pharmacy building at the University of Toronto. Ethics approval

for this thesis study was granted by the Health Sciences Research Ethics Board at the

University of Toronto (Protocol Reference # 25661).

Chapter 4: Results

47

Chapter 4: Results

The results for this thesis work are presented in this chapter. The chapter begins with

a description of the study sample and internal consistency of multi-item scales. Regression

diagnostics, including the examination of independent variables in meeting logistic

regression assumptions are then presented. The last part of the chapter includes the regression

results, with a presentation of results from the bivariate analyses followed by results from the

multivariable and sensitivity analyses.

4.1 Sample Characteristics

The CSOFT dataset consisted of 871 community-dwelling women from Ontario.

After excluding women that reported use of calcium supplements in the past but not currently

using calcium supplements (n=73), the total sample size for this study was 798 (mean

age=75.4, SD=6.1) (Figure 3). The study sample was 96% Caucasian, with 48% residing in

Toronto at the time of data collection. Fifty-four percent of the study sample reported to have

been living alone and 45% were married or in a common law relationship. Only 23% of the

total study sample reported having a post-secondary education.

Of the 798 respondents, 434 reported regular use of calcium supplements (54%)

(Table 7). Only 2.5% of the study sample reported dietary calcium intake that was calculated

as meeting the 2002 recommended calcium intake level of 1500 mg/day from dietary calcium

intake alone, while 9% of the study sample reported dietary calcium intake that met the 2010

recommended calcium intake level of 1200 mg/day (Table 7).

Chapter 4: Results 48

48

4.2 Internal Consistency

Internal consistency, measured by Cronbach’s alpha, determines the reliability of the

items of a multi-item scale in measuring the same concept [78]. Although internal

consistency was examined for the multi-item scales in CSOFT, it was important to also

examine internal consistency in this thesis, given the change in sample size (i.e., after

excluding the 73 past calcium supplement users for a total sample size of n=798). Four multi-

item scales were examined in this study: perceived susceptibility to osteoporosis subscale of

the OHBS, perceived benefits to calcium subscale of the OHBS, SF-36v2 physical health

functioning multi-item scales and the SF-36v2 mental health functioning multi-item scales.

The perceived susceptibility to osteoporosis subscale of the OHBS had a Cronbach

alpha value of 0.90 in this study (n=798), which was equivalent to the Cronbach alpha value

of 0.90 determined in CSOFT (n=871). Therefore, this scale had very good internal

consistency and was a reliable measure for perceived susceptibility to osteoporosis.

Similarly, the perceived benefits to calcium subscale of the OHBS had a Cronbach’s alpha

value of 0.90 in this study (compared to 0.89 in CSOFT), and thus was determined to be a

very good measure of perceived benefits to calcium.

Items of the multi-item scales comprising the physical functioning composite score

have been shown to have very good reliability (Cronbach’s alpha=0.92) [67], as well as those

comprising the mental functioning composite score (Cronbach’s alpha=0.88) [67] (Appendix

H).

Thus, the perceived susceptibility to osteoporosis subscale of the OHBS, perceived

benefits to calcium subscale of the OHBS, SF-36v2 physical health functioning multi-item


49

scales and the SF-36v2 mental health functioning multi-items scales were deemed

sufficiently reliable to be used in this thesis as multi-item scales.

4.3 Regression Model Diagnostics

Independent variable distribution and frequency was examined and correlations

between independent variables were calculated to determine if logistic regression

assumptions were met.

4.3.1 Examination of Nominal and Ordinal Variables

An examination of contingency tables for the outcome versus each nominal and

ordinal variable in this study resulted in no table yielding a zero cell. Therefore, all 42

nominal and ordinal variables of this study were acceptable for use in the regression

analyses.

4.3.2 Examination of Continuous Variables

The distribution of data was examined for the four continuous variables: perceived

susceptibility to osteoporosis score, perceived benefits to calcium score, SF-36v2 physical

health functioning composite score and SF-36v2 mental health functioning composite score.

The histogram and box-and-whisker plots for the perceived susceptibility to

osteoporosis scores indicated mild skewness in the data (skewness=0.74) and no presence of

outliers (Figure 4). The residuals in the Pearson residual plot lay in a horizontal band with no

indication of a curvature pattern and therefore suggested that the scores were appropriate to

use in logistic regression. In addition, all the residuals in the Pearson residual plot were


50

between -3 and +2 (Figure 5) and therefore no data points were considered influential

observations. It was thus concluded that the perceived susceptibility to osteoporosis scores

did not require transformation to fit the logit model and did not contain any influential

observations.

The distribution of the perceived benefits to calcium scores was mildly skewed

(skewness=-1.25) (Figure 6). The Pearson residual plot of the perceived benefits to calcium

scores showed that there were influential observations in the data, with residuals ranging

from +7 to -4 (Figure 7). Influential observations (n=22), were removed and this was

deemed appropriate because the influential observations comprised less than 5% of the total

data. The resulting Pearson residual plot with removed influential observations yielded all

residuals within the range of -3 to +3. (Figure 8 and Figure 9).

The SF-36v2 physical composite score had a kurtosis value of -1.0 and skewness

value of 0.44, indicating slight negative skewness in the data (Figure 10). The histogram and

box-and-whisker plots, did not indicate any outliers in the data. The Pearson residual plot for

the SF-36v2 physical composite score had residuals between -2 and +1 with no indication of

curvature, indicating no presence of influential observations or need to transform the scores

(Figure 11).

The SF-36v2 mental functioning composite score had slight negative skewness

(skewness=-0.4), and the histogram and box-and-whisker plots indicated the presence of

outliers in the data (Figure 12). However, the Pearson residual plot (Figure 13) showed that

there were no influential observations in the data (with all residuals being within the range of

-2 and +2), and no indication of curvature. Therefore, the SF-36v2 mental functioning


51

composite score fit the logit transformation of logistic regression and had no observations

that would substantially change the estimate of the regression coefficient.

Hence, all four scores were suitable to use with the logit transformation given in

logistic regression modeling (i.e., no curvature in Pearson residual plots). Only the perceived

benefits to calcium score had influential data points that required removal, and the other three

scores were appropriate for use in regression analyses, without requiring removal of data

points.

4.3.3 Correlations

To minimize the existence of collinearity in the final multivariable model,

correlations between independent variables were examined prior to bivariate and

multivariable analyses. The largest correlation was found between perceived susceptibility to

osteoporosis and self-report of osteoporosis diagnosis (point-biseral correlation

coefficient=0.88) (Table 8), and was deemed to be a high correlation. It was therefore

important to only include one of the two variables in the multivariable regression model.

Perceived susceptibility to osteoporosis was the only variable in this study representing the

perceived susceptibility component of the HBM, and was therefore required for inclusion in

the multivariable model, as per the a priori regression model building strategy proposed in

this thesis (see section 3.6.3.4). In addition, a CSOFT validation study identified that self-

reported osteoporosis diagnosis was poor, with only 60% confirmed to have osteoporosis as

identified through BMD patient reports of women reporting osteoporosis diagnosis [74].

Hence, the self-reported osteoporosis diagnosis variable was not considered in regression

analyses because of concerns about its validity.


52

Correlations greater than or equal to 0.5 included:

current osteoporosis treatment and:

o i) previous BMD testing (r=0.73),

o ii) discussion with a physician about the importance of calcium for bone

(r=0.65); and

ethnicity and residence in Toronto (r=-0.70);

previous BMD testing and a discussion with a physician about the importance of

calcium for bone (r=0.70);

preventive health check-ups and the general health motivation item “Keeping healthy

is important to you” (r=0.64);

the two proxy items for perceived barriers to calcium: stomach problems and

perception of taking too many medications (r=0.52).

4.4 Logistic Regression Model Building

4.4.1 Bivariate Analyses

The bivariate results identified factors associated with calcium supplement use (Table

9). Calcium supplement users were similar to non-users with regards to age, primary spoken

language, annual income and living arrangements. A greater proportion of calcium

supplement users had a post-secondary education and significantly more users resided in

Toronto (the metropolitan region).

At least one variable from each of the five main HBM components was found to be a

significant correlate of calcium supplement use (p<0.25). Effect estimates for twelve

variables were found to have p>0.25 and therefore were not considered in the multivariable


53

regression model: the osteoporosis seriousness items: “If you had osteoporosis you would be

crippled,” “Your feelings about yourself would change if you got osteoporosis,” and “It

would be serious if you got osteoporosis”; current living arrangements; general perceived

health status; SF-36v2 physical functioning composite score; SF-36v2 mental functioning

composite score; smoking status, meeting calcium intake level of 1500 mg/day, early

menopause; competing health conditions; and perception of taking too many medications.

The 33 variables that had significant association (p<0.25) with calcium supplement use from

the bivariate analyses were considered in the multivariable regression model.

4.4.2 Multivariable Analysis

Given that 22 influential observations were identified for the perceived benefits to

calcium score, these participants were excluded from the multivariable model, leaving 776

participants in the final model. Contingency tables of each nominal and ordinal variable

versus the outcome were again examined to determine if any table yielded a zero cell; and the

general health motivation item “Keeping healthy is important to you,” was thus removed. The

preliminary effects model therefore consisted of 32 independent variables.

After backward stepwise regression analysis, the final effects model consisted of ten

predictor variables (Table 10). Eight variables were positively associated with calcium

supplement use: perceived susceptibility to osteoporosis, perceived benefits of calcium,

residence in Toronto, NHP use, self-reported previous BMD test, fall in the past year,

discussion with the physician about importance of calcium for bone and a discussion with a

pharmacist about osteoporosis in the past year. Current treatment with etidronate was

negatively associated with calcium supplement use. The perceived seriousness to


54

osteoporosis item “It would be very costly if you got osteoporosis,” was the only HBM

variable not significantly associated with calcium supplement use. The final regression model

had a c-statistic of 0.88, and therefore had outstanding discriminatory power [79] in

differentiating calcium supplement users from non-users.

Individuals perceiving a greater susceptibility to osteoporosis were more likely to

take calcium supplements, with women about 1.1 times more likely to use calcium

supplements for every one unit increase in the perceived susceptibility to osteoporosis score.

The only demographic variable associated with calcium supplement use was region of

residence, with women residing in Toronto 1.5 times more likely to use calcium supplements,

although this association was on the borderline of significance with a p=0.048. The only

sociopsychological variable that was associated with calcium supplement use was the use of

NHPs other than calcium/vitamin D/multivitamins (OR=1.8).

Cues to action variables were important to calcium supplement use, with five out of

the nine statistically significant variables in the final regression model being cues to action

variables. Women who had experienced a fall in the past year were 1.6 times more to use

calcium supplements than those who did not experience the fall, and those who had a

previous BMD test had about 1.7 times greater odds of being users of calcium supplements.

Women being treated for osteoporosis with etidronate were less likely (OR=0.16) than those

not taking treatment to use calcium supplements. The likelihood of this decreased drastically

from bivariate results, compared to the multivariable result (i.e., bivariate OR=0.98,

multivariable OR=0.16). Furthermore, women who had a discussion with a physician about

the importance of calcium for bone were 3.6 times more likely to be using calcium

supplements, compared to those who did not have the discussion with their physician.


55

Similarly, those who talked to their pharmacist about osteoporosis in the past year were about

4.7 times more likely to be using calcium supplements compared to those who did not talk to

a pharmacist, although there was a wide confidence interval associated with this effect

estimate (95% CI=1.3-17.2). Lastly, those who perceived greater benefits to calcium were

about 2.0 times more likely to take calcium supplements regularly. Therefore, five cues to

action variables were pertinent factors of association with calcium supplement use among

older community-dwelling women.

The preliminary multivariable model explained 57% of the variance, while the final

effects model explained 54% of the variance. This was expected as a result of the large

number of predictor variables in the preliminary multivariable model compared to the final

effects model [79].

4.4.2.1 Sensitivity Analysis

A sensitivity analysis to test whether it was useful to consider the HBM when

building the multivariable model (i.e., theory-driven approach to regression analysis) was

completed. For the sensitivity analysis, variables with non-significant (p≥0.25) regression

coefficients from the bivariate analyses were not forced into the final multivariable model,

regardless of relevance to the HBM. Therefore, the perceived seriousness to osteoporosis

item “It would be costly if you got osteoporosis,” was not forced into the multivariable model

of the sensitivity analysis. The same variables that were in the final regression model that

was constructed based on the HBM were found in the sensitivity analysis model, with the

obvious exception of the perceived seriousness to osteoporosis item (Table 10).


56

Lastly, the value of Nagelkerke’s R2 for the final effects model (0.5432) and for the

model created for the sensitivity analysis (0.5427) were equal to two decimal places,

indicating that correlates in the model did not change regardless of whether or not the

osteoporosis seriousness item was forced into the model.

4.5 Chapter Summary

After examination of regression diagnostics, the self-report of osteoporosis diagnosis

variable was removed from consideration in the regression analysis, because of high

correlation (point-biseral correlation coefficient=0.88) with the perceived susceptibility to

osteoporosis variable and prior evidence that self-report of osteoporosis diagnosis is poor

[74]. Bivariate analyses indicated that all but twelve variables were associated with calcium

supplement use enough to be considered in the multivariable model. The final multivariable

model had high discriminatory power (c-statistic=0.88), and only one variable belonging to

the osteoporosis seriousness component of the HBM was forced in the model regardless of

statistical significance. Eight variables in the final multivariable regression model were

positively associated with calcium supplement use: perceived susceptibility to osteoporosis,

residence in Toronto, NHP use (other than calcium/vitamin D/multivitamins), a fall in the

past year, previous BMD test, discussion about the importance of calcium for bone with a

physician, discussion with a pharmacist about osteoporosis in the past year and perceived

benefits of calcium. Current etidronate treatment was negatively associated with calcium

supplement use. The same variables that were in the final effects model that was constructed

based on the HBM were found in the sensitivity analysis model, with the obvious exception

of the perceived seriousness to osteoporosis item.


57

Table 7. Descriptive characteristics of study sample (n=798)

Descriptive characteristic

N=798 %

Mean age in years, (SD) 75.4 (6.1)* Caucasian 764 95.7 Primary language English 656 82.6 Annual household income

<$30,000 137 17.2 $30,000-$49,999 258 32.3 ≥$50,000 78 9.8 Missing 325 40.7

Live Alone 432 54.4 Marital Status

Married/Common Law 361 45.4 Separated/Divorced 49 6.2 Single 59 7.4 Widow 327 41.1

Highest Level of Education Grade school 177 22.3 High school 433 54.6 Post-secondary 183 23.1

Resided in Toronto 380 47.6 Dietary Calcium Intake

Met 2002 guidelines (1500 mg/day) 20 2.5 Met 2010 guidelines (1200 mg/day) 71 8.9

Current Osteoporosis Treatment None 617 80.1 Etidronate Therapy 76 9.9 Other (aledronate, risedronate, calcitonin, raloxifene) 77 10.0

*standard deviation value


Table 8. Ten largest correlations between predictor variables

Perceived

Susceptibility to OP

OP diagnosis (self-report)

Current OP treatment

Previous BMD testing (self-report)

Talked with physician

about calcium

Ethnicity (Caucasian)

Stomach problems

Preventive health

check-ups

OP diagnosis (self-report)

0.88a --

Current OP treatment

0.50 0.77c --

Previous BMD testing (self-report)

0.35 0.70b 0.73

c --

Talked with physician about

calcium 0.32 0.63

b 0.65

c 0.70

b --

Resided in Toronto 0.11 0.22 0.28 0.31 0.27 -0.70b

Too many medications

-0.01 -0.08 -0.13 -0.0025 -0.05 -0.29 0.52b

Keeping healthy is important to you

-0.05 -0.09 -0.10 0.01 0.20 --d -0.44 0.64

b

OP- osteoporosis

Shaded cells represent the largest 10 correlations. aPoint-biserial coefficient: dichotomous vs. continuous

bTetrachoric coefficient: dichotomous vs. dichotomous

cPolychoric coefficient: dichotomous vs. categorical

d No correlation found because zero cell in 2x2 table of variables


Table 9. Descriptive statistics and odds ratio estimates for bivariate analyses

N=798 % Users

(n=434) Non-users

(n=364) Bivariate (n=798)

OR 95% CI p-value

Perceived Susceptibility OP susceptibility

a, mean (SD) 13.0¹ (4.2)² 2.6¹ (0.3) 2.4¹ (0.3) 1.13 (1.09, 1.17) <0.0001

Perceived Seriousness The thought of having OP scares you, n (%) 234 (29.3) 153 (35.3) 81 (22.3) 1.90 (1.39, 2.61) <0.0001 If you had OP you would be crippled, n (%) 204 (25.6) 110 (25.4) 94 (25.8) 0.98 (0.71, 1.34) 0.88 Your feelings about yourself would change if you got OP, n (%) 293 (36.7) 153 (35.3) 140 (38.5) 0.87 (0.65, 1.16) 0.35 It would be very costly if you got OP, n (%) 513 (64.3) 271 (62.4) 242 (66.5) 0.84 (0.63, 1.12) 0.24 When you think about OP you get depressed, n (%) 83 (10.4) 55 (12.7) 28 (7.7) 1.74 (1.08, 2.81) 0.02 It would be very serious if you got OP, n (%) 739 (92.6) 401 (92.4) 338 (92.9) 0.94 (0.55, 1.60) 0.81

Demographics Caucasian, n (%) 764 (95.7) 408 (94.0) 356 (97.8) 0.35 (0.16, 0.79) 0. 01 Age (years)

65-69, n (%) 192 (24.1) 92 (21.2) 79 (21.7) 1.48 (0.97, 2.26) 0.07 70-74, n (%) 219 (27.4) 122 (28.1) 70 (19.2) 0.87 (0.57, 1.28) 0.45 75-79, n (%) 216 (27.1) 111 (25.6) 108 (29.7) 0.82 (0.54, 1.23) 0.33 80-90, n (%) 244 (28.0) 109 (25.1) 107 (29.4) 1.00

Annual household income <$30,000, n (%) 137 (17.2) 70 (16.1) 67 (18.4) 1.00 $30,000-$49,999, n (%) 258 (32.3) 149 (34.3) 109 (30.0) 1.31 (0.86, 2.00) 0.21 ≥$50,000, n (%) 78 (9.8) 46 (10.6) 32 (8.8) 1.43 (0.81, 2.53) 0.22 Missing, n (%) 325 (40.7) 169 (38.9) 156 (42.9) 1.03 (0.69, 1.55) 0.89

Living Alone, n (%) 432 (54.4) 242 (56.2) 190 (52.3) 1.16 (0.88, 1.55) 0.29 Highest Level of Education

Grade school, n (%) 177 (22.3) 92 (21.4) 85 (23.4) 0.56 (0.36, 0.86) 0.01 High school, n (%) 433 (54.6) 221 (51.4) 212 (58.4) 0.57 (0.39, 0.81) 0.002 Post-secondary, n (%) 183 (23.1) 117 (27.2) 66 (18.2) 1.00

Resided in Toronto, n (%) 380 (47.6) 230 (53.0) 150 (41.2) 1.59 (1.20, 2.12) 0.001 Sociopsychological Factors Health Status Poor perceived health status (excellent/very good/good, fair/poor), n (%)

347 (43.5) 191 (44.0) 156 (42.9) 1.05 (0.79, 1.40) 0.73

SF-36v2 Physical Functioning Composite Score, mean (SD) b 43.7¹ (10.8)² 43.8¹ (10.9)² 43.7¹ (10.8)² 1.00 (0.99, 1.01) 0.88

SF-36v2 Mental Functioning Composite Score, mean (SD) c 53.6¹ (6.7)² 53.4¹ (6.6)² 53.8¹ (6.8)² 0.99 (0.97, 1.01) 0.43

Health Motivation You eat a well balanced diet, n (%) 698 (87.5) 387 (89.2) 311 (85.4) 1.48 (0.96, 2.29) 0.08


Table 9. Descriptive statistics and odds ratio estimates for bivariate analyses (continued)

N=798 %

Users (n=434)

Non-users (n=364)

Bivariate (n=798) OR 95% CI p-value

You look for new information related to your health, n (%) 525 (65.8) 309 (71.2) 216 (59.3) 1.84 (1.36, 2.49) <0.0001 Keeping healthy is very important for you, n (%) 791 (99.1) 432 (99.5) 359 (98.6) 5.88 (0.68, 50.52) 0.12 You try to discover health problems early, n (%) 681 (85.3) 381 (87.8) 300 (82.4) 1.64 (1.09, 2.45) 0.02 You follow recommendations to keep you healthy, n (%) 752 (96.9) 424 (97.7) 350 (96.2) 1.65 (0.72, 3.76) 0.23 Preventive health checkups, n (%) 625 (78.3) 358 (82.5) 267 (73.4) 1.80 (1.28, 2.55) 0.0008 NHP use, n (%) 238 (29.8) 34 (7.8) 14 (3.9) 2.26 (1.63, 3.12) <0.0001 Level of physical activity

None, n (%) 268 (33.6) 130 (30.0) 148 (40.7) 0.53 (0.38, 0.74) 0.0002 Moderate, n (%) 187 (23.4) 221 (50.9) 173 (47.5) 0.68 (0.48, 0.99) 0.04 Target, n (%) 343 (43.0) 83 (19.1) 43 (11.8) 1.00

Current smoker, n (%) 75 (9.4) 35 (8.12) 37 (10.2) 0.90 (0.56, 1.45) 0.67 Structural Variables Lifestyle Meeting calcium intake level of 1500 mg/day, n (%) 20 (2.5) 11 (2.5) 9 (2.5) 0.96 (0.38, 2.38) 0.92 Lactose intolerant, n (%) 35 (4.4) 26 (6.0) 9 (2.5) 2.48 (1.14, 5.39) 0.02 Chronic health conditions

None, n (%) 332 (41.6) 38 (8.8) 52 (14.3) 1.00 1, n (%) 269 (33.7) 134 (30.9) 97 (26.7) 1.09 (0.79, 1.52) 0.60 2 or more, n (%) 197 24.7) 262 (60.4) 215 (59.1) 0.94 (0.66, 1.34) 0.73

Knowledge There is no way to prevent OP, n (%) 322 (40.4) 192 (44.2) 130 (35.7) 1.43 (1.07, 1.90) 0.01 Bones can be rebuilt once they thin from OP, n (%) 285 (35.7) 173 (39.9) 112 (30.8) 1.49 (1.11, 2.00) 0.01 If a woman has OP, something as simple as lifting a bag of groceries can break a bone, n (%)

548 (68.7) 330 (76.0) 218 (59.9) 2.13 (1.57, 2.88) <0.0001

The health problems cause by OP can be life-threatening, n (%)

265 (33.2) 158 (36.4) 107 (29.4) 1.38 (1.02, 1.85) 0.04

Cues to Action Osteoporosis Risk Factors

Fall in past year, n (%) 201 (25.2) 119 (27.4) 82 (22.5) 1.29 (0.93, 1.79) 0.13 Low trauma fracture (any) since age 40, n (%) 194 (24.3) 111 (25.6) 83 (22.8) 1.17 (0.84, 1.63) 0.35 Height loss, n (%) 132 (16.5) 73 (16.8) 50 (13.7) 1.31 (0.89, 1.94) 0.17 Maternal family history of OP, n (%) 216 (27.1) 139 (32.0) 77 (21.2) 1.80 (1.30, 2.50) 0.0004 Early menopause, n (%) 193 (24.19) 100 (23.0) 93 (25.6) 0.92 (0.66, 1.28) 0.62

Osteoporosis Care Management BMD test (self-report), n (%) 467 (58.5) 320 (73.7) 147 (40.4) 4.22 (3.11, 5.72) <0.0001


Shaded variables indicate those that had p<0.25 associated with the regression coefficient in bivariate analysis and were therefore considered in the

multivariable analysis.

OP- osteoporosis 1mean value

2 standard deviation value

*Includes alendronate, risedronate, calcitonin and raloxifene.

a Cronbach’s alpha= 0.90

b Cronbach’s alpha= 0.92

c Cronbach’s alpha= 0.88

d Cronbach’s alpha= 0.90

§n=776; score with influential observations removed (n=22).

Table 9. Descriptive statistics and odds ratio estimates for bivariate analyses (continued)

N=798 % Users

(n=434) Non-users

(n=364) Bivariate (n=798)

OR 95% CI p-value

Current OP treatment None, n (%) 617 (80.1) 311 (75.9) 306 (85.0) 1.00 Etidronate, n (%) 76 (9.9) 37 (9.0) 39 (10.8) 0.98 (0.61, 1.60) 0.95 Other*, n (%) 77 (10.0) 62 (15.1) 15 (4.2) 3.70 (2.05, 6.66) <0.0001 Hormone therapy use, n (%) 69 (8.6) 44 (10.1) 25 (6.9) 1.61 (0.95, 2.70) 0.08

Discussion about Osteoporosis

Discussion about calcium for bone with physician, n (%) 515 (64.5) 361 (83.2) 154 (42.3) 7.28 (5.21, 10.18) <0.0001 Talked with pharmacist about OP in last year, n (%) 41 (5.1) 37 (8.5) 4 (1.1) 8.13 (2.86, 23.11) <0.0001 Talked with family/friend about OP in last year, n (%) 443 (55.5) 267 (61.5) 157 (43.1) 2.17 (1.62, 2.89) <0.0001

Perceived Benefits §Calcium benefits, mean (SD)

d 18.5¹ (2.6)² 19.6¹ (1.7)² 17.2¹ (2.9)² 2.09 (1.84, 2.36) <0.0001

Perceived Barriers Taking too many medications, n (%) 67 (8.4) 35 (8.1) 32 (8.8) 0.91 (0.54, 1.53) 0.73 Stomach problems, n (%) 35 (4.4) 24 (5.5) 11 (3.0) 1.93 (0.93, 4.00) 0.08


(continued)

Table 10. Multivariable odds ratio estimates for calcium supplement users and non-users

Preliminary Effects Model (n=776)

†

Final Effects Model (n=776)§

Sensitivity Analysis Model (n=776)

£

OR 95% CI p OR 95% CI p OR 95% CI p

Perceived Susceptibility OP susceptibility 1.06 (0.99, 1.13) 0.10 1.08 (1.02, 1.14) 0.01 1.08 (1.02, 1.14) 0.01

Perceived Seriousness The thought of having OP scares you 1.00 (0.60, 1.67) 1.00 It would be very costly if you got OP 0.88 (0.57, 1.36) 0.56 0.87 (0.59, 1.30) 0.50 When you think about OP you get depressed 0.79 (0.39, 1.60) 0.51

Demographics Caucasian 0.79 (0.11, 1.47) 0.17 Age (years)

65-69 1.73 (0.96, 3.12) 0.07 70-74 1.00 (1.00, 1.80) 0.99 75-79 1.41 (0.76, 2.62) 0.28 80-90 1.00

Annual household income <$30,000 1.00 $30,000-$49,999 1.47 (0.81, 2.69) 0.21 ≥$50,000 1.03 (0.44, 2.39) 0.95 Missing 0.98 (0.55, 1.75) 0.95

Highest Level of Education Grade school 1.00 High school 1.35 (0.69, 2.62) 0.38 Post-secondary 0.78 (0.48, 1.28) 0.33

Resided in Toronto 1.49 (0.97, 2.29) 0.07 1.48 (1.00, 2.20) 0.048 1.49 (1.00, 2.20) 0.048 Sociopsychological Factors Health Motivation You eat a well-balanced diet 0.74 (0.34, 1.63) 0.46 You look for new information related to your health 1.05 (0.64, 1.73) 0.84 You try to discover health problems early 0.63 (0.31, 1.26) 0.19 You follow recommendations to keep you healthy 1.02 (0.60, 1.75) 0.93 Preventive health checkups 1.19 (0.37, 3.84) 0.78 NHP use 1.65 (1.06, 2.59) 0.03 1.75 (1.14, 2.67) 0.01 1.77 (1.16, 2.70) 0.01 Level of Physical Activity

None 0.93 (0.56, 1.53) 0.77 Moderate 0.85 (0.51, 1.41) 0.53


Table 10. Multivariable odds ratio estimates for calcium supplement users and non-users

Preliminary Effects Model (n=776)

†

Final Effects Model (n=776)§

Sensitivity Analysis Model (n=776)

£

OR 95% CI p OR 95% CI p OR 95% CI p

Target 1.00 Structural Variables Knowledge There is no way to prevent OP 0.66 (0.41, 1.05) 0.08 Bones can be rebuilt once they thin from OP 1.13 (0.71, 1.80) 0.61 If a woman has OP, something as simple as lifting a bag of groceries can break a bone

1.48 (0.94, 2.33) 0.09

The health problems cause by OP can be life-threatening

0.96 (0.62, 1.47) 0.84

Lifestyle Lactose intolerant 2.61 (0.86, 7.93) 0.09

Cues to Action Osteoporosis Risk Factors

Fall in past year 1.72 (1.06, 2.77) 0.03 1.61 (1.03, 2.52) 0.04 1.61 (1.03, 2.52) 0.04 Height loss 0.84 (0.48, 1.47) 0.54 Maternal family history of OP 1.32 (0.83, 2.13) 0.24

Osteoporosis Care Management BMD test (self-report) 1.60 (0.99, 2.59) 0.06 1.68 (1.07, 2.62) 0.02 1.69 (1.08, 2.63) 0.02 Current OP treatment

None 1.00 Etidronate 0.14 (0.07, 0.28) <0.0001 0.16 (0.08, 0.31) <0.0001 0.16 (0.08, 0.31) <0.0001 Other* 0.60 (0.27, 1.35) 0.22 0.52 (0.24, 1.12) 0.10 0.52 (0.24, 1.13) 0.10

Hormone therapy use 1.14 (0.59, 2.22) 0.70 Discussion about Osteoporosis

Discussion about calcium for bone with physician 4.28 (2.62, 7.02) <0.0001 3.56 (2.28, 5.56) <0.0001 3.54 (2.27, 5.53) <0.0001 Talked with pharmacist about OP in last year 5.80 (1.53, 22.05) 0.01 4.68 (1.28, 17.17) 0.02 4.75 (1.29, 17.50) 0.02 Talked with family/friend about OP in last year 1.14 (0.74, 1.76) 0.55

Perceived Benefits Calcium benefits 2.03 (1.76, 2.35) <0.0001 1.98 (1.73, 2.27) <0.0001 1.98 (1.73, 2.27) <0.0001

Perceived Barriers Stomach problems 1.57 (0.57, 1.34) 0.39

OP- osteoporosis *Includes alendronate, risedronate, calcitonin and raloxifene.

†C-statistic=0.890, Nagelkerke’s R

2=0.5739

§C-statistic=0.878, Nagelkerke’s R

2=0.5432

£C-statistic=0.877, Nagelkerke’s R

2=0.5427


Figure 3. Study sample flowchart

Eligible study sample n=798

Calcium supplement users

n=434

Non-users n=364

n=73 past calcium

supplement users

CSOFT participants n=871


Histogram # Boxplot

25.5+* 3 |

.** 7 |

23.5+* 5 |

.** 9 |

21.5+*** 15 |

.******** 38 |

19.5+******* 33 |

.********* 45 |

17.5+******* 33 |

.****** 28 +-----+

15.5+******* 31 | |

.******* 35 | |

13.5+******** 37 | + |

.************ 58 | |

11.5+************** 70 *-----*

.*********************************************** 233 +-----+

9.5+********** 46 |

.***** 22 |

7.5+*** 11 |

.* 5 |

5.5+** 8 |

----+----+----+----+----+----+----+----+----+--

* may represent up to 5 counts

Median=11, mode=10, mean=13.0, SD=4.2, skewness= 0.7, kurtosis=-0.4

Figure 4. Histogram and box-and-whisker plot of perceived susceptibility to osteoporosis

scores (n=798)

Case Number

calcium supplement user non-user

Figure 5. Pearson residual plot of perceived susceptibility to osteoporosis scores (n=798)


Histogram # Boxplot

24.5+* 4 0

.* 1 |

.** 16 |

.**** 30 |

.********************************************** 412 +-----+

.******** 67 | |

.******* 55 +--+--+

17.5+**** 32 |

.**** 30 |

.***** 42 |

.****** 48 0

.** 17 0

.* 6 0

.* 2 *

10.5+** 10 *

----+----+----+----+----+----+----+----+----+-


Median=20, mode=20, mean=18.5, SD=2.6, skewness=-1.3, kurtosis=1.0

Figure 6. Histogram and box-and-whisker plot of perceived calcium benefits scores

(n=798)

Case Number


Figure 7. Pearson residual plot of perceived calcium benefits scores (n=798)


Histogram # Boxplot

24.5+* 4 0

.* 1 |

.** 16 |

.**** 30 |

.*********************************************** 416 +-----+

.******** 67 | |

.******* 55 +--+--+

17.5+**** 32 |

.**** 30 |

.***** 42 |

.****** 48 0

.** 17 0

.* 6 0

.* 2 *

10.5+** 10 *

----+----+----+----+----+----+----+----+----+--


Median=20, mode=20, mean=18.6, SD=2.5, skewness=-1.3, kurtosis=1.3

Figure 8. Histogram and box-and-whisker plot of perceived calcium benefits scores with

removed influential observations (n=776)

Case Number


Figure 9. Pearson residual plot of perceived calcium benefits scores with removed

influential observations (n=776)


Median=45.3, mode=54.7, mean=43.7, SD=10.8 skewness=-0.4, kurtosis=-1.0

Figure 10. Histogram and box-and-whisker plot of SF-36v2 physical functioning

composite scores (n=798)

Case Number


Figure 11. Pearson residual plot of SF-36v2 physical functioning composite scores

(n=798)


Histogram # Boxplot

71+* 1 0

.* 2 0

.* 4 |

.** 7 |

.***** 19 |

.********** 40 |

.*************************** 105 |

.********************************************* 177 +-----+

.******************************* 123 *-----*

.********************* 81 | + |

.*************** 57 +-----+

.*********** 44 |

47+********* 35 |

.******* 27 |

.**** 14 |

.****** 22 |

.*** 9 0

.*** 9 0

.** 7 0

.* 4 0

.* 2 0

.* 3 0

.

.* 3 *

23+* 1 *

----+----+----+----+----+----+----+----+----+


Median=55.3, mode=57.0, mean=53.6, SD=6.7, skewness=-1.3, kurtosis=2.4

Figure 12. Histogram and box-and-whisker plot of SF-36v2 mental functioning composite

scores (n=798)

Case Number


Figure 13. Pearson residual plot of SF-36v2 mental functioning composite scores (n=798)

Chapter 5: Discussion

70

Chapter 5: Discussion

In this final chapter of the thesis, an overview of the main thesis findings is provided

and the generalizability of results is discussed. Results are first discussed within the context

of the literature, examining factors associated with dietary calcium intake/calcium

supplement use, and then the adequacy of the HBM as a conceptual framework for this thesis

work is examined. This is followed by a discussion of recent controversies regarding calcium

supplementation, as well as a discussion of the study’s limitations and strengths. The chapter

ends with recommendations for clinical practice and future research, and overall conclusions.

5.1 Main Thesis Findings

Calcium supplements are an important OTC therapeutic option for meeting

recommended calcium intake levels and maintaining bone mass [1]. To our knowledge, our

study is the first to examine a comprehensive list of predictor variables, structured around a

health behaviour conceptual framework (i.e., the HBM), for the purpose of determining

factors associated with calcium supplement use in older community-dwelling women.

Through multivariable logistic regression modelling, we were able to simultaneously

examine the relative association of HBM factors to calcium supplement use. Our logistic

regression model had a c-statistic of 0.88, indicating that the regression model had enough

power to distinguish between calcium supplement users and non-users.

Results indicated that calcium supplement use was more likely among women who

reported greater perceived susceptibility to osteoporosis, resided in Toronto (compared to

Chapter 5: Discussion 71

Oxford County), used NHPs (other than calcium/vitamin D/multivitamins), had a fall in the

past year, had a prior BMD test, talked to a physician about the importance of calcium, talked

to a pharmacist about osteoporosis in the past year and had greater perceived benefits of

calcium. In contrast, women reporting current use of etidronate treatment were less likely to

use calcium supplements.

Our study also illustrated the low levels of dietary calcium intake among older

community-dwelling Canadian women. Surprisingly, low dietary calcium intake was not

associated with calcium supplement use. Only 8.9% of women in our study sample reported

dairy product intake that met the 2010 recommended calcium intake levels of 1200 mg/day,

while only 2.5% met the 200213

recommendation of 1500 mg/day.

5.2 Generalizability of Findings

To ascertain generalizability (external validity) of study findings, the study sample

characteristics were compared to those of the general Canadian population of older women.

Compared to the general Canadian population of older individuals, this study’s sample

included a larger proportion of women in the older age group (80 to 90 years), [89]. Our

study sample also consisted of a greater proportion of women reporting to have had a post-

secondary education (23%), compared to the general population of older Canadian women

reporting some high school education or greater (18%) [90].

When compared to data from the 1998/1999, National Population Health Survey

(NPHS), participants in this thesis study were similar to older Canadian women with regards

13

Taking into consideration that CSOFT data used in this were collected during 2003-2004, it is important to

consider whether women were meeting the recommended calcium intake levels for that time period (i.e., the

2002 recommended levels of 1500 mg/day).


to marital status (45% of this study’s participants reporting being married or in a common

law relationship compared with 45% reported from NPHS data), and smoking status (9% of

study participants reporting being current smokers compared to 13% reported by NPHS).

Participants also were similar with regards to self-reported prevalence of chronic conditions,

such as arthritis (50% reported arthritis in this study compared to 48% from NPHS

estimates), diabetes (8% vs. 10%) and lung problems (14% vs. 13%).

Fifty-four percent of women in this study sample reported using calcium

supplements. To our knowledge, the only other Canadian study, published in 2006, that has

estimated calcium supplement use in older women, has reported that 64% of healthy

postmenopausal women were users of calcium supplements [91]. However, its study sample

was recruited via convenience sampling and was younger (mean age=61.9 years) compared

to the random sample of women in this thesis (mean age=75.4 years).

Although this thesis study sample excluded women who had moderate to severe

osteoarthritis between 1995 to 1997 and those who reported using calcium supplements in

the past, we suggest that results obtained from our study sample may be generalizable to the

general Ontario population of English-speaking women 65 years and older who are healthy

enough to complete a lengthy telephone interview. We acknowledge that older women with

cognitive or hearing impairments, language barriers, or living in a long-term care residence

may be different and therefore our results are not generalizable to that population.

5.3 Comparing of Study Results to Prior Research

This section discusses the thesis findings in relation to prior research and is organized

according to the components of the HBM.


5.3.1 Perceived Susceptibility to Osteoporosis

This study identified a significant positive association between perceived

susceptibility to osteoporosis and calcium supplement use. Only one prior study identified

from the literature search for this thesis, that examined 990 randomly selected women 45

years and older, found a significant positive association (through the use of cluster analysis),

between calcium supplement use and perceived susceptibility to osteoporosis (measured

using the OHBS subscale for perceived susceptibility to osteoporosis) [50]. Another study

found no association between perceived susceptibility to osteoporosis and calcium

supplement use but examined a smaller convenience sample (n=60) of women 40 to 95 years

of age and did not use a validated measure for perceived susceptibility to osteoporosis [41].

The HBM states that greater perceived susceptibility to a disease is more likely to lead to

preventive action against the condition. Thus, the positive association found in our study is

consistent with the HBM and prior work based on a large generalizable sample and a

validated measure of perceived susceptibility to osteoporosis.

5.3.2 Perceived Seriousness of Osteoporosis

In this study sample, the perception of osteoporosis as a serious disease did not

significantly increase a woman’s likelihood of using calcium supplements. This lack of

association could be attributed to several factors. First, this finding agrees with results from

previous studies. One previous study identified no association between perceived seriousness

and calcium/vitamin D/soy supplement use among 185 women aged 20 to 64 years [51]. In

support of this finding are data from a Canadian focus group study, which found that the

majority of women do not take preventive measures against osteoporosis despite their belief


that osteoporosis is a serious disease. Second, general research examining the HBM identifies

the perceived seriousness component as the least predictive for determining health behaviour

[92], possibly explaining the lack of association to calcium supplement use found in this

study and others. However, in this thesis, the collapse of response options into a dichotomy

for the perceived seriousness to osteoporosis items could have led to a decrease in possible

association and therefore resulted in our finding that there was no significant association to

calcium supplement use. Only 1-item measuring perceived seriousness of osteoporosis

remained in the final regression model in this study: “It would be very costly if you got

osteoporosis.” Yet, the perceived seriousness component of the HBM is intended to evaluate

a combination of medical, clinical and social consequences influencing perception to

seriousness of the disease [46]. The 1-item measurement of osteoporosis seriousness used in

this study may not have captured medical and clinical consequences. Thus, although we did

not find perceived seriousness of osteoporosis to be associated with calcium supplement use,

it is the weakest predictor variable in the HBM and we acknowledge limitations of its

measurement in this thesis study. Further research may be warranted to clarify the

relationship between perceived seriousness of osteoporosis and calcium supplement use in

older community-dwelling women.

5.3.3 Personal Factors

Demographics

Analyses indicated that none of the demographic variables (age, income, level of

education and current living arrangements), with the exception of residence in Toronto, were

associated with calcium supplement use. Residence in Toronto was associated with calcium


supplement use although the significance of this association was borderline (p=0.048). An

examination of the 2001 Ontario census data showed that the only significant difference

between residents of these two regions was the proportion of residents who were immigrants:

89% of the female population of East York (borough in Toronto examined in this study) was

foreign-born [93], while only 46% of females in Oxford County were foreign-born [94]. To

our knowledge, no studies have identified an association between being foreign-born and

using calcium supplements, limiting our discussion on this factor. The association between

region of residence identified in this study, however, may also be attributed to access to care.

Indeed, inequities in access to care for fracture prevention between the two regions have been

identified [53], possibly affecting a community-dwelling women’s likelihood to use calcium

supplements.

With regards to the association between age and calcium supplement use, our results

are not consistent with a prior study of women 25 years and older, recruited from six

suburban community-based family medicine practices in Cleveland [51]. Using multivariable

regression analysis, the study suggested that older age was more common among calcium

supplement users than non-users. Discrepancy between the findings and ours could be

attributed to differences in the mean ages of the study samples (mean age=43 years for the

latter study vs. mean age=75 years for this thesis). A younger age group is more likely to

capture those individuals who are relatively healthy and taking calcium supplements in order

to meet recommended calcium intake levels, while the older age group studied in this thesis

focuses on those at risk for osteoporosis who may be using calcium supplements for the

purpose of fracture prevention, as is recommended by Canadian guidelines for osteoporosis

prevention among older women 65 years and older [1].


The HBM categorizes demographic factors as modifying characteristics that may

influence health beliefs, but are not directly linked to the health behaviour. Indeed, our

results suggest that demographic factors may have a less contributory independent effect on

calcium supplement use in older community-dwelling women, compared to health beliefs

such as perceived susceptibility to osteoporosis.

Sociopsychological Variables

In this study, none of the variables measuring health status (i.e., general perceived

health status, SF-36v2 physical functioning composite score and the SF-36v2 mental

functioning composite score) were associated with calcium supplement use. The only other

study identified through the literature search that examined this also found no significant

association between health status and calcium supplement use [51]. Data from the Canadian

NPHS also suggest that in multivariable analysis, health status is of less importance to NHP

use compared to other factors [95]. We therefore propose that health status may not be an

important contributory factor to calcium supplement use in a generally healthy group of older

community-dwelling women able to complete a lengthy telephone interview.

When explaining health behaviour related to chronic disease prevention, the HBM

proposes that an individual’s health motivation is an important factor influencing the

likelihood of action. In this study, we did not identify a significant association with general

health motivation items. Given that the majority of study participants reported strongly agree

or agree with each of the general health motivation items, these items may have poor

discriminatory power and therefore may not be good measures of general health motivation.

In addition, each item was dichotomized and the items measured general health motivation as


opposed to specific bone health motivation, possibly contributing to the lack of association

with calcium supplement use, once other factors were adjusted for in the multivariable

analysis.

However, health motivation specific to the use of NHPs (other than calcium, vitamin

D or multivitamins) was found to be positively associated with calcium supplement use. This

result was anticipated based on multivariable results from a prior study that examined the use

of multivitamins as an indicator of health motivation [51] and the understanding that women

who use other NHPs are generally more inclined to use calcium supplements [91].

Other measures of health motivation examined in this thesis included smoking status

and level of physical activity. Consistent with another study [47], smoking status was not

indicative of calcium supplement use, and once other factors were adjusted for, level of

physical activity showed no association. To our knowledge, only one other study has

examined physical activity as a possible correlate of supplement use [50]. However, this

study did not examine calcium supplement use separately and used cluster analysis, reporting

that women who perceived greater susceptibility and seriousness to osteoporosis were less

likely to exercise greater than three times per week. Our study, however, was thorough in

determining the type and level of physical activity and our physical activity variable was

coded based on three levels of activity (target, moderate, none) rather than frequency of

activity. Our coding of the physical activity variable thus allowed us to differentiate between

women who exercised for the purpose of maintaining health (i.e., target level of activity), and

those who were sedentary and possibly had physical limitations. Regardless, we identified

little association between exercise and calcium supplementation.



When other factors, such as perceived benefits to calcium were accounted for,

osteoporosis knowledge was not associated with a woman’s likelihood of using calcium

supplements. However, we acknowledge data were restricted to two items that measured

knowledge about osteoporosis prevention and two that measured knowledge about

osteoporosis consequences. These items did not tap into calcium-specific knowledge and

may not have captured osteoporosis knowledge well enough to be able to detect an

association. Research shows that women report uncertainty regarding calcium knowledge,

and confusion with understanding dosage and formulations of calcium supplements [49].

More research is needed to clarify the association between knowledge and calcium

supplementation.

In this study, lactose intolerance was the only lifestyle factor positively associated

with calcium supplement use in bivariate analyses, however, this association was non-

significant in multivariable results. A focus group study identified that lactose intolerance

impeded dietary calcium intake [49]. Thus, although lactose intolerance may not be

associated with calcium supplement use at the population-level (4.4% reported lactose

intolerance in this study), it may be an important factor of association at the individual-level.

5.3.4 Cues to Action

We considered risk factors for osteoporotic fracture and osteoporosis management as

potential cues to calcium supplementation. In the multivariable analysis of this study,

maternal family history of osteoporosis and height loss were not contributing factors to

calcium supplement use. A previous study among 185 women aged 20 to 64 years of age,


found that calcium supplement use was more prevalent among those with a family history of

osteoporosis [51]. Our finding suggests that maternal family history of osteoporosis may not

be an important factor in influencing calcium supplement use among older women (i.e., 65

years and older) as it is in younger women. This point is further supported by other research

that has found younger women to more frequently attribute their risk for osteoporosis to their

family history, compared to older women [41].

The only osteoporosis risk factor found to be indicative of calcium supplement use

was a fall in the past year. The cross-sectional data used in this thesis, however, do not allow

us to ascertain whether women started using calcium supplements after their fall or whether

they were using the supplements prior to the fall.

Osteoporosis management variables including: previous BMD testing, current

osteoporosis treatment and discussion with health care providers were associated with

calcium supplement use. The positive association found between calcium supplement use and

previous BMD testing is consistent with results from several pre-post intervention studies

that have identified a positive association between BMD testing and calcium intake [96-101].

Surprisingly, treatment with etidronate was negatively associated with calcium

supplement use, yet women on osteoporosis treatment other than etidronate therapy (i.e.,

alendronate, risedronate, calcitonin and raloxifene), were not more likely to use calcium

supplements than those on no treatment for osteoporosis. At the time of CSOFT data

collection, cyclical etidronate was available and was dispensed as 14 days of etidronic acid

(prescription drug) and 76 days of calcium supplementation. Women consuming an average

amount of dietary calcium (i.e., 700 mg/day) or higher and on cyclical etidronate (including

500 mg from calcium supplement as part of therapy) may still not have been meeting the


recommended calcium intake levels at the time of data collection (i.e., 1500 mg/day) and

therefore required additional supplementation. Today, however, given the lower

recommendations for calcium levels (i.e., 1200 mg/day), women consuming an average

amount of dietary calcium and on cyclical etidronate may be meeting the recommended

calcium intake levels. However, since the time of CSOFT data collection, other

bisphosphonates have been open listed in Ontario (i.e., alendronate and risedronate), which

do not include calcium and therefore fewer women are being treated with etidronate therapy

[76]. Women taking these other bisphosphonates still require calcium supplementation if they

are not meeting the recommended targets from diet alone. A recent study involving clinically

diagnosed osteoporotic women 65 years and older, identified the need for clearer information

regarding osteoporosis and its treatment, including information on diagnosis, implications of

diagnosis, treatment options and efficacy [102]. This indicates a potential role for health care

professionals to help improve patients’ knowledge of calcium supplementation and

osteoporosis treatment.

Finally, women who had a conversation with a physician about the importance of

calcium had greater odds of using calcium supplements. Those who had a conversation with

a pharmacist in the past year about osteoporosis were also more likely to be using calcium

supplements, although only 37 women of the study sample reported speaking with a

pharmacist, indicating less certainty in the accuracy of the odds ratio estimate (95% CI=1.4-

17.4). The cross-sectional nature of our data limits us in discerning whether or not women

talked to a pharmacist after talking with a physician about the importance of calcium.

Regardless, these results emphasize the importance of discussions with health care providers

in increasing the likelihood of calcium supplementation. Indeed, research has found that


pharmacists interventions may improve bone mineral density testing and calcium intake

among patients at high risk for osteoporosis [103].

5.3.5 Perceived Benefits and Barriers of Calcium

Our study also found that perceived calcium benefits were significantly associated

with calcium supplement use. This was consistent with results from most prior research [38,

48, 50] and is consistent with the HBM concept that perceived benefits directly affect the

likelihood to take action. However, our study was not able to determine whether perceived

barriers to calcium were associated with calcium supplement use. Our proxy items were

likely poor measures of perceived barriers to calcium because the two items were related to

medication use rather than supplement use. Previous research has reported a negative

association between perceived barriers and calcium intake [38, 48, 50] and has emphasized

the importance of the perceived barriers component of the HBM in predicting health

behaviour [92]. Therefore, we hypothesize that perceived barriers to calcium supplement use

would be an important factor related to a woman’s likelihood of using calcium supplements

and therefore should be examined in future research. Appropriate measures for perceived

barriers to calcium supplement use would include perception of the risks of calcium

supplements, including perceptions about side-effects (i.e., constipation and nausea), and

issues with swallowing based on the size of the supplement tablet.

Summary

Results of this project identified cues to action variables as the strongest correlates of

the likelihood to calcium supplement use. Health beliefs of perceived susceptibility to


osteoporosis and perceived benefits to calcium were of importance, but perceived seriousness

of osteoporosis was not associated with calcium supplement use. Lastly, most demographic

factors were not associated with calcium supplement use among this sample of women, with

only residence in a metropolitan region (i.e., Toronto) having a significant association with

calcium supplement use once all factors were adjusted for.

5.4 Using the HBM to Examine Factors Associated with Calcium Supplement Use

We considered calcium supplement use to be a health behaviour that is influenced by

underlying health beliefs, personal factors, as well as experiences, and therefore rationalized

that the HBM was useful for examining calcium supplement use in older community-

dwelling women. Our use of a conceptual framework was important to compiling a

comprehensive list of predictors possibly associated with calcium supplement use and

guiding regression modelling. Indeed, measures of major HBM components (i.e., perceived

susceptibility to osteoporosis and perceived benefits to calcium) were found to be correlates

of calcium supplement use in the final regression model, indicating the value of using the

model as a conceptual framework to identifying correlates of calcium supplement use.

Several cues to action variables were associated with a woman’s likelihood to use

calcium supplements. To date, no study has systematically studied or defined the cues to

action component of the HBM, making it difficult to determine which variables (of the

CSOFT dataset) should have been categorized as part of this component. Furthermore, the

HBM models personal factors and cues to action as each directly influencing perceived threat

to the disease, but does not illustrate a direct relationship between cues to action and personal

factors. It seems, however, that in addition to influencing perceived threat, personal factors


and cues to action may also have a synergistic relationship. For example, a cue to action such

as BMD testing has been shown to affect osteoporosis knowledge [100, 101] (a structural

variable of the personal factor component of the HBM), suggesting an association between

the cues to action component and the personal factors component of the HBM.

Our results identify that the perceived seriousness component of the HBM was of

little importance in predicting calcium supplement use among older community-dwelling

women in Ontario. This thus supports the notion that the perceived seriousness component of

the HBM is the least predictive of health behaviour [92].

Overall, use of the HBM was important to identifying a comprehensive list of factors

possibly associated with the health behaviour (calcium supplement use). However, we

acknowledge the limitations of the HBM with regards to a lack of the structured definition of

the cues to action component and the poor predictive ability of the perceived seriousness

component. We were also limited by previously collected data that did not enable us to

examine perceived barriers to calcium or self-efficacy.

5.5 Limitations and Strengths

We note several study limitations and discuss these limitations here. First, the study

was cross-sectional in design and therefore causal relationships could not be established. For

example, we cannot ascertain whether cues to action such as having talked with a health care

provider, influenced a woman’s decision to take calcium supplements, or whether women

who were already taking calcium broached the subject of osteoporosis/calcium with health

care providers.


Second, the study was largely based on self-report data collected through a telephone

interview. Possible response bias could have been introduced, with women completing the

questionnaire based on what they thought the interviewer wanted to hear.

Third, our outcome measure of calcium supplement use was based on self-report and

excluded women reporting past use of calcium supplements. Although calcium supplement

use was self-reported, data suggest that self-report of calcium supplement use via telephone

interview has high test-retest reliability [62] and there is moderate correlation (r=0.69,

95%CI=0.60-0.77) between self-reported daily intake of calcium supplements and calcium

intake level as calculated from transcriptions of supplement bottle labels [63]. However, the

CSOFT question inquiring about calcium supplement use could have been susceptible to

recall bias, since older women sampled may have forgotten that they had used calcium

supplements in the past and instead reported that they had never used calcium supplements.

Even more so, women taking calcium as part of a multivitamin might not have been captured

as current calcium supplement users. Therefore, we may have misclassified some past users

as never users and some current calcium users obtaining calcium via multivitamins as non-

users. We expect that recall bias is more likely among women who used calcium

supplements in the distant past, and therefore possibly miscoded as “never users”. However,

we also expect that women who forgot about past calcium supplement use in the distant past

would respond more similarly as never users and thus expect little impact on our model. In

addition, although some women may have adequate calcium intake via multivitamins, they

could be taking multivitamins for several reasons that are not specific to bone health and we

therefore believe that despite potential misclassification of users, our results have good

internal validity to identify correlates of current calcium supplement use in contrast to never


users. This is evidenced by the very high c-statistic calculated for the regression model (c-

statistic=0.88).

Fourth, we were restricted to data previously collected and thus could not measure

self-efficacy or perceived barriers to calcium supplementation. It is difficult to determine

what the effects of self-efficacy would have been on calcium supplement use in older

community-dwelling women, since to our knowledge no other study has examined the

association between self-efficacy and calcium supplement use specifically. However, we

hypothesize a positive association based on results from previous studies indicating a positive

association between self-efficacy and dietary or total calcium intake [38, 42]. In addition, the

CSOFT dataset only contained a measure for perceived barriers to dietary calcium intake (the

perceived barriers to calcium subscale of the OHBS) and not perceived barriers to

supplement use. As a result, two proxy items were used to measure perceived barriers to

calcium supplementation in this study. The validity of the two items is questionable because

the items were used in CSOFT to measure perceived barriers to osteoporosis prescription

drugs rather than perceived barriers to supplements. Evidence from Ontario indicates that

postmenopausal women perceive NHPs, including calcium, as different from prescription

medications [91], highlighting the limitations of the proxy items.

Lastly, our study focused on examining correlates of calcium supplement use, yet

vitamin D is another supplement used in osteoporosis management. However, given that the

majority of women who reported using calcium supplements were also taking vitamin D

(90%), we expect that the correlates associated with calcium supplement use may be similar

to correlates of vitamin D use. Verification of this would require further investigation.


Despite these limitations our study has many strengths. First, the use of a

multivariable regression model allowed us to examine the simultaneous relative associations

of different variables related to the HBM. The final logistic regression model had high

discriminatory performance (c=0.88), adding to the strength of the study. Second, this study

examined a comprehensive list of possible correlates to calcium supplement use including

several validated measures. In particular, unlike other studies, we examined several cues to

action variables, which were validated measures (e.g., previous BMD testing [74],

osteoporosis treatment [75]). Third, in addition to its comprehensive nature, our study used

the HBM to guide the selection of variables. This is of importance because the combination

of multiple factors such as knowledge, health beliefs and cues to action simultaneously

influence health behaviour [43]. Fourth, this study involved the careful selection of variables

from a dataset based on the HBM, enabling enough study power to detect significant

associations for the 32 variables that were considered for the multivariable logistic regression

model. Lastly, our study examined a large randomly selected sample of postmenopausal

women in two regions of Ontario, increasing generalizability of our results to older English-

speaking community-dwelling women in Ontario, healthy enough to complete a lengthy

telephone interview.

5.6 Recent Controversies of Calcium Supplement Use

Recent controversies have emerged regarding the adverse effects of high doses of

calcium supplements [104, 105]. First, a meta-analysis of randomized controlled trials

(RCTs) concluded that calcium supplements, without the use of vitamin D, increased the risk

of myocardial infarction (OR=1.25 95% CI=1.08-1.45, p=0.003) and raised concern that


calcium supplementation might be more harmful than beneficial [104, 106]. In contrast, a

recent 5-year RCT (n=1460) has contradicted the findings of the meta-analysis, reporting that

calcium supplementation of 1200 mg/day does not lead to significant increased

cardiovascular risk in older women (mean age=75.1 ± 2.7 years ) [105]. The authors of the

RCT argue several points as to why their results are more accurate than those of the meta-

analysis and speficially of the RCTs making up the meta-analysis. First, there was poor

concordance between self-reported events and validated events in the RCTs. Second, the

trials included in the meta-analysis did not include cardiovascular risk as a primary outcome.

Third, atherosclerotic vascular disease consists of several categories of diseases and therefore

when combined (as was done in the meta-analysis), the low frequency of events within each

of the categories of disease could have led to statistical differences between groups in the

RCTs of the meta-analysis, thereby leading to an overestimation of risk of myocardial

infarction associated with calcium supplement use. Fourth, the wide confidence intervals

around the effect size lead to concerns regarding the significance of the results. The authors

suggest that all four limitations were overcome in their RCT and therefore there is no

significant increased cardiovascular risk associated with 1200 mg/day of calcium

supplementation.

In addition, the American Society of Bone and Mineral Research has issued a

statement after publication of the meta-analysis, concluding that numerous large studies have

not shown an increased risk for cardiovascular events as a result of calcium supplementation

and further studies are required to ascertain a link between calcium supplements and

cardiovascular events because of contradictions in the available literature [107]. The Institute

of Medicine, on behalf of the Canadian and United States governments, has recently reported


that risk of harm with calcium supplements only increases when calcium intake levels

surpass 2000 mg/day [108]. Therefore, despite recent concern regarding potential harm with

high dosage of calcium supplements, the current recommended calcium intake level of 1200

mg/day is below the risk of harm level, and given that the average older women only

consumes about 700 mg/day of calcium from her diet and about 500 mg/day from calcium

supplements, it is concluded that calcium supplements are needed to help prevent bone loss

and osteoporosis fracture [1].

5.7 Recommendations for Clinical Practice

Results of this thesis highlight the association between a discussion with health care

professional and calcium supplement use among older postmenopausal community-dwelling

women. A previous cohort study reported that 90% of postmenopausal women believed that

recommendations from their physicians were an important factor in taking preventive action

against osteoporosis [41]. However, prior data also suggest that health care professionals do

not discuss osteoporosis with about 49% of women aged 40 to 69 years, with those women

having more risk factors for osteoporosis not more likely than other women to be counselled

about osteoporosis prevention [109]. Given the recent controversies regarding calcium

supplementation, physician-patient discussions about the importance of calcium for bone

health may be even more important.

This study also found that individuals who had a discussion with a pharmacist about

osteoporosis were more likely to take calcium supplements. This result coincides well with

pharmacy practice research. A recent systematic review of three RCTs has reported that

pharmacists’ interventions, including discussions about osteoporosis, risk factors,


consequences of the disease and the importance of calcium for increasing BMD, increase

calcium intake among patients at-risk for osteoporosis [103]. Women have reported that they

are misinformed [41] and confused [49] about calcium supplementation and given their

knowledge about pharmaceuticals, pharmacists can therefore help educate patients on the

importance of calcium supplementation as prophylaxis for osteoporosis or in conjunction

with other bone-building treatment.. Furthermore, non-adherence to calcium supplementation

appears to be at least partially attributable to the side effects caused by the supplements, as

well as the difficulty in taking the supplements due to tablet size [110]. Pharmacists may be

able to help with recommending other forms of calcium that may be more suitable to

patients’ needs. As pharmacists’ scope of practice continues to broaden in Ontario,

community pharmacists may be very well placed to help with osteoporosis prevention and

management.

Greater awareness regarding the importance of calcium supplements is important.

Based on current recommended calcium intake levels, patients on etidronate therapy may be

getting enough calcium from their diet and supplementation that is part of their therapy. Yet,

it is still important for health care providers to discuss the importance of calcium for bone

and recommend supplementation for those taking other types of bisphosphonates and those

with low dietary calcium intake. Communication should be tailored towards women’s

specific information needs and misconceptions regarding calcium for bone health, especially

given the recent controversies regarding calcium supplementation in the media. Even more

so, Canadian recommendations for calcium have changed from 1500 mg/day in 2002 to 1200

mg/day in 2010. This change in calcium intake recommendations can also add to the

confusion regarding calcium supplement use. The health care practitioner therefore has a role


in educating the patient about the change in recommendations and helping clarify whether

patients require calcium supplements and the level of supplementation.

5.8 Recommendations for Future Research

This thesis work raises several questions that should be examined further in future

research. First, this study identified a low proportion of calcium supplement users among a

population of women that should be regularly using calcium supplements because of low

dietary calcium intake and increased risk for osteoporosis. The current Canadian osteoporosis

guidelines recommend that individuals at risk for osteoporosis implement changes to their

lifestyle to prevent fractures, including maintaining recommended levels of calcium intake

[1]. However, the general Canadian population of women 50 years and older do not meet the

2010 daily calcium intake recommendation of 1200 mg/day, increasing their susceptibility to

osteoporotic fracture [19]. Negative media attention as a result of recent controversies

regarding calcium supplement use may further reduce calcium intake and supplement use.

Future studies can address the effect of negative media attention with regards to calcium use.

For example, a time-series analysis can be done to examine the possible changes in calcium

supplement use among older Canadian postmenopausal women using NHPS data, and

highlighting the years that Canadian osteoporosis guidelines changed recommended calcium

intake levels and publications of controversial RCT or meta-analysis data regarding calcium

supplement safety.

This study also highlights the importance of cues to action variables in influencing

health behaviour. Given that the cues to action component of the HBM has not been

systematically studied, future research should focus on structurally defining this component


and examining its influence on chronic disease prevention. For example, longitudinal studies

can be conducted in women at-risk for osteoporosis to identify if cues to actions (such as

physician visits and BMD tests) predict supplement use.

Given the importance of the knowledge variable in the HBM and the lack of calcium

supplement use in women on osteoporosis treatment, future studies should further examine

the association between knowledge and calcium supplement use specifically. To our

knowledge no prior study has done this and our knowledge measures led to inconclusive

results.

5.9 Conclusions

This study adds to knowledge regarding correlates of calcium supplement use.

Previous studies regarding health behaviour and dietary calcium intake and/or supplement

use have included small study samples that are not generalizable to older women. Our study,

however, consisted of a large random-stratified sample of community-dwelling older Ontario

women, who were demographically similar to the general population of older Ontario

women. In addition, most previous studies did not use theory driven identification or

examination of variables, possibly limiting the number of independent variables studied. The

work of this thesis was framed by the conceptual framework of the HBM, thereby providing

a rationale for the choice of predictor variables.

Greater perceived susceptibility to osteoporosis, greater perceived benefits towards

calcium, NHP use, a fall within the past year, a previous BMD test and residence in a

metropolitan region were factors associated with calcium supplement use. As well, women

who had discussed the importance of calcium for bone with a physician and discussed


osteoporosis with a pharmacist were more likely to use calcium supplements. Therefore,

health care practitioners, especially pharmacists who are experts on therapeutics and on the

front-line of patient care, are in a unique position to educate patients about the importance of

calcium and osteoporosis prevention.

Lastly, our results indicated that women were not achieving recommended calcium

intake levels through diet alone, yet most were not taking calcium supplements. Those on

cyclical etidronate therapy were less likely to take calcium supplements, highlighting the

importance and need for women to be given clear messages from their health care

professionals about the role of calcium supplementation in osteoporosis prevention and

management.

References

93

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Appendices

100

Appendices

Appendix A- Flow Chart of Literature Search Strategy

Figure A 1. Flow chart of literature search strategy

The following terms were searched in the databases: calcium, behaviour and osteoporosis. aEmbase Classic+Embase 1947 to 2011 Week 30;

bOvid Healthstar 1966 to June 2011;

cInternational Pharmaceutical Abstracts 1970 to July 2011;

dOvid MEDLINE(R) 1948 to July Week 3 2011; Ovid OLDMEDLINE(R) 1946 to 1965; Ovid MEDLINE(R) In-

Process & Other Non-Indexed Citations July 29, 2011; ePsycINFO 1806 to July Week 4 2011;

fHealth and Psychosocial Instruments 1985 to July 2011.

Articles excluded based on:

title (n= 370)

abstract (n=83)

no primary data collection (n=4)

intervention studies (n=35)

did not study older women (n=32)

no examination of calcium intake

factors (n=12)

Articles excluded after content analysis (n=17)

no examination of correlates

(i.e., descriptive) (n=9)

no examination of calcium intake and/or

supplement use specifically (n=8)

Duplicates removed (n=290)

Articles identified (n=769)

EMBASEa

(n= 399)

HealthStarb

(n= 143)

IPAc

(n= 8)

MEDLINEd

(n= 170)

PsychINFOe

(n= 48)


Health and

Psychosocial

Instrumentsf

(n= 1)



Appendices 101

Appendix B- Study Participants Reporting Calcium, Vitamin D and Multivitamin Use

In an effort to better understand the relationship between calcium supplement use and

other supplementation (i.e., vitamin D and multivitamin use), contingency tables were

created to determine the proportion of participants who reported calcium and vitamin D use

(Table B1), as well as calcium and multivitamin use (Table B2). Women reporting use of

calcium supplements also reported vitamin D use (r=0.85), as is expected since women are

advised to take calcium and vitamin D supplementation together [1]. There was more

variation in the proportion of women reporting calcium and multivitamin use.

Table B 1. 3x3 Table for calcium and vitamin D supplement use (n=871)

Vitamin D

Calcium

Never Current Past

Never 354 10 0

Current 43 389 2

Past 27 12 34

Spearman correlation= 0.85

Table B 2. 3x3 Table for calcium and multivitamin supplement use (n=871)

Multivitamin

Calcium

Never Current Past

Never 206 130 28

Current 169 247 18

Past 35 31 7

Spearman correlation=0.19

Appendices 102

Appendix C- Characteristics of Current, Never and Past Calcium Supplement Users

Women who had reported using calcium supplements in the past were compared to those reporting current use of calcium

supplements and those who reported to have never used calcium supplements (Table C1). Those reporting past use of calcium

supplements were different from current users of calcium supplements and those who had never used calcium supplements. For

example, the proportion of past users self-reporting osteoporosis diagnosis (30%) was similar to current users (30%) but different

from those who reported to have never used calcium supplements (11%). Yet, the proportion of past users who reported having a

postsecondary education (16%) was similar to those who had never used calcium supplements (18%) and different from those who

reported current use of calcium supplements (27%).

Table C 1. Comparison of current and past calcium supplement users and non-users

N=871 %

Number Missing

Current Users

(n=434)

Non-Users (Never) (n=364)

Past Users (n=73) p

n % n % n %

Perceived Susceptibility OP susceptibility, mean (SD) 13.1¹ (4.16)² 2.6¹ (0.3)² 2.4¹ (0.3)² 13.7¹ (3.9)² <0.0001 Perceived Seriousness

The thought of having OP scares you, n (%) 263 (30.2) 153 (35.3) 81 (22.3) 29 (39.7) <0.0001

If you had OP you would be crippled, n (%) 226 (26.0) 110 (25.4) 94 (25.8) 22 (30.1) 0.69

Your feelings about yourself would change if you got OP, n (%)

327 (37.5) 153 (35.3) 140 (38.5) 34 (46.6) 0.16

It would be very costly if you got OP, n (%) 559 (64.2) 271 (62.4) 242 (66.5) 46 (63.0) 0.48

When you think about OP you got depressed, n (%) 90 (10.3) 55 (12.7) 28 (2.7) 7 (9.6) 0.07

It would be very serious if you got OP, n (%) 805 (92.4) 401 (92.4) 338 (92.9) 66 (90.4) 0.77 Demographics

Caucasian 837 (96.1) 408 (94.0) 356 (97.8) 73 (100.0) 0.004

Age (years)

65-69 191 (21.9) 92 (21.2) 79 (21.7) 20 (27.4) 0.08

70-74 211 (24.2) 122 (28.1) 70 (19.2) 19 (26.0)

75-79 238 (27.3) 111 (25.6) 100 (29.7) 19 (26.0)

80-90 231 (26.5) 109 (25.1) 107 (29.4) 15 (20.6)

Annual household income

<$30,000 149 (17.1) 70 (16.1) 67 (18.4) 12 (16.4) 0.33

$30,000-$49,999 285 (32.7) 149 (34.3) 109 (30.0) 27 (37.0)

Appendices 103

Table C1. Comparison of current and past calcium supplement users and non-users (continued)

N=871 % Number Missing

Current Users (n=434)


Past Users (n=73)

p

n % n % n %

≥$50,000 89 (10.2) 46 (10.6) 32 (8.8) 11 (15.1)

Missing 348 (40.0) 169 (38.9) 156 (42.9) 23 (31.5)

Live Alone 473 (54.6) 4 242 (56.2) 190 (52.3) 41 (56.2) 0.54

Marital Status 2 0.24

Married/Common Law 397 (45.7) 209 (48.4) 152 (41.8) 36 (49.3)

Separated/Divorced 52 (6.0) 30 (6.9) 19 (5.2) 3 (4.1)

Single 62 (7.1) 32 (7.4) 27 (7.4) 3 (4.1)

Widow 358 (41.2) 161 (37.3) 166 (45.6) 31 (42.5)

Highest Level of Education 5 0.01

Grade school 190 (21.9) 92 (21.4) 85 (23.4) 13 (21.9)

High school 481 (55.5) 221 (51.4) 212 (58.4) 48 (55.5)

Post-secondary 195 (22.5) 117 (27.2) 66 (18.2) 12 (16.4)

Resided in Toronto 411 (47.2) 230 (53.0) 150 (41.2) 31 (42.5) 0.0028

Sociopsychological Factors

Health Status

Poor perceived health status (excellent/very good/good, fair/poor), n (%)

375 (43.1) 191 (44.0) 156 (42.9) 28 (38.4) 0.66

SF-36v2 Physical Functioning Composite Score, mean (SD) 43.7¹ (10.9)² 2 43.8¹ (10.9)² 43.7¹ (10.8)² 43.0¹ (11.7)² <0.0001

SF-36v2 Mental Functioning Composite Score, mean (SD) 53.6¹ (6.8)² 2 53.4¹ (6.6)² 53.8¹ (6.8)² 53.6¹ (7.3)² <0.0001

Health Motivation

You eat a well balanced diet, n (%) 769 (88.3) 387 (89.2) 311 (85.4) 71 (97.3) 0.01

You look for new information related to your health, n (%) 581 (66.7) 309 (71.2) 216 (59.3) 56 (76.7) 0.0003

Keeping healthy is very important for you, n (%) 864 (99.2) 432 (99.5) 359 (98.6) 73 (100.0) 0.21

You try to discover health problems early, n (%) 745 (85.5) 381 (87.8) 300 (82.4) 64 (87.7) 0.09

You follow recommendations to keep you healthy, n (%) 683 (78.4) 424 (97.7) 350 (96.2) 58 (79.5) 0.01

Preventive health checkups, n (%) 847 (97.2) 358 (82.5) 267 (73.4) 73 (100.0) 0.13

NHP use, n (%) 259 (29.7) 34 (7.8) 14 (3.9) 21 (28.8) <0.0001

Level of physical activity 0.0004

None, n (%) 283 (32.5) 130 (30.0) 148 (40.7) 15 (20.6)

Moderate, n (%) 205 (23.5) 221 (50.9) 173 (47.5) 18 (24.7)

Target, n (%) 383 (44.0) 83 (19.1) 43 (11.8) 40 (54.8)

Current smoker, n (%)

83 (9.5) 35 (8.12) 37 (10.2) 8 (11.0) 0.72

Appendices 104





Past Users (n=73)

p

n % n % n %


Lifestyle

Meeting calcium intake level of 1500 mg/day, n (%) 22 (2.5) 11 (2.5) 9 (2.5) 2 (2.7) 0.99

Lactose intolerant, n (%) 40 (4.6) 26 (6.0) 9 (2.5) 5 (6.9) 0.04

Chronic health conditions 14 0.58

None, n (%) 351 (41.0) 38 (8.8) 52 (14.3) 32 (44.4)

1, n (%) 288 (33.6) 134 (30.9) 97 (26.7) 19 (26.4)

2 or more, n (%) 218 (25.4) 262 (60.4) 215 (59.1) 21 (29.2)

Knowledge

There is no way to prevent osteoporosis, n (%) 346 (39.7) 192 (44.2) 130 (35.7) 24 (32.9) 0.02

Bones can be rebuilt once they thin from osteoporosis, n (%) 312 (35.8) 173 (39.9) 112 (30.8) 27 (37.0) 0.03

If a woman has osteoporosis, something as simple as lifting a bag of groceries can break a bone, n (%)

596 (68.4) 330 (76.0) 218 (59.9) 48 (65.8) <0.0001

The health problems cause by osteoporosis can be life-threatening, n (%)

297 (34.1) 158 (36.4) 107 (29.4) 32 (43.8) 0.02

Cues to Action

Osteoporosis Risk Factors

Fall in past year, n (%) 224 (25.7) 119 (27.4) 82 (22.5) 23 (31.5) 0.14

Low trauma fracture (any) since age 40, n (%) 218 (25.0) 111 (25.6) 83 (22.8) 24 (32.9) 0.18

Height loss, n (%) 148 (17.0) 73 (16.8) 50 (13.7) 16 (21.9) 0.15

Maternal family history of OP, n (%) 244 (28.0) 139 (32.0) 77 (21.2) 28 (38.4) 0.0004

Early menopause, n (%) 213 (24.5) 100 (23.0) 93 (25.6) 20 (27.4) 0.59

Osteoporosis Care Management

OP diagnosis (self-report), n (%) 189 (21.8) 4 128 (29.8) 39 (10.7) 22 (30.1) <0.0001

BMD test (self-report), n (%) 510 (58.6) 320 (73.7) 147 (40.4) 43 (58.9) <0.0001

Current OP treatment 36 <0.0001

None, n (%) 660 (79.0) 311 (75.9) 306 (85.0) 46 (68.7)

Etidronate, n (%) 94 (11.3) 37 (9.0) 39 (10.8) 18 (26.9)

Other*, n (%) 81 (9.7) 62 (15.1) 15 (4.2) 3 (4.5)

Hormone therapy use, n (%) 72 (8.3) 44 (10.1) 25 (6.9) 3 (4.1) 0.10

Discussion about Osteoporosis

Discussion of importance of calcium for bone with physician, n (%)

576 (66.1) 361 (83.2) 154 (42.3) 61 (83.6) <0.0001

Appendices 105

OP- osteoporosis 1 mean value

2 standard deviation value





Past Users (n=73)

p

n % n % n %

Talked with pharmacist about OP in last year, n (%) 48 (5.5) 37 (8.5) 4 (1.1) 7 (9.6) <0.0001

Talked with family/friend about OP in last year, n (%) 493 (56.6) 267 (61.5) 157 (43.1) 50 (68.5) <0.0001

Perceived Benefits

Calcium benefits, mean (SD) 18.5¹ (2.6)² 19.6¹ (1.7)² 17.2¹ (2.9)² 18.5¹ (2.4)² <0.0001

Perceived Barriers

Taking too many medications, n (%) 75 (8.6) 35 (8.1) 32 (8.8) 8 (11.0) 0.71

Stomach problems, n (%) 39 (4.5) 24 (5.5) 11 (3.0) 4 (5.5) 0.21

Appendices 106

106

Appendix D- CSOFT Variables Not Examined in This Study

The following data collected in CSOFT was not examined in this study (Table D1).

Table D 1. List of CSOFT variables not examined in this study Health Services Utilization Family doctor characteristics (sex, age, years seeing, office location, times visited in past year, time required to book appointment, travel option to doctor’s office, average travel time to doctor’s office) Health services use in past year (number of visits, number of hospitalizations, visits to specialist) Bone Health Management Ever heard of osteoporosis Discussion with doctor about (vitamin D, exercise, smoking) Previous BMD tests (number of tests, date of most recent tests, results of tests) Number of falls in past year Behaviour change as a result of fall (i.e., not going outside when icy)

Sites of fractures (since turning 40 years of age)

Drug benefit plan for prescription medications Treatment with other medication (tamoxifen for breast cancer, fluotic, prednisone, seizure pills, thyroid pills) Lifestyle Use arm to stand up from chair Ever had hysterectomy Height (without shoes) Current weight Problems with vision/ wear eyeglasses Last time had eyes checked Ever had dizziness and lightheadedness Ever had balance problems Osteoporosis Health Beliefs Perceived benefits to exercise subscale of OHBS Perceived barriers to exercise subscale of OHBS Perceived barriers to calcium subscale of OHBS Osteoporosis Drug Beliefs Osteoporosis drug benefits scale Osteoporosis drug barriers scale Health Services Beliefs NPHS Preference for Self-Care Scale (Prefer doctor gives choices, patients should not challenge authority of doctor, prefer doctor assume all responsibility for medical care, generally better to take care of own health than go to doctor, almost always better to go to doctor than treat yourself)

BMD- Bone mineral density

OHBS- Osteoporosis Health Belief Scale

NPHS- National Population Health Survey

Appendices 107

(continued)

Appendix E- Independent Variables in This Study

Table E 1. Independent variables examined Variable Type of Data Reliability/Validity Rationale

Perceived Susceptibility Perceived Susceptibility to OP Continuous

(OHBS- OP susceptibility domain subscale, 5 items)

Cronbach’s alpha= 0.90 [59]

Women who perceive a greater susceptibility towards OP may be more inclined to take calcium supplementation.

Perceived Seriousness Thought of having OP scares you Crippled if had OP Feelings would change in got OP Costly if got OP Get depressed when think of OP Serious if got OP

Dichotomous (yes/no)

Women who perceive osteoporosis as a serious disease may be more inclined to take calcium supplementation.

Dichotomous (yes/no) Dichotomous (yes/no) Dichotomous (yes/no) Dichotomous (yes/no) Dichotomous (yes/no)

Personal Factors Demographics Age Categorical (65-69, 70-74, 75-79 and 80-

90 years)

Demographic factors have been shown to influence health related behaviour. Specifically, Education level may influence the likelihood of calcium supplement intake. For example, women with higher levels of education may have greater access to information or have greater knowledge regarding preventive measures for OP.

Living Alone (Current Living Arrangements) Dichotomous (yes/no) Caucasian (Ethnicity) Dichotomous (yes/no) Income Categorical (<$30 000; $30 000-$49 999;

$50 000; and missing/refusal or don’t know)

Education Categorical (low: <high school, mid: at least some high school, or high: post-secondary)

Residence in Toronto Dichotomous (Toronto or Oxford County) Sociopsychological Variables

Health Status

Perceived Health Status

Categorical (excellent, very good or good, poor or fair)

Women who perceive better physical or mental health might be more inclined to carry out health behaviours and therefore SF-36v2 Physical Functioning Composite Continuous (SF-36v2) reliability= 0.92 [67]

Appendices 108

(continued)

(continued) Table E 1. Independent variables examined Variable Type of Data Reliability/Validity Rationale

Score

take calcium supplements.

SF-36v2 Mental Functioning Composite Score

Continuous (SF-36v2) reliability=0.88 [67]

Health Motivation Look for new information related to health Dichotomous (yes/no)

Women with a greater motivation towards taking care of their health through self-care and actions such as supplement use and physical activity are at an increased likelihood of taking calcium supplements.

Eat well-balanced diet Dichotomous (yes/no)

Try to discover health problems early Dichotomous (yes/no) Follow recommendations to keep healthy Dichotomous (yes/no) Preventive Health Check-ups Dichotomous (yes/no) NHP use (other than calcium/vitamin D/multivitamins)


Level of Physical Activity Categorical (none, moderate, target) Current Smoker Dichotomous (yes/no)


Lifestyle

Lactose intolerance Dichotomous (yes/no) Women who met the required dietary calcium intake levels might not be inclined to use calcium supplements.

Met 2002 recommended calcium intake level*


Comorbidities Categorical (none, 1, 2 or more)

Knowledge

No way to prevent OP Dichotomous (correct/incorrect) Women with greater knowledge about osteoporosis and its consequences may be more inclined to use take calcium supplements as a preventive behaviour.

Health problems from OP can be life threatening

Dichotomous (correct/incorrect)

If have OP, lifting bag can break bone Dichotomous (correct/incorrect) Bones can be rebuilt after thinning from OP Dichotomous (correct/incorrect)

Cues to Action Fall in past year Dichotomous (yes/no) Women with personal risk

factors for OP (i.e. past falls, fractures, early menopause, height loss) may be more aware of their risks for OP and therefore increase their likelihood to take calcium

Low trauma fracture (any) since age 40 Dichotomous (yes/no) Height loss Dichotomous (yes/no) Early menopause Dichotomous (yes/no)

Appendices 109

(continued)

(continued) Table E 1. Independent variables examined Variable Type of Data Reliability/Validity Rationale

supplements. Maternal family history of OP Dichotomous (yes/no) Women with family history of

OP may be more aware of prevention for the disease.

OP Care Management Osteoporosis diagnosis (self-reported) Dichotomous (yes/no) (kappa =0.43; 95%

CI=0.33-0.53[74]

Women with previous osteoporosis management care may be more aware of the importance of calcium supplement use.

Previous BMD test (self-reported) Dichotomous (yes/no) positive predictive value=93%, 95% CI=90.6-95.7; sensitivity=98%, 95% CI=95.9-99.1; specificity=93%, 95% CI=89.8-95.4) [74, 75]

Treated by doctor with medication for bone health

Categorical (none, etidronate, other) kappa=0.81, 95% CI=0.76-0.86 [76]

Hormone therapy use Dichotomous (yes/no)

Discussion with others about OP Discussion with doctor about importance for calcium for bones or joints

Dichotomous (yes/no) A discussion with the physician can be a cue to action that causes an individual to take calcium supplements.

Talked about OP with pharmacist Dichotomous (yes/no) Talked about OP with family/friends Dichotomous (yes/no) Perceived Benefits to Calcium Perceived benefits to calcium supplementation

Continuous (OHBS- Calcium benefits domain subscale, 5 items)

Cronbach’s alpha=0.89 [59]

A woman’s perception of the benefits of calcium may influence her decision to take or not take calcium supplements.

Perceived Barriers to Calcium Too many medications Dichotomous (yes/no) A woman’s perception of the

barriers to drug use may indicate her perceived barriers to calcium supplement use and influence her decision to use calcium supplements or not.

Stomach Problems Dichotomous (yes/no)

OP-osteoporosis *1500 mg/day

Appendices 110

Appendix F- Osteoporosis Seriousness Items

The osteoporosis seriousness subscale of the OHBS used in CSOFT had a low Cronbach’s alpha (0.66). Therefore the six

items of the subscale (Table F1) were utilized as separate items in this thesis study. Each item was re-coded as a dichotomous

variable: yes (strongly agree/agree/neutral) and no (disagree/strongly disagree). The dichotomy of the items was based on the item

response frequency.

Table F 1. Item response frequency of osteoporosis seriousness items in CSOFT

Item Item Response Frequency (%)

N Med Mean SD 1 2 3 4 5

The thought of having osteoporosis scares you. 798 2 2.37 0.82 6.77 63.91 14.91 13.91 0.50 If you had osteoporosis you would be crippled. 798 2 2.18 0.69 11.40 63.03 21.43 3.88 0.25 Your feelings about yourself would change if you got osteoporosis.

798 2 2.52 0.77 1.13 62.16 20.93 15.54 0.25

It would be very costly if you got osteoporosis. 798 3 2.82 0.72 0.13 35.59 46.49 17.42 0.38 When you think about osteoporosis you got depressed. 798 2 2.06 0.55 8.40 81.20 6.77 3.38 0.25 It would be very serious if you got osteoporosis. 798 4 3.58 0.68 0.38 7.02 29.45 60.15 3.01

All items were used as dichotomous variables.

1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree

Med=median, SD=standard deviation

Appendices 111

111

Appendix G- Frequency Distribution of Age

The distribution of age suggested that there was an overall even distribution of the

data, with slight skewness to the right (i.e., fewer participants over the age of 80 years

compared to those younger than 80 years of age) (Figure G1). Therefore, the age variable

was coded as an ordinal variable with 4 levels: 65-69, 70-74, 75-79 and 80-90 years. In

addition, the variable was coded in this manner because there is no evidence from literature

that there is an association between age and calcium supplement use.

Figure G 1. Frequency distribution of study sample’s age

AGE

Per

cent

Appendices 112

112

Appendix H- SF-36v2 Multi-Item Scales

Table H 1. Physical Functioning Score: composite score comprised of scores for each of the

SF-36v2 scales Scale Abbreviated Item

Physical Functioning Vigorous activities, such as running, lifting heavy objects, participating in

strenuous sports

Moderate activities, such as moving a table, pushing a vacuum cleaner, bowling

or playing golf

Lifting or carrying groceries

Climbing several flights of stairs

Climbing one flight of stairs

Bending, knelling, or stooping

Walking more than a mile

Walking several hundred yards

Bathing or dressing yourself

Role Physical Cut down the amount of time you spent on work or other activities

Accomplished less than you would like

Were limited in the kind of work or other activities

Had difficulty performing the work or other activities (for example, it took extra

effort)

Bodily Pain Intensity of bodily pain

Extent pain interfered with normal work

General Health Is your health: excellent, very good, good, fair, poor

I seem to get sick a little easier than other people

I am as healthy as anybody I know

I expect my health to get worse

My health is excellent

Table H 2. Mental Functioning Score: composite score comprised of scores for each of the

SF-36v2 scales Scale Abbreviated Item

Vitality Feel full of life

Have a lot of energy

Feel worn out

Feel tired

Social Functioning Extent health problems interfered with normal social activities

Frequency health problems interfered with social activities

Role Emotional Cut down the amount of time spent on work or other activities

Accomplished less than you would like

Did work or other activities less carefully than usual

Mental Health Been very nervous

Felt so down in the dumps that nothing could cheer you up

Feel calm and peaceful

Felt downhearted and depressed

Been happy

Appendices 113

Appendix I- General Health Motivation Items

The general health motivation subscale of the OHBS used in CSOFT had a low Cronbach’s alpha (0.64). Therefore five

items of the subscale (Table I1) were utilized as separate items in this thesis study. Each item was re-coded as a dichotomous

variable: yes (strongly agree/agree) and no (neutral/disagree/strongly disagree). The dichotomy was based on the item response

frequency.

Table I 1. Item response frequency of general health motivation items in CSOFT Item Item Response Frequency (%)

N Mean SD Med 1 2 3 4 5

You eat a well-balanced diet 798 3.95 0.54 4 0.0 2.0 10.4 77.6 9.9

You look for new information related to your health 798 3.55 0.73 4 0.0 12.5 21.7 64.0 1.7 Keeping healthy is important for you 798 4.17 0.40 4 0.0 0.1 0.8 81.3 17.8

You try to discover health problems early 798 3.85 0.48 4 0.0 2.6 12.0 82.6 2.8

You follow recommendations to keep you healthy 798 3.99 0.27 4 0.0 0.6 2.4 94.6 2.4

All items were used as dichotomous variables.

1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree

Med=median, SD=standard deviation

Appendices 114

114

Appendix J - Coding Sheet for CSOFT Participants’ Intake of NHPs

An open-ended question in CSOFT inquired about whether participants were using

NHPs other than calcium/vitamin D/multivitamins, OTC products and other health food store

preparations. Responses were coded by the author according to the Canadian Natural Health

Products Regulations definition of NHPs [68]. The coding sheet (Figure J1) was filled for all

CSOFT participants (n=871). For this thesis study, only NHP use was examined and

included: chondroitin, glucosamine, methylsulfonylmethane, vitamins A-C and E, iron,

magnesium, potassium and zinc [69]. NHP use was coded as a dichotomous variable: NHP

use or not.

Supplements** Herbal Remedies Yes No

Other Products: Amino Acids and Fatty Acids Yes No

Specifics:

Joint Health Vitamins and Minerals

Chondroitin Vitamin A Iron

Glucosamine Vitamin B Magnesium

Methylsulfonylmethane Vitamin C Potassium

Vitamin E Zinc

Figure J 1. Coding sheet for use of NHPs other than calcium/vitamin D/multivitamins

Appendices 115

115

Appendix K- Coding Sheet for CSOFT Participants’ Level of Physical Activity

Three open-ended questions in CSOFT inquired about participants physical activity

“How many city blocks or their equivalent do you normally walk each day?”, b) “What is your

usual pace of walking?”, and c) “Please list any sports, recreational or activities that you have

actively participated in during the past year. Please remember seasonal sports or events, and

include use of self-propelled wheelchair, walking, gardening, chores, etc.” The coding sheet

(Figure K1) was filled for all CSOFT participants (n=871). For this thesis study, a single

categorical variable was then created based on the level and type of activity: 1) none (having

no activity), moderate (meeting moderate level of activity or less in at least one type of

activity), and target (meeting target activity in at least one type of activity).

Physical Activities*

Level of Endurance Activities

Level of Strength and Balance

Activities

Level of Flexibility Activities

Target Level of Activity Target Level of Activity Target Level of Activity

Moderate Level of Activity Moderate Level of Activity Moderate Level of Activity

No Activity No Activity No Activity

Fine Motor Movement Activities: Yes No Functional Limitations: Yes No

----------------------------------------------------------------------------------------------------------------

Sports and Recreational Activities Definitions*

Endurance Activities:

Target Level of Activity: 4-7 days/week Moderate Level of Activity: 1-3 days/week No Activity 0

days/week

Strength and Balance Activities

Target Level of Activity: 2+ days/week Moderate Level of Activity: 1 day/week No Activity: 0

days/week

Flexibility Activities

Target Level of Activity: 7 days/week Moderate Level of Activity: 1-6 days/weeks No Activity: 0

days/week

Endurance Activity- Walking Coding Rules

Walking distance, time and pace was coded under endurance activity, depending on the distance, time and pace

of the walk.

Also:

-if time was greater than 30 minutes but walking was the only recreational activity reported then no activities

was selected

*Definitions for types of activities and level of activity was based on Canada’s Physical Activity Guide to

Healthy Active Living for Older Adults [70].

Figure K 1. Coding sheet for level of physical activity

Appendices 116

116

Appendix L- Chronic Health Conditions

In CSOFT, participants were asked to indicate whether or not they had certain chronic

health conditions and whether they were being treated for these conditions. Chronic health

conditions which participants were being treated for were accounted for in this thesis study

(Table L1).

Table L 1. List of chronic health conditions Chronic Health Conditions Being Treated For n %*

Arthritis 58 7.2 Lung problems (emphysema, chronic bronchitis, asthma) 112 14.0 Heart problems (angina, heart attack, heart failure) 186 23.3 Diabetes (besides during pregnancy) 65 8.1 Kidney Disease or Kidney Stones 43 5.4 Stroke or TIA (transient ischemic attack) 33 4.1 Cancer 89 11.1 Depression or other major mental illness 19 2.4 Overactive thyroid, Grave’s Disease or hyperthyroidism 120 15.0

*Based on sample size of n=798.

The comorbidities variable was coded as a categorical variable with three levels: 0, 1

or ≥2. This was done because the majority or participants had no comorbidities (42%) or 1

comorbidity (36%), but fewer had (25%) had 2 or more comorbidities. Therefore, this was

the most appropriate coding of the variable (Figure L1).

0 1.05 1.95

0

15

30

40

45

Percent

COMORBCAT

Figure L 1. Frequency distribution of chronic health conditions

0 1

Comorbidities ≥2

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Correlates of Calcium Supplement Use in Older · perceived osteoporosis susceptibility, perceived calcium benefits, natural health product use, residing in Toronto and general osteoporosis