Is depression associated with edentulism in Canadian adults?

by

Muneera Al Shamrany

A thesis submitted in conformity with the requirements

for the degree of Master of Science in Dental Public Health

Graduate Department of Dentistry

University of Toronto

© Copyrights by Muneera Al Shamrany (2008)

ii

ABSTRACT

Is depression associated with edentulism in Canadian adults?

Muneera Al Shamrany

Master of Science in Dental Public Health

Graduate Department of Dentistry

University of Toronto

2008

It has been hypothesized that depression can be both a risk factor and a consequence of

oral diseases. Tooth loss leads to discomfort, pain, and functional limitations which could

lead to disability and, subsequently, to handicap. However, the association between

depression and edentulism has not been established yet. Data from the Canadian

Community Health Survey (CCHS) Cycle 2.1 were used to examine the association

between edentulism and depression in community-dwelling Canadians 45 years of age

and older. Separate logistic regression models were developed for dentate and edentulous

groups as well. Different regression selection methods were implemented and the area

under the ROC curve was used to select models with the highest predictability. Analysis

showed that edentulism was not associated with depression. For the edentulous group,

oral/facial pain was the only oral health factor predicting depression, whereas avoiding

smiling or laughing, dry mouth, oral/facial pain predicted depression in the dentate

individuals.

iii

Dedication

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iv

Acknowledgements

I would like to express my greatest gratitude to my parents who gave me the choice to

follow my passion in life. Next, I would like to express great appreciation and admiration

for my beloved partner in life, my husband Naeem for being by my side all the time.

I’m deeply grateful and extremely thankful to my supervisor, Dr. Herenia Lawrence who

helped me, guided me and cared for me. She has always been available and exceptionally

generous with her time and adamant to resolve difficulties along the way.

I’m also very grateful to the members of my committee, Drs. Rosemary Meier and

Asbjørn Jokstad for their valuable comments and guidance.

Special thanks to the department of Community Dentistry at the Faculty of Dentistry for

giving me such nice memories.

Last, but not least, I would like to extent my thanks to my colleagues for the positive

experience as graduate student and beyond.

v

TABLE OF CONTENTS

Abstract ii

Dedication iii

Acknowledgments iv

List of tables vi

List of Figures vii

List of abbreviations viii

Chapter 1: Introduction and overview

1. Edentulism 2

1.1 Prevalence and risk factors 3

1.2 Effect of edentulism 4

2. Depressive disorders 8

2.1 Definition of depression 8

2.2 Epidemiology of depression 9

2.3 Measurement of depression 11

2.4 Depression and oral health 13

2.5 Depression and edentulism 14

3. Aim of the study 17

Sampling Processes 18

Chapter 2: Manuscript 1 21

Chapter 3: Manuscript 2 47

Chapter 4: General discussion 70

4.1 Methodology 71

4.2 Study results 74

Chapter 5: Summary and conclusion 79

References 83

Tables 94

vi

LIST OF TABLES

Table 1. Cross-national lifetime prevalence of Major Depression Disorder (MDD) 95

Table 2. Depressive symptoms and their epidemiologic correlates 96

Table 3. Item contents of the Oral Health1 (OH1) module 103

Table 4. Item contents of the Oral Health2 (OH2) module 104

Table 5. Item contents of the Depression (DPS) module 105

vii

LIST OF FIGURES

Figure 1: Relationship between edentulism and depression 16

Figure 2: Sample extraction process for this study 20

Figure 3: Theoretical framework for variable selection 42, 66

Figure 4: ROC of the logistic regression models predicting depression for the

whole study population 67

Figure 5: ROC of the logistic regression models predicting depression for the

dentate participants 67

Figure 6: ROC of the logistic regression models predicting depression for the

edentulous participants 68

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LIST OF ABBREVIATIONS

ADL Activity of Daily Living AOR Adjusted odds ratio APA American Psychiatric Association AUC Area under the curve BDI Beck’s Depression Inventory BSI Brief Symptom Inventory BW Backward selection (Regression) CCHS Canadian Community Health Survey CES-D Center for Epidemiological Studies Depression Scale CI Confidence interval CIDI Composite International Diagnostic Interview CIDI-SFMD Composite International Diagnostic Interview-Short Form for Major

Depression CIHI Canadian Institute of Health Information CV Coefficient of Variation DALYs Disability Adjusted Life Years DIS Diagnostic Interview Schedule DPS Depression module DSM Diagnostic and Statistical Manual of Mental Disorders FW-Block Forward stepwise selection Block entry (Regression) FW-Item Forward stepwise selection Item-by-item entry (Regression) HAD Hospital Anxiety and Depression Scale HRSD Hamilton Rating Scale for Depression IADL Instrumental Activities of Daily Living ICD-10 International Classification of Diseases, 10th revision NCS National Comorbidity Survey NPHS National Population Health Survey MDD Major Depression Disorder OH1 Oral Health 1 module OH2 Oral Health 2 module OHRQoL Oral Health-related Quality of Life OR Odds ratio PAL Participation and Activity Limitation PDS Temporomandibular joint pain and dysfunction PSE Present State Examination PUMF Public use microdata file RAC Restriction of Activity module ROC Receiver Operating Characteristic SCAN Schedules for Clinical Assessment in Neuropsychiatry SPSS Statistical Package for the Social Sciences WHO World Health Organization WMH-CIDI World Mental Health-Composite International Diagnostic Interview ZSDS Zung Self-Rating Depression Scale

1

CHAPTER 1:

INTRODUCTION AND OVERVIEW

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1. Edentulism

Tooth loss can affect people of all ages. Although there is a decrease in the prevalence

of edentulism over the last years, the rate of this condition still increases with age (1-4).

Prevention of tooth loss has improved tremendously in the developed world, yet studies

have shown that the mean number of teeth retained declines with aging worldwide (5).

It may seem logical that as individuals retain their natural teeth, with age they become

more vulnerable to repeated attacks of oral diseases such as dental caries and periodontal

diseases. However, the natural history of tooth loss should be clearly distinguished from

common misconceptions about “aging of the oral cavity” (6). In the past, we used to

think that tooth loss is an inevitable part of the normal aging process because of

advancing periodontal disease. In addition, we used to believe that all adults are

susceptible to severe periodontal disease and that dental caries is not a common disease in

the elderly. Nevertheless, longitudinal dental studies and laboratory investigations of

older populations have refuted those misconceptions and advanced our knowledge

regarding the natural history and prevention of oral diseases. Now, we are aware that not

all oral diseases are “aging changes”. More importantly, we learned that oral conditions

such as coronal and root caries, periodontal diseases, tooth loss, and oral mucosal

problems are “age-related changes”. These age-related changes indicate the cumulative

effect of oral diseases, stress, trauma, medications, and other health conditions over time

(6).

3

1.1. Prevalence and risk factors

Numerous studies have been conducted to examine the underlying etiology and to

predict risks of tooth loss in the adult population. For example, research from the United

States (US) showed an increase in the prevalence of edentulism with age: <1% among

adults aged 20-39 years, 4.9% among those aged 40-59 years, and 24.9% among those

aged >60 years (7). A correlation was observed between edentulism and lower family

incomes (14.6% for lower family income compared to 4.8% for high family income) and

smoking (14.4% among current smokers, 7.9% among former smokers, and 4.5% among

those who never smoked). An inverse correlation was observed between edentulism and

education: 13.5% of adults with less than a high school education, 9.1% of adults who

graduated from high school and 3.5% of adults with more than a high school education

were edentulous.

Several subgroups also have been disproportionately affected by tooth loss and

edentulism, including Black Americans, Native American Indians and Australian

Aboriginals (6). Other studies have focused on identifying risk indicators of edentulism

in community-dwelling, functionally independent older adults. Several risk indicators

were associated with tooth loss in these populations such as clinical indicators of existing

disease, history of tooth loss, smoking, race, marital status, and education (8, 9, 10).

A secondary analysis of data from the Ontario Study of the Oral Health of Older

Adults in the province of Ontario, Canada was carried out to describe the burden of oral

disorders in a population of adults of 50 years of age and older living independently in

the community (7). Approximately, 2 in 10 (24.1 %) of the participants were edentulous.

Moreover, marital status, educational status and household income were significantly

4

associated with the condition. For example, those not currently married, those with less

than high school education and those with an annual income of less than $ 20,000 had the

highest levels of edentulism.

The article "Edentulism and denture use," in the November 2005 release of Health

Reports published by Statistics Canada provides the most recent data on edentulism in

Canada (11). According to this article, about 9% of Canadians aged 15 or older reported

to the Canadian Community Health Survey in 2003 that they had no natural teeth. This

was down from 16% in 1990 (11).

1.2. Effect of edentulism

Negative impacts associated with edentulism are well-documented in the literature

(12-14). Edentulism causes a decreased ability to chew and speak, increased disability

and handicap, poor nutrition and potential harm to general health.

Physical health:

Teeth are tools for chewing a wide variety of food of different textures and nutritional

values. When teeth are lost, masticatory function declines leading to changes in dietary

intake. Evidence suggests that edentulous individuals avoid hard-to-chew foods such as

fruits, vegetables and meats and tend to choose softer, more processed foods (15).

Consequently, a typical diet of an edentulous person lacks major sources of vitamins,

minerals and proteins and is high in fat and cholesterol. Moreover, Wayler and Chauncey

examined four groups of subjects with different degrees of tooth loss and found that the

shift patterns in food selection depended on the severity of impairment (16). Other

5

researchers also documented similar findings where subjects with more teeth tended to

consume more hard-to-chew foods (17-19).

Because edentulous people alter their diet, they are at higher risk of malnutrition

compared to dentate individuals even when confounding effects of age, gender and other

socioeconomic factors were taken into account in the statistical analysis (20). Sheiham

and co-workers conducted a dental examination and collected 4-day weighted diet

diaries, blood samples and urine samples from elderly people who lived independently in

order to determine whether oral health had an impact on their consumption of essential

nutrients (20). Findings of the study showed that dentate subjects significantly consumed

higher intake of vitamin C, vitamin E, calcium, protein, fiber, non-haem iron, thiamin,

riboflavin, niacin, pantothenic acid, and intrinsic and milk sugars. In addition, the

findings suggested a dose-response effect where mean daily intake of each nutrient and

total caloric intake increased with an increase in the number of teeth retained.

There is no sufficient evidence to support the direct adverse effect of the compromised

nutritional status of edentulous individuals on their general health. However, it might be

possible to conclude that edentulous people are at greater risk for a variety of chronic

diseases such as various forms of cancer, diabetes, hypertension and heart disease due to

nutrient deficiencies (15).

Psychological health

The teeth and mouth have social, psychological and cultural significance due to their

importance in verbal and nonverbal communication (1). Research has indicated that

individuals tend to avoid social interactions due to embarrassment and/or functional

problems with perceived inadequate dentition. For example, Smith and Sheiham have

6

shown that 9% of the elderly participants in their study felt uncomfortable chewing in

front of others and 13% reported embarrassment during social interactions due to tooth

loss (21). On a more personal level, people without natural teeth avoid close relationships

because they fear rejection when the other party discovers their edentulousness (22).

Other authors have indicated that tooth loss in adults is a serious life event which requires

a more difficult readjustment period than retirement (23).

The concept of Oral Health Related Quality of Life (OHRQoL) has been used to

capture physical and psychological impacts of tooth loss since the late 1980’s. Extensive

research was conducted in the area of OHRQoL measures and, consequently, we became

able to quantify the impact of a wide variety of oral conditions in a form of score (24). A

comparison between two national samples from the United Kingdom (UK) and Australia

revealed a similar effect of tooth loss on OHRQoL (25). Findings from this study showed

that tooth loss, corrected for age, was an independent determinant of OHRQoL. A worse

effect on OHRQoL was observed with fewer than 17 natural teeth in the UK sample and

fewer than 21 teeth in the Australians. More significantly, other studies reported a dose-

response relationship between how people perceive their OHRQoL and the number of

remaining natural teeth; as the number of teeth retained decreased, people perceived their

OHRQoL more negatively (26, 27).

Others have measured psychological stress associated with oral conditions, mainly

those that cause pain, affect appearance or involve extensive tooth loss (1). Different

measures have been utilized, ranging from a single question to more standardized and

lengthy questionnaires. Findings indicated that individuals suffering from oral health

7

problems including tooth loss tend to avoid social contact, while those with persisting

pain were isolated and depressed (1).

Locker’s conceptual model of oral health explains the impact of a variety of oral

disorders including tooth loss in older adults (28). According to the model, tooth loss

leads to discomfort, pain, and functional limitations which could lead to disability and,

subsequently, to handicap. It is conceivable to hypothesize that this process could lead to

a sense of loss or feeling of depression (29).

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2. Depressive disorders

2.1. Definition of depression

The World Health Organization (WHO) defines depression as “a common mental

disorder that presents with depressed mood, loss of interest or pleasure, feelings of guilt

or low self-worth, disturbed sleep or appetite, low energy, poor concentration and

recurrent thoughts of death and suicide” (30). According to the 2001 WHO report on

mental health, depression is a common mental disorder, causing a very high level of

disease burden, and is expected to show a rising trend during the coming 20 years as

predicted from previous trends (30).

A more comprehensive definition of depression is provided by the American

Psychiatric Association (APA) in the Diagnostic and Statistical Manual Disorders (DSM)

(31-34). In all of the four DSM versions, the APA identifies three “depressive disorders”

which are: major depression, dysthymia, and depressive disorder not otherwise specified.

Major depression or Major Depressive Disorder (MDD) is defined as a depressive

episode that lasts at least two weeks and includes at least five of the following symptoms:

depressed mood, loss of interest or pleasure in all or almost all activities, significant

weight loss or gain, insomnia or hypersomnia, psychomotor agitation or retardation,

fatigue or loss of energy, feeling of worthlessness, diminished ability to think or

concentrate and recurrent thoughts of death.

In contrast, dysthymia is a chronic condition involving depressed mood for at least two

years, during which the condition has not met the criteria of major depression and should

have at least two of the following symptoms: poor appetite or overeating, insomnia or

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hypersomnia, low energy or fatigue, low self-esteem, poor concentration or difficulty in

making decisions, and feeling hopelessness (32-34).

Depressive disorder not otherwise specified includes conditions with depressive

features that do not meet the criteria for MDD or dysthymia disorder such as

premenstrual dysphonic disorder and minor depressive disorder. This category is defined

as at least two but fewer than five of the symptoms of the MDD in a given 2 week period

with the presence of either depressed mood or loss of interest or pleasure in activities and

significant impairment of functional status (33). The Canadian Psychiatric Association

adopted the Diagnostic and Statistical Manual Disorders IV-Text Revised (DSM IV-TR)

classification to define and diagnose depression and depressive symptoms (35).

Another important aspect of depression is how it manifests itself in the affected

individuals. Many have looked at the complex nature of depression and described several

subclassifications such as those depressive episodes present in various other mental

illness and subsyndromal depression (35). Others have looked at whether major

depression, minor depression or dysthymia, and subsyndromal depression symptoms vary

over time in the same patients as a symptomatic continuum of a single clinical disease

(34).

2.2. Epidemiology of depression

Today, MDD is the leading cause of disability globally and ranks fourth in the ten

leading causes of the global burden of disease. If projections are accurate, within the next

20 years, depression may become the second cause of the global disease burden. By the

year 2020, if current demographic and epidemiological trends continue, the burden of

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depression will increase to 5.7% of the total burden of disease, becoming the second

leading cause of the Disability Adjusted Life Years lost (DALYs), a measure of the time

lived with disability and the time lost due to premature mortality. Worldwide depression

will be second to ischemic heart disease for DALYs lost (36). In the developed regions,

depression will then be the highest ranking cause of burden of disease. MDD, in

particular, is highly prevalent (37). Table 1 shows lifetime prevalence of MDD in ten

countries (21).

Up to 2002, the annual prevalence of MDD in Canada was available from five

Canadian surveys (38). The first three surveys of the National Population Health Surveys

(NPHS) in 1994, 1996, and 1998 reported an annual MDD prevalence of 7.5%, 5.2%, and

6.3%, respectively in people 45 years or older (38). The Canadian Community Health

Survey (CCHS) Cycle 1.1 (2000-2001) and Cycle 1.2 (2002) reported 10.1 % and 6.4%,

respectively in the same population. In Cycle 1.1, the Composite International Diagnostic

Interview-Short Form for Major Depression (CIDI-SFMD) was used, while in Cycle 1.2

the full World Mental Health-Composite International Diagnostic Interview (WMH-

CIDI) (39), a more lengthy and detailed instrument, was implemented; hence, this may

have led to the difference in MDD prevalence between 2000 and 2002.

There is a long standing perception that depression is, naturally, associated with aging.

However, studies suggest that the current and lifetime prevalence of MDD in people aged

45 years or over is significantly lower than that at younger ages (40-42). Depression is

more common among adults with physically disabling disorders (43). The presence of

depression further increases disability among this population. Moreover, depressive

disorders among elderly people (65 years and older) can go undetected even more often

11

than in younger adults because in older adults depression is often mistakenly considered a

part of the aging process. Also, depression is significantly associated with income. In the

National Comorbidity Survey (NCS) in the US, the odds ratio for MDD was significantly

higher for those individuals earning less than $20,000 a year, and declined as income

increased (40). Marital status was found to be highly associated with depression, with

those divorced and separated having over two-fold increase in depression compared to

married and never-married individuals (40). Depression may also develop as a co-morbid

condition with a wide range of chronic conditions associated with physical illness such as

cardiovascular diseases (44, 45) and diabetes (46). As part of this thesis, the literature on

correlates of depression was searched for large community/ epidemiological studies.

Articles that were identified during the search were examined and factors associated with

depressive symptoms were summarized from four countries, including Canada, in Table

2.

2.3. Measurement of depression

A large number of standardized instruments are available for measuring depression in

research and clinical settings. Those instruments can be classified according to the mode

of administration and whether or not the instrument is specific to depression. According

to the mode of administration, there are self-administrated rating and interviewer-

administrated scales. The Hamilton Rating Scale for Depression (HRSD), which was

developed to measure the severity of depression in clinical and experimental settings (47,

48), is an example of the interviewer-rated scales. The Beck Depression Inventory (BDI)

(49), the Zung Self-Rating Depression Scale (ZSDS) (50), and the Center for

12

Epidemiological Studies Depression Scale (CES-D) (51) are the most widely used self-

rating scales. Some multidimensional or generic mental assessment scales have subsets of

items specific to depression, while other scales are entirely specific to depression.

Examples of the most frequently used multidimensional scales are the Present State

Examination (PSE) (52), the Diagnostic Interview Schedule (DIS) (53), the Composite

International Diagnostic Interview (CIDI) (54) and the Schedules for Clinical Assessment

in Neuropsychiatry (SCAN) (55). Additionally, there are several shortened versions of

the original, longer scales such as the CIDI-depression short-form (CIDI-SFMD) (56)

which was designed to provide an operationalization of the DSM-IV diagnostic criteria

for major depression. The CIDI-SFMD is sufficiently brief that it can be included in

general health surveys. It detects symptoms indicative of major depression where

identification of five such symptoms (one of which must be depressed mood or loss of

interest) indicates a high probability that the person fulfilled DSM-IV criteria for major

depression in the 12 months preceding the interview. It should be noted, however, that

the short-form does not contain all exclusion items pertaining to organic conditions that

are included in the full CIDI, and may therefore detect some sub-clinical episodes (57).

The CIDI-SFMD has been developed to reduce costs in large-scale epidemiologic

studies and surveys; on the other hand, its accuracy is likely to be less than that of the full

questionnaire. When Kessler and colleagues (1998) developed the CIDI-SFMD, they

assessed its accuracy against the original CIDI. They reported overall classification

accuracy from a low of 93% for MDD episode to a high of over 99% for generalized

anxiety disorder. However, these findings are limited because the authors validated the

short-form on the same data set used to develop it (32). It is highly recommended that a

13

newly developed or shortened questionnaire be validated in an independent data set and

not in the same data set used during the development of the questionnaire (58). Patten et

al. (2000) compared the positive and negative predictive values of the CIDI-SFMD to

those of the full CIDI on a sample of 561 of subjects (57). The short-form was found to

have excellent negative predictive ability of 97.8% and a good positive predictive ability

of 75.1%.

On the other hand, there are measurement issues inherited to depression which could

limit cross-studies comparisons. The first issue is whether a study is reporting depression

at current time (current prevalence), 12-months prevalence, 6-months prevalence, 1-

month prevalence, or sometime in the past (lifetime prevalence). The second issue is the

use of different diagnostic definitions of depression. Third, most of the studies reporting

depression prevalence have limited their research to MDD type, which represents the

most extensively studied category world wide (30).

2.4. Depression and oral health

The causal relationship between depression and deteriorated oral health has not been

established yet. Nevertheless, some studies have suggested that depression could

predispose to oral diseases through some biological alterations, seen in depression, and

behavioral alterations such as loss of motivation (4), dietary changes and smoking. On

the contrary, others have indicated that the consequences of dental diseases may interfere

with positive self-esteem and OHRQoL and therefore adversely affect psychological

wellbeing (29, 59). Some researches, however, found that there is a relationship between

depression and a range of oral conditions. Dry mouth, temporomandibular joint pain and

14

dysfunction syndrome (PDS), burning mouth syndrome, and recurrent aphthous

stomatitis have been linked to depression (60-62).

The relationship between periodontal disease, a reason for tooth loss, and depression

was investigated in a number of observational studies with contradicting results. For

example, Monteiro da Silva et al. (1996) and Elter et al. (2002) have reported significant

association between depression and periodontal disease (63, 64). Examples of studies that

found no relationship between depression and periodontal disease are the investigations

conducted by Solis et al. (2004) on a sample of 153 adults, and Persson et al. (2003) on

701 older subjects (65, 66).

2.5. Depression and edentulism

Few studies have investigated the relationship between tooth loss and depression.

Drake et al. (1995) assessed the predictors of tooth loss over a three-year period among

Black and White older adults in North Carolina (9). Although tooth loss was not

associated with depression in Whites, it was significantly related with having fewer

depressive symptoms in Blacks. In another study, Hayashi et al. (2001) assessed the

association between tooth loss and a group of psychological factors, including

depression, among Japanese male factory workers and found no significant association

between depression and tooth loss (67). Anttila et al. investigated the relationship

between depression and edentulousness in a 55-year-old population in a Finnish town,

where 780 adults were included (68). They found that depressive symptoms were not

associated with edentulousness except among non-smoker men. Also, they reported that

dentate women with high rates of depressive symptoms had a more negative attitude

15

towards preserving their natural teeth, used sugary products more frequently, reported a

longer time since their last dental visit, and tended to have a lower percentage of filled

tooth surfaces than the non-depressed dentate women.

Common epidemiologic correlates

Edentulism and depression share many similar correlates such as being female, having

lower income, being divorced, widowed or separated and smoking. Although age was

also related to both conditions, evidence indicates that there is an opposite trend. While

depression is more common in younger adults, edentulism incidence increases with age

leading to higher prevalence in the elderly. As stated earlier, edentulism is correlated with

lower education; depression is related to chronic diseases leading to physical disability.

Therefore, we hypothesized a conceptual relationship between depression and

edentulism, as outlined in Figure 1 below.

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Figure 1: Relationship between edentulism and depression

Edentulism Depression

Low Education

Low Income

Marital Status (being divorced,

widowed or separated)

Gender (being female)

Chronic disease

Physical Disability

OHRQoL

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3. Aim of the study

Edentulism is a chronic condition known to cause physical as well as psychological

disability. Without proper oral health rehabilitation, the edentulous individual would not

have normal performance of eating, speaking, and smiling (69). Depression is known to

be related to chronic disabling conditions. Research into this topic is important especially

in the new era of holistic approach to health which is advocated as a requirement for

successful outcome in patient care.

The aim of this study is to examine the association between edentulism and depression

in individuals 45 years old or over who live independently in the community.

Specific Objectives

• Use the CCHS Cycle 2.1 data to assess the association between edentulism and

depression in participants who were 45 years or older.

• Assess the relationship of interest, controlling for known confounders or factors

associated with depression and/or edentulism in this age cohort (Figure 1).

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Sampling processes

Since the sampling process of the CCHS was complex, it might be appropriate to

discuss the sample design of the original survey. The target population of the CCHS

Cycle 2.1 was household Canadians aged 12 years or older who are living in private

dwellings in the ten provinces and the three territories. Persons living on Indian Reserves

or Crown lands, clientele of institutions, full-time members of the Canadian Forces and

residents of certain remote regions were excluded from this survey. The CCHS covered

approximately 98% of the Canadian population aged 12 or older (70).

Sample allocation

Each province was divided into health regions (HR) and each territory was designated

as a single HR. Cycle 2.1 of the CCHS collected data in 123 HRs in the ten provinces, in

addition to one HR per territory, totaling 126 HRs. To provide reliable estimates for these

126 HRs, and given the budget allocated, a sample of 130,700 respondents was desired.

Although producing reliable estimates at the HR level was a primary objective, the

quality of the estimates for certain key characteristics at the provincial level was also

deemed important. Therefore, the sample allocation strategy, consisting of three steps,

gave relatively equal importance to the HRs and the provinces. In the first two steps, the

sample was allocated among the provinces according to their respective populations and

the number of HRs they contained. In the third step, each province’s sample was

allocated among its HRs proportionally to the square root of the estimated population in

each HR. This three-step approach guaranteed each HR sufficient sample with minimal

disturbance to the provincial allocation of sample sizes. The sample sizes were enlarged

before data collection to take into account out-of-scope and vacant dwellings and

19

anticipated non-response (70).

Sampling frames and household Sampling Strategies

The CCHS used three sampling frames to select the sample of households:

• Area frame: 48% of the sample of households came from this frame

• Telephone numbers frame: this frame contributed to 50% of the sample of

households

• Random Digit Dialling (RDD) sampling frame: was used for the remaining

2% of the sample

The rule for selecting persons from households was defined as a function of the

household composition by assigning varying probabilities of selection to individuals.

Cycle 2.1 of the CCHS included “Common” and “Optional” modules. Common

modules were the mandatory questionnaires where every participant responded to. On the

other hand, Optional modules were discretionary questionnaires planned for sub-sample

content, also available for selection by health region as optional content. In the present

study, we were primarily interested in the Depression module (DPS) and the Oral Health

2 module (OH2) to ensure measurement of the outcome and the main predictor (70).

We accessed the CCHS data electronically through the data centre of the Computing

in the Humanities and Social Sciences (CHASS). The CHASS provides support to a

number of institutes, centers, and other units within the University of Toronto. The

CHASS electronic data centre is also used by over 25 subscribing universities from

Canada and the United States. In the present study, we selectively obtained a sample from

the original sample by using the automated filtering options available on the webpage of

20

the CHASS which included removal of participants who were less than 45 years of age

and did not respond to the DPS and the OH2 questionnaires (Figure 2).

Original sample of CCHS

Cycle 2.1

Filter by age &

questionnaires completed

Sample of this study (7.5% of the original sample)

Figure 2: Sample extraction process for this study

134, 072 participants ≥ 12 yrs Responded to variety of

questionnaires (common and /or optional)

10,036 participants) ≥ 45 yrs Must have responded to

DPS & OH2 with other questionnaires

21

CHAPTER 2:

MANUSCRIPT 1

A submission to Community Dentistry and Oral Epidemiology

22

“Is depression associated with edentulism in Canadian adults?”

Muneera Al Shamrany, BDS, MSc (Dental Sciences),1 Herenia P Lawrence, DDS, MSc, PhD,2

Rosemary Meier, MB, ChB, MSc, FRCPsych, FRCPC,3 and Asbjørn Jokstad, DDS, PhD4

1MSc candidate (Dental Public Health program). Faculty of Dentistry, University of

Toronto.

2Associate Professor. Department of Biological and Diagnostic Sciences, Discipline of

Community Dentistry. Faculty of Dentistry, University of Toronto. 124 Edward Street,

Rm 515D. Toronto, Ontario, Canada. M5G 1G6

Phone: 416-979-4908 ext. 4492, Fax: 416-979-4936

E-mail: herenia.lawrence@utoronto.ca

3Assistant Professor. Department of Psychiatry & Department of Public Health Sciences.

Faculty of Medicine , University of Toronto. Saint Joseph’s Health Centre, Toronto.

4Professor and Head. Department of Clinical Sciences, Discipline of Prosthodontics.

Faculty of Dentistry, University of Toronto.

Contact HP Lawrence for reprint requests

Muneera Al Shamrany was sponsored by the Ministry of Higher Education, Saudi Arabia

to conduct this study and fulfill one of the requirements for the degree of Master of

Sciences in Dental Public Health.

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Abstract – Background: Negative impacts associated with edentulism, such as a

decreased ability to chew and speak, discomfort and pain, disability and handicap, poor

nutrition and potential harm to general health are well documented. Some studies

indicated that this process could lead to a sense of loss or feeling of depression. However,

the association between depression and deteriorated oral health has not been established

yet. It has been hypothesized that depression can be both a risk factor and a consequence

of oral diseases. Objective: To examine the association between edentulism and

depression in community-dwelling adults aged 45 years old and over. Methods: This was

a secondary data analysis of cross-sectional data from the Canadian Community Health

Survey (CCHS) Cycle 2.1. The strength of the relationship between edentulism and

depression was measured using ORs (95% CI), and adjusted for potential confounders.

Separate models were also developed for dentate and edentulous groups. Results: In this

large sample of the Canadian population, 14.4% (1450/10036) were completely

edentulous. The prevalence of ‘90% probability’ of depression, as measured by the CIDI-

SFMD scale, was 5% (503/10036). Conclusions: Borderline association (p=0.056)

between complete edentulism and depression showed that people who are completely

edentulous are less likely to be depressed. This association became non-significant once

the effects of potential confounders were controlled for in the multivariate analysis. For

the edentulous group, oral/facial pain was the only oral health factor predicting

depression, whereas avoiding smiling or laughing, dry mouth, oral/facial pain among

other factors predicted depression in the dentate individuals.

24

Background

Despite the fact that tooth loss prevention has improved tremendously in the

developed world, studies have shown that the mean number of teeth retained declines

with aging worldwide (1-4). For example, research from the United States (US) showed

an increase in prevalence of edentulism with age: <1% among adults aged 20-39 years,

4.9% among those aged 40-59 years, and 24.9% among those aged >60 years (5). In

Canada, about 9% of Canadians aged 15 or older who participated in the Canadian

Community Health Survey in 2003 reported that they had no natural teeth. This was

down from 16% in 1990 (6). Negative physical impacts associated with edentulism, such

as a decreased ability to chew and speak, increased disability and handicap, poor nutrition

and potential harm to general health are well-documented in the literature. Furthermore,

edentulism is associated with decreased psychological and social functioning. Therefore,

it is conceivable to hypothesize that edentulism could lead to a sense of loss or feeling of

depression (7).

The World Health Organization (WHO) defines depression as “a common mental

disorder that presents with depressed mood, loss of interest or pleasure, feelings of guilt

or low self-worth, disturbed sleep or appetite, low energy, poor concentration and

recurrent thoughts of death and suicide” (8). At present, depression is the leading cause of

disability globally and ranks fourth in the ten leading causes of the global burden of

disease. If projections are accurate, within the next 20 years, depression may become the

second cause of the global disease burden (8). The Canadian Community Health Survey

(CCHS) Cycle 1.1 (2000-2001) and Cycle 1.2 (2002) reported prevalence of depression

in 10.1% and 6.4% of the population, respectively (9). Depression is more common

25

among adults with chronic conditions that lead to physical illness such as cardiovascular

diseases, asthma and diabetes (10). Edentulism is a chronic condition, and unless the

edentulous individual receives proper oral health rehabilitation, eating, speaking, and

smiling would not be performed normally (11).

The causal relationship between depression and deteriorated oral health has not been

established yet. Some studies suggested that depression could predispose to oral diseases

through some biological alterations, seen in depression, and behavioral alterations such as

loss of motivation (4), dietary changes and smoking. On the contrary, others have

indicated that the consequences of dental diseases may interfere with positive self-esteem

and oral-health-related quality of life (OHRQoL) and therefore adversely affect

psychological wellbeing (7, 12). Some researches, however, found that there is a

relationship between depression and a range of oral conditions. Dry mouth,

temporomandibular joint pain and dysfunction syndrome, burning mouth syndrome, and

recurrent aphthous stomatitis have been linked to depression (13-15). The relationship

between depression and edentulism, on the other hand, has been less extensively

investigated. For example, Drake et al. (1995) assessed the predictors of tooth loss over a

three-year period among Black and White older adults in North Carolina (16). Although

tooth loss was not associated with depression in Whites, it was significantly related to

having fewer depressive symptoms in Blacks. In another study, Hayashi et al. (2001)

assessed the association between tooth loss and a group of psychological factors

including depression among male Japanese factory workers and found no significant

association between depression and tooth loss (17). Anttila et al. (2001) investigated the

relationship between depression and edentulousness in a 55-year-old population in a

26

Finnish town, where 780 adults were included (18). It was found that depressive

symptoms were not associated with edentulousness except among non-smoker males.

Also, it was reported that dentate women with high rates of depressive symptoms had a

more negative attitude towards preserving their natural teeth, used sugary products more

frequently, reported a longer time since their last dental visit, and tended to have a lower

percentage of filled tooth surfaces than the non-depressed dentate women.

Research into the relationship between tooth loss and depressive disorders is very

important especially in this new era of a holistic approach to health which is advocated as

a requirement for successful outcome in patient care. The aim of this study was to

examine the potential association between edentulism and depression in adults who were

living independently in the community. Specifically, the CCHS Cycle 2.1 data were used

to assess the association between edentulism and depression in participants who were 45

years and older.

Material and methods

Selection and description of participants

This study was a secondary data analysis of cross-sectional data from the Canadian

Community Health Survey (CCHS) Cycle 2.1. Participants in the current investigation

were individuals who were 45 years of age and older who participated in the CCHS

Cycle 2.1, particularly those who responded to the Oral Health 2 (OH2) and Depression

(DPS) modules in addition to other selected modules. The CCHS Cycle 2.1 was

conducted by Statistics Canada, in partnership with Health Canada, the Canadian Institute

of Health Information (CIHI), and provincial and territorial health ministries. Health

27

information for 133 health regions was collected on household-dwelling Canadians 12

years old and older. Data collection started in January 2003 and was completed in

December 2003 through personal and telephone interviews, using computer-assisted

interviewing software (19).

Measurement of dentate status

The oral health component was measured using the Oral Health 1 (OH1) and Oral

Health 2 (OH2) questionnaires. The OH1 questionnaire assesses self-perceived health of

mouth and teeth, ability to chew different foods, and frequency of pain in teeth and gums

(in the past month). The OH2 questionnaire is a more comprehensive assessment of oral

health status; it is longer and more detailed than the OH1. Specifically, edentulism is

defined as having no remaining natural teeth (complete edentulism) and is measured in

the OH2 Questionnaire by the ‘oh2c_20’ item: Has one/more of own teeth. We identified

individuals who chose the response 2 =NO as edentulous.

Measurement of depressive disorder

In the CCHS Cycle 2.1, the items used to measure depression were based on the work

of Kessler and Mroczek (20). They selected a subset of items from the Composite

International Diagnostic Interview (CIDI) (21) to develop the Short-Form for Major

Depression (CIDI-SFMD) which measures the occurrence of depressive symptoms in the

same 2-week period (in the same individuals) during the preceding years. Each

participant was asked to identify the frequency of feeling sad, blue or depressed or loss of

interest that lasted for a period of two weeks in the past 12 months before the interview.

Next, participants were asked about the frequency of feeling tired, weight gain or loss,

trouble falling asleep, trouble concentrating, feelings of worthlessness, thoughts about

28

death during the worst two-week period in the past year. Identification of five symptoms

(one of which was either a depressed mood or loss of interest) indicated a high

probability that the person fulfilled the criteria for Major Depressive Disorder (MDD) in

the 12 months preceding the interview.

Hence, in the present study depressive disorder was defined according to the

Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV).

Specifically, the item ‘dpscdpp’ in the DPS modules: Probability of Caseness to

Respondents calculates the probability, expressed as a proportion from 0 to 1, that the

respondent would have been diagnosed as having experienced MDD in the past 12

months, if they had completed the original Composite International Diagnostic Interview

(CIDI). We selected 0.9 as a cut-off value for this item indicating a 90% probability of

Caseness that the respondent would have been diagnosed as having experienced a major

depressive episode in the past 12 months. From this point further, we will refer to the

participants who had 90% probability of depression as “depressed”, “had depression” or

“had depressive disorder”.

Other covariates

Data obtained from other modules included age, gender, marital status, level of

education, income, chronic conditions, and restriction of activities. Chronic conditions

included a variety of illnesses from allergies to heart diseases, defined in the original

survey, and subsequently in this study, as conditions lasting 6 months or more. The

interviewers specified to participants that the questions were inquiring about “long-term

conditions” which has been expected to last or had already lasted 6 months or more and

should have been diagnosed by a health professional. Responses to each question were

29

not verified by the medical history. The Restriction of Activity module (RAC) used in the

original interview consisted of the Instrumental Activities of Daily Living (IADL) and

the Participation and Activity Limitation (PAL) global rating among other questions.

Statistical analyses

To address the research question, the ‘oh2c_20’ variable – “Has one/more of own”

teeth in the OH2 module was used as the main explanatory variable, while the variable

‘dpscdpp’ – “Probability of Caseness” to respondents in the DPS module was considered

the outcome measure. Bivariate analyses were conducted by performing cross-tabulations

to analyze the variation in the distributions of the variables. Statistical tests at this level

included the Chi-squared test, Fisher’s exact test and estimates of the risks (odds ratio

and 95% confidence intervals). At the multivariate level, the association between

depressive disorder and dentate status was examined controlling for confounders such as

dental and socio-demographic characteristics, dental visit patterns, chewing ability,

(removable) denture wearing, chronic conditions, and physical function. We combined

statistical and expert approaches in conducting the multivariate analyses to ensure

inclusion of factors found statistically significant in bivariate analyses as well as those

found very well documented in the literature, even though they were not statistically

significant at the bivariate level. A hierarchical regression approach was employed to

control the order of variables entry. Figure 3 shows the order in which variables were

entered into the model.

Different selection procedures were used including: 1) forward stepwise selection

where models were built by adding items in blocks (FW-Block), 2) item-by-item forward

stepwise selection (FW-Item) and 3) backward stepwise selection (BW). First, those

30

methods were applied to the entire study population which included both dentate and

edentulous individuals to examine the association between depression and edentulism.

Because some variables were specific to the dentate cases such as experiencing

toothache, bleeding gums and frequency of tooth brushing, models were also constructed

for dentate and edentulous groups separately (using the 3 methods of variables selection)

to improve the fit of each model. We anticipated that the final models would be different

in terms of number and what variables they included; thus the Receiver Operating

Characteristic (ROC) curve was employed to compare the predictive abilities of the

models for each group. Models with the greater area under the curve were selected as

final models.

Since a complex sampling process had been used in the original CCHS Cycle 2.1

where every participant was assigned a probability value to be included in the survey,

coefficients of variation (CV) approximation tables were obtained from Statistics Canada

and were used in our study to estimate the precise 95% CI around the Adjusted Odds

Ratios (AOR). This method was necessary to avoid underestimating the variance. Data

analyses were carried out using SPSS, Version 15.0.

Results

Out of 134,072 participants in the CCHS original survey, a sample size of 10,036

(7.49%) was extracted through the inclusion process. This sample consisted of 43.1%

males and 31% were aged 45 to 54 years (Table 1). Most of the participants, 61.9%, were

married or had a common-law partner. Also, 53% had more than secondary school

education and only 9.2% had low income. While the prevalence of edentulism was

31

14.4%, the 1-year prevalence of depressive disorder was 5%. A high proportion of

participants (85.1%) reported that they had one or more chronic condition and 46%

experienced limitations in participation and activity ‘sometimes’ or ‘often’.

Only 58 individuals were identified as being both edentulous and having a depressive

disorder; they constituted 11.5% of all depressed individuals and only 4% of the

edentulous populations (Table 2). Interestingly, while the prevalence of edentulism

increased consistently with age, the prevalence of depressive disorder showed an opposite

trend. Seventy percent of the edentulous population reported having secondary school

grade education or less. In addition, the proportion of individuals who suffered one or

more chronic conditions was very large in the edentulous and the depressed populations,

91.7% and 96.6%, respectively. Females were nearly as twice as likely to be depressed

compared to males. The OR of being socially limited such as having difficulty making

new friends, dealing with unknown people, and starting and/ or maintaining

conversations among the edentate individuals ranged from 1.57 to 1.86. Similarly, the

likelihood of social limitation among the depressed group was extremely high (OR=

8.32-10.10). Table 2 summarizes demographic, socioeconomic, and other characteristics

by the main predictor and outcome.

Some oral symptoms were negatively associated with being edentulous such as

experiencing bleeding gums, bad breath, and oral or facial pain (Table 3). On the

contrary, dentates were 1.21 more times more likely to report dry mouth compared to

edentates. It is important, however, to mention that only 67 (4.6%) individuals in the

edentulous population of this study reported that they did not wear dentures (bare gums).

More importantly, almost one-third of the dentate population (32.7%) in this study

32

reported wearing dentures. Still the OR of wearing dentures among the edentulous

participants was as high as 42.41. Oral functional disability was reported more frequently

by the edentulous individuals compared to the dentates. On the other hand, there were no

statistically significant differences between the two groups concerning the avoidance of

smiling or laughing and conversation; and whether the individuals felt that they were

socially limited due to their oral health conditions. The frequency of dental visits showed

an opposite trend for the two groups of dentate and edentulous participants. Among the

edentulous study population, 84.3% reported that they visited the dentist on an

emergency basis only. Moreover, when we combined the proportion of edentulous

individuals who reported visiting a dentist more than once a year and those who visited a

dentist only once a year, the combined proportion is still very small (11%). Among the

dentate population the largest proportion (47.1%) reported visiting their dentists more

than once a year; still, 17.8% of the dentates reported visiting the dentist on an

emergency basis only.

Individuals who were identified as depressed were more likely to have dental

insurance and less likely to wear dentures (Table 3). However, they were more likely to

experience oral health symptoms and have oral functional disability compared to the non-

depressed participants. In terms of oral functions, depressed individuals were more likely

to experience inability to chew when compared to non-depressed participants. More

significantly, dry mouth and orofacial pain were 3 times more likely to be reported by

depressed adults compared to the non-depressed. Regarding negative social impact, the

ORs ranged from 2.80 to 4.61 among the depressed individuals.

33

Table 4 presents the final models predicting depression for the study population,

dentate participants, and edentulous participants. The model for the study population

showed that the relationship between depression and dentate status was not significant.

However, this model included other oral health variables such as bleeding gums, dry

mouth, orofacial pain and avoidance of laughing/smiling. The model predicting

depression in edentulous participants had nearly half (8 variables) the number of

variables retained in the model developed for the dentate participants (14 variables). Age,

orofacial pain, and back pain were retained in the three models in Table 4. The

interaction term of age and edentulism was not associated with depression (p = 0.847).

The models displayed in Table 4 were selected as final models because they had the

highest AUC compared to the rest of the models, not presented in this paper. The models

predicting depression for the entire study sample had very similar AUC: FW-Block

model =0.812 (95% CI = 0.791-0.832), FW-Item model =0.817 (95% CI = 0.797-0.837),

and BW model =0.816 (95% CI = 0.796-0.836).

The AUC for the models for the edentulous individuals were: FW-Block

model=0.859 (95% CI = 0.804-0.914), FW-Item model =0.850 (95% CI = 0.795-0.907),

and BW model =0.863 (95% CI = 0.806-0.921).

Finally, the AUC values for the dentate models were: FW-Block model=0.813 (95%

CI = 0.793-0.833), FW-Item model =0.827 (95% CI = 0.792-0.832), and BW model

=0.809 (95% CI = 0.788-0.829).

In general, the predictability for all models ranged from a low of AUC= 0.809 to as

high as 0.863 which is considered as excellent predictability.

34

Discussion

At the bivariate level of analysis, borderline association (p=0.056) existed between

dentate status and depression. The association showed that people who are completely

edentulous are less likely to be depressed. But, this borderline association became non-

significant once the joint effect of potential confounders was controlled for in the

multivariate analysis. Results from the bivariate analysis were consistent with those of

Drake’s et al. (1995) study where tooth loss was significantly related to having fewer

depressive symptoms in Blacks (16). In our study, the final logistic regression model

indicated that in adults aged 45 years or older, dentate status is not associated with being

depressed, which confirmed the general findings of all previous studies (16-18).

Although smoking was not related to depression or edentulism at the bivariate level,

we included it in the multivariate modeling because there is evidence supporting the

relationship among smoking, tooth loss, and depression (18). Also, experts advise against

using the bivariate associations as a method of screening risk factors for use in

multivariate analysis because important predictors may be excluded if their effect is

masked by others (22). Nevertheless, smoking was not retained in any of the models

indicating that it was not an important correlate of depression in our sample. This finding

contradicts that reported by Ohayon et al. (1999) which showed a dose-response

relationship between cigarette consumption and depression where the risk of depression

increased with an increase in the number of cigarettes consumed (23).

Although edentulism was not a significant predictor of depression, the final model for

the entire study sample included oral health-related predictors that were confirmed to be

related to depression by previous evidence. The model explains that not only are chronic

35

conditions such as migraine headaches, fibromyalgia, and back problems associated with

depression, but also oral health problems such as avoidance of smiling or laughing, dry

mouth and oral/facial pain are significant predictors of depression.

One important observation is the difference between the final model for the dentate

and that for the edentulous groups in terms of the number and type of variables retained.

Initially, we resorted to the apparent difference in age between dentate and edentulous

populations, as shown in Table 2, to explain the difference between the models for each

group. The bivariate analysis indicated that as age increased, edentulism prevalence

increased and depression prevalence decreased steadily. Yet, the interaction term between

age, edentulism and depression was not significant. This led us to become more inclined

to believe that having teeth is the only explanation for more dental-related predictors in

the dentate regression model. Specifically, depression was associated with symptoms

related to “having teeth” in the dentate population.

The prevalence of edentulism and depression in this selected study sample was 14.4%

and 5%, respectively. It is important to be extremely cautious when citing these statistics

as estimates for edentulism and depression in Canadian adults 45 years or older. The

estimates we reported are restricted to the sample we selected through specific selection

criteria and, therefore, not absolutely applicable to this age group in Canada.

Nevertheless, the estimates are in agreement with the previously reported national

statistics. About 9% of Canadians aged 15 or older reported to the Canadian Community

Health Survey in 2003 that they had no natural teeth. This was down from 16% in 1990

(24). Also, the annual prevalence of MDD was 4.8% as reported in the CCHS 1.2 (25).

36

The main limitation of this study is the weak discriminative ability of the question

assessing dentate status because it classifies people with as few as one remaining tooth as

dentate. Also, the original survey did not account for implant supported dentures versus

conventional dentures. On the other hand, the large sample size allowed for a large

number of potential confounders to be controlled for in the analysis. In addition, choosing

strict cut-off point for depression at 90% probability increased the accuracy of identifying

cases of depression (26).

Depression should be one of the conditions included in the medical history taking

prior to dental treatment. One study has shown that self-reported depression was second

only to hypertension in frequency of all diseases and disorders recorded in the medical

history (27). We highly encourage dental professionals to be aware of what oral health

factors are associated with depression in order to identify those at high risk. Evidence

indicates that having dental symptoms such as dry mouth, pain and cosmetic concerns

that interfere with social activities are linked with depression. Dental factors along with

other factors known to increase the probability of depression should be kept in mind and

used as a screening tool to identify potentially depressed patients visiting the dental

office. Having said so, it is important to be always cautious when extrapolating clinical

data, in this instance clinical correlates of depression, from community surveys due to the

methodological limitations inherited to such surveys.

Nonetheless, dental professionals are encouraged to refer potential patients for

psychiatric care and utilize already available community and local resources targeted to

prevent and treat depression. This approach has a significant implication in dentistry

given that depressed patients require special management because of the negative

37

behavioral and biological impacts of depression (28). Further, the outcomes of successful

dental interventions for edentulism can be underestimated by the depressed patients

leading to dissatisfaction of those patients (29). An inter-disciplinary approach is an

important determinant of successful, long-term dental intervention outcomes.

As a final note, we found that the lack of association between depression and

edentulism is a counter-intuitive. However, this finding was consistent with what has

been previously reported. We suspect that depression could be more related to the

incidence of edentulism rather than the prevalence. In another word, we hypothesized that

depression could manifest with rapid onset of edentulism where the individual does not

have enough time to adapt to the condition. The previous studies that investigated the

relationship between depression and edentulism did not control for the onset of

edentulism. Unfortunately, the CCHS surveys do not contain items that allow us to

assess this hypothesis as well.

Acknowledgements

The authors especially wish to thank: Laine Ruus, University of Toronto, Data

Library Service; Andrew Turvey, Statistics Canada / Statistique Canada, Central

Region / Région du Centre; and Chee Wong, Public Health Branch, Ministry of

Health Promotion, Ontario, Canada for their assistance in accessing and weighting

the CCHS data.

38

Reference

1. US Department of Health and Human Services. Oral health in America: a report of

the Surgeon General. Rockville (MD): US Department of Health and Human

Services, national Institute of Dental and Cranofacial Research, National Institute of

Health, 2000.

2. Kelly M, Steele J, Nuttall N, Bradnock G, Morris J, Nunn J, et al. Adult Dental

Health Survey: Oral Health in the United Kingdom 1998. London: The Stationary

Office. 2000.

3. World Health Organization. WHO oral health country/ area profile programme.

Malmo University, Sweden: WHO Division of Non-communicable Disease/Oral

Health, WHO Collaborating Center; 2003.

4. Douglass CW, Shih A, Ostry L. Will there be a need for complete dentures in the

United States in 2020? J Prosthet Dent 2002;87(1):5–8.

5. Beltrán-Aguilar ED, Baker LK, Canto MT, Dye BA, Gooch BF, Griffin SO and

others. Surveillance for Dental Caries, Dental Sealants, Tooth Retention,

Edentulism, and Enamel Fluorosis -United States, 1988-1994 and 1999-2002.

MMWR August 26, 2005; 54(03):1-44.

6. Statistic Canada, Health Reports. Edentulism in Canada. The Daily, November 16,

2005. November issue of Health Reports, Vol. 17, No. 1.

7. Kressin NR, Spiro A 3rd, Atchison KA, Kazis L, Jones JA. Is depressive

symptomatology associated with worse oral functioning and well-being among

older adults? J Public Health Dent. 2002 Winter; 62(1):5-12.

39

8. The World Health Report 2001 - Mental Health: New Understanding, New Hope.

Geneva World Health Organization, 2001.

9. Kessler RC, Ustun TB. The World Mental Health (WMH) Survey Initiative Version

of the World Health Organization (WHO) Composite International Diagnostic

Interview (CIDI). Int J Methods Psychiatr Res. 2004;13(2):93-121.

10. Patten SB, Wang JL, Beck CA, Maxwell CJ. Measurement issues related to the

evaluation and monitoring of major depression prevalence in Canada. Chronic Dis

Can. 2005 Fall;26(4):100-6.

11. Heydecke G, Locker D, Awad MA, Lund JP, Feine JS. Oral and general health-

related quality of life with conventional and implant dentures. Community Dent

Oral Epidemiol. 2003 Jun;31(3):161-8.

12. Sheiham A, Cushing AM, Maizels J. The social impacts of dental disease. In: Slade

GD (ed) Measuring oral heath and quality of life. University of North Carolina,

Dental Ecology, Chapel Hill. 1997.

13. Anttila SS, Knuuttila ML, Sakki TK. Depressive symptoms as an underlying factor

of the sensation of dry mouth. Psychosom Med. 1998 Mar-Apr;60(2):215-18.

14. Soto Araya M, Rojas Alcayaga G, Esguep A. Association between psychological

disorders and the presence of Oral lichen planus, Burning mouth syndrome and

Recurrent aphthous stomatitis. Med Oral. 2004; Jan-Feb;9(1):1-7.

15. Gatchel RJ, Stowell AW, Buschang P. The relationships among depression, pain,

and masticatory functioning in temporomandibular disorder patients. J Orofac Pain.

2006 Fall;20(4):288-96.

40

16. Drake CW, Hunt RJ, Koch GG. Three-year Tooth Loss among Black and White

Older Adults in North Carolina. J Dent Res 1995;74(2): 675-80.

17. Hayashi N, Tamagawa H, Tanaka M, Hanioka T, Maruyama S, Takeshita T and

others. Association of tooth loss with psychological factors in male Japanese

employee. J Occup Health 2001; 43:351-55.

18. Anttila SS, Knuuttila MLE & Sakki TK. Relationship of depressive symptoms to

edentulousness, dental health, and dental health behavior. Acta Odontol Scand

2001; 59:406–12.

19. Statistics Canada. The CCHS Cycle 2.1 available from the URL:

<http://www.statcan.ca/english/concepts/health/cycle2_1/index.htm>

20. Kessler RC, Andrews G, Mroczek D, Üstün TB, Wittchen H-U. The World Health

Organization Composite International Diagnostic Interview Short-Form (CIDI-SF).

Int J Methods Psychiatr Res. 1998;7:171-85.

21. World Health Organization. Composite International Diagnostic Interview (CIDI).

WHO, Division of Mental Health 1990, Geneva.

22. Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk

factors for use in multivariable analysis. J Clin Epidemiol 1996; 49:907-16.

23. Ohayon MM, Priest RG, Guilleminault C, Caulet M. The prevalence of depressive

disorders in the United Kingdom. Biol Psychiatry 1999; 45:300-07.

24. Statistic Canada, Health Reports. Edentulism in Canada. The Daily, November 16,

2005. November issue of Health Reports, Vol. 17, No. 1.

41

25. Patten SB, Wang JL, Williams J VA, Currie S, Beck CA, Maxwell CJ, el-Guebaly

N. Descriptive Epidemiology of Major Depression in Canada. Can J Psychiatry

2006; 51(2): 84-90.

26. Patten SB. Long-term medical conditions and major depression in the Canadian

population. Can J Psychiatry. 1999 Mar; 44(2):151-7.

27. Woods CD. Self-Reported Mental Illness in a Dental School Clinic Population.

Journal of Dental Education 2003; 67(5): 500-504.

28. Friedlander AH, Mahler ME. Major depressive disorder. Psychopathology, medical

management and dental implications. JADA 2001; 132: 629-38.

29. John M, Micheelis W, Steele J G. Depression as a Risk Factor for Denture

Dissatisfaction. Journal of Dental Research; Sep 2007; 86(9): 852-7.

42

Figure 3: Theoretical framework for variable selection

5th Horizontal Selection

4th Horizontal Selection 3rd Horizontal Selection 2nd Horizontal Selection 1st Horizontal Selection Outcome

Physical Disability variables

Chronic conditions

Socio-economic variables

Dentate status (main predictor) & Demographic variables

Depression

Oral health variables

43

Table 1: General characteristics of the study population

Variable Frequency (%)

Gender (Female) 5707 (56.9) Age

45-54 yrs 55-64 yrs 65-74 yrs ≥ 75 yrs

3067 (30.6) 2842 (28.3) 2366 (23.6) 1761 (17.5)

Marital status Married/ Common-law Widow/Separated/ Divorced Single/Never Married

6203 (61.9) 3106 (31) 717 (7.2)

Education ≤ Secondary Grad > Secondary Grad

4601 (46.8) 5223 (53.2)

Income* Low Income Middle or High Income

834 (9.2)

8231 (90.8) Has depression (at 90% probability)** 503 (5) Has no teeth (edentulism) 1450 (14.4) Has a chronic condition 8533 (85.1) Participation and activity limitation

Sometimes Often Never

2448 (24.5) 2167 (21.7) 5382 (53.8)

* This variable classifies the total household income into 2 categories based on total household

income and the number of people living in the household. Low Income includes: < $15,000 if 1

or 2 people, <$20,000 if 3 or 4 people, and <$30,000 if 5+ people. Middle or High Income

includes: ≥$15,000 if 1 or 2 people, ≥$20,000 if 3 or 4 people, and ≥$ 30,000 if 5+ people.

** Depression was measured using the Composite International Diagnostic Interview -Short-

Form for Major Depression (CIDI-SFMD).

44

Tabl

e 2.

Pre

vale

nce

of d

emog

raph

ic, s

ocio

econ

omic

and

oth

er c

hara

cter

istic

s by

dent

ate

and

depr

essi

on st

atus

Com

plet

ely

Ede

ntul

ous

90%

Pro

babi

lity

of

Dep

ress

ion

Var

iabl

e Y

es %

n*

OR

(95%

C.I.

)

Yes

%

n*

O

R (9

5%C

.I.)

Dep

ress

ion

4 85

86

0.76

^ (0

.58-

1.01

)

- -

- Ed

entu

lism

-

- -

95

33 (1

4.6)

50

3 (1

1.5)

0.

76^

(0.5

8-1.

01)

Gen

der (

Fem

ale)

60

.4

8586

1.

19b (1

.06-

1.33

)

9533

(56.

2)

503

(70.

4)

1.86

c (1.5

3-2.

26)

Age

45

-54

yrs

55-6

4 yr

s 65

-74

yrs

≥ 75

yrs

8.

6 20

.4

33.8

37

.2

85

86

1 2.

76c (2

.22-

3.42

) 6.

20c (5

.05-

7.61

) 10

.50c (8

.54-

12.9

0)

52

.1

29.6

11

.1

7.2

95

33

1 0.

59c (0

.48-

0.73

) 0.

26c (0

.19-

0.35

) 0.

22c (0

.16-

0.32

) M

arita

l sta

tus

Mar

ried/

Com

mon

-law

Wid

ow/S

epar

ated

/ Div

orce

d Si

ngle

/Nev

er M

arrie

d

49

.2

46.8

4

85

77

1 2.

15c (1

.92-

2.43

) 0.

68b (0

.51-

0.90

)

48

.4

40.2

11

.4

95

24

1 1.

71c (1

.42-

2.07

) 2.

12c (1

.57-

2.86

) N

umbe

r of p

erso

ns ≥

12

year

s old

in h

ouse

hold

N

one

≥ 1

15

5.9

85

86

0.

36c (0

.25-

0.51

)

93

7

95

33

1.

28

(0.9

0-1.

83)

Num

ber o

f per

sons

6-1

1 ye

ars o

ld in

hou

seho

ld

Non

e ≥

1

14

.9

5.3

85

86

0.

32c (0

.21-

0.49

)

95

5

95

33

1.

16 (0

.77-

1.75

)

Num

ber o

f per

sons

≤ 5

yea

rs o

ld in

hou

seho

ld

Non

e ≥

1

14

.6

7.1

85

86

8586

0.

45b (0

.27-

0.76

)

97

.6

2.4

95

33

1.

15 (0

.64-

2.08

)

Educ

atio

n (≤

Sec

onda

ry G

rad)

70

.1

8405

3.

12c (2

.77-

3.53

)

44.4

93

28

0.90

(0.7

5-1.

08)

Low

-Inco

me

20.1

77

99

3.12

c (2.6

6-3.

67)

16

.9

8593

2.

12c (1

.65-

2.73

) Sm

okin

g (D

aily

/ Occ

asio

nal)

20.6

85

53

1.03

(0.9

0-1.

19)

21

.5

9494

1.

09 (0

.87-

1.35

) H

as a

chr

onic

con

ditio

n 91

.7

8580

2.

12c (1

.74-

2.58

)

96.6

95

28

5.25

c (3.2

2-8.

54)

Nee

ded

help

with

serie

s of t

asks

(IA

DL)

40

.3

8567

2.

24c (2

.00-

2.52

)

48.1

95

10

2.87

c (2.3

9-3.

44)

Parti

cipa

tion

and

Act

ivity

Lim

itatio

n (P

AL)

N

ever

So

met

imes

O

ften

41

.3

33.5

25

.2

85

55

1 2.

31c (2

.02-

2.63

) 1.

41c (1

.22-

1.62

)

29

.3

42.4

28

.3

94

95

1 3.

88c (3

.13-

4.82

) 2.

19c (1

.73-

2.78

) D

iffic

ulty

- m

akin

g ne

w fr

iend

s 23

.6

8579

1.

86c (1

.39-

2.49

)

16.1

95

26

10.1

0c (7.6

4-13

.37)

D

iffic

ulty

- de

alin

g w

ith u

nkno

wn

peop

le

22.7

85

78

1.77

c (1.3

1-2.

38)

14

.5

9524

8.

69c (6

.51-

11.5

9)

Diff

icul

ty -

star

ting/

mai

ntai

ning

con

vers

atio

n 20

.7

8583

1.

57b (1

.13-

2.16

)

12.9

95

28

8.32

c (6.1

5-11

.25)

^B

orde

rline

sig

nific

ance

(p =

0.0

56),

a p

< 0.

05, b p

< 0

.005

, c p <

0.0

01

* n

repr

esen

ts th

e nu

mbe

r of r

espo

nden

ts in

eac

h qu

estio

n. T

he n

umbe

r var

ies d

ue to

diff

eren

ces i

n th

e re

spon

se ra

te fo

r eac

h qu

estio

n. T

he n

umbe

r of p

artic

ipan

ts re

spon

ded

“Yes

” ca

n be

obt

aine

d by

mul

tiply

ing

the

% b

y th

e n

for e

ach

row

. The

tota

l num

ber o

f the

ede

ntul

ous p

artic

ipan

ts is

145

0, a

nd th

e to

tal n

umbe

r of t

he p

artic

ipan

ts w

ith 9

0%

prob

abili

ty o

f dep

ress

ion

is 5

03.

45

Tabl

e 3.

Ora

l hea

lth st

atus

by

dent

ate

and

depr

essi

on st

atus

Com

plet

ely

Ede

ntul

ous

90%

Pro

babi

lity

of

Dep

ress

ion

Var

iabl

e Y

es %

n

O

R (9

5%C

.I.)

Y

es %

n

O

R (9

5%C

.I.)

Too

th lo

ss a

nd (r

emov

able

) den

ture

wea

ring

: Te

eth

rem

oved

(in

the

past

12

mon

ths)

17.1

6654

1.53

a (1

.06-

2.21

)

16

.6

65

16

1.

50b (1

.12-

2.01

) W

ears

den

ture

s 95

.4

8582

42

.41c (3

3.06

-54.

42)

34

.6

9528

0.

73b (0

.60-

0.88

) O

ral/f

acia

l pai

n an

d sy

mpt

oms:

O

ral/f

acia

l pai

n

27

.6

85

30

0.

49c (0

.43-

0.55

)

69

.4

94

52

3.

41c (2

.80-

4.14

) H

ad b

leed

ing

gum

s (in

the

past

mon

th)

0.5

8585

0.

05c (0

.03-

0.12

)

15.7

95

32

2.63

c (2.0

4-3.

39)

Had

dry

mou

th (i

n th

e pa

st m

onth

) 16

.8

8581

1.

21a (1

.04-

1.41

)

34.4

95

28

3.32

c (2.7

4-4.

03)

Had

bad

bre

ath

(in th

e pa

st m

onth

) 6

8456

0.

59c (0

.46-

0.73

)

18.1

93

85

2.27

c (1.7

9-2.

89)

Func

tiona

l dis

abili

ty:

Inab

ility

to c

hew

all

type

of f

ood

33

.8

8568

5.14

c (4.5

0-5.

86)

16.7

9502

1.42

b (1.1

2-1.

18)

Diff

icul

ty in

spea

king

cle

arly

bec

ause

of t

he m

outh

co

nditi

on

5.4

8582

2.

18c (1

.68-

2.84

)

7.4

9529

2.

80c (1

.96-

4.00

)

Soci

al d

isab

ility

: A

void

ed c

onve

rsat

ion

beca

use

of th

e m

outh

con

ditio

n 2.3

85

83

1.

19 (0

.82-

1.73

)

6

9530

3.41

c (2.2

9- 5

.08)

A

void

ed la

ughi

ng/sm

iling

bec

ause

of t

he m

outh

co

nditi

on

2.1

8582

0.

79 (0

.54-

1.16

)

9.5

9528

4.

61c (3

.33-

6.4

0)

Lim

ited

soci

ally

due

to o

ral h

ealth

3.

2 85

80

0.91

(0.6

6-1.

25)

10

.5

9526

3.

73c (2

.74-

5.0

7)

Self-

perc

eive

d he

alth

of t

eeth

and

mou

th:

Exce

llent

23.7

1

14

.9

1

Very

goo

d 24

.7

0.

68c (0

.58-

0.80

)

26.1

0.82

(0.6

1-1.

10)

Goo

d 37

.5

8582

0.

96 (0

.83-

1.11

)

29.9

95

21

0.81

(0.6

1-1.

08)

Fair

7.

6

0.49

c (0.3

9-0.

61)

17

.3

0.

50c (0

.36-

0.68

) Po

or

6.5

1.

05 (0

.81-

1.35

)

11.8

0.31

c (0.2

2-0.

44)

Den

tal i

nsur

ance

and

vis

it:

No

dent

al in

sura

nce

63

.4

85

51

2.

52c (2

.24-

2.83

)

33

.8

94

74

0.

64c (0

.53-

0.77

) Fr

eque

ncy

of d

enta

l vis

its

> O

nce/

year

4.1

1

42.3

1 O

nce/

year

6.

9 85

66

2.64

c (7.7

0-15

.94)

23.9

94

90

0.85

(068

-1.0

7)

< O

nce/

year

4.

8

11.0

8c (0.0

6-0.

13)

7.

2

1.43

(0.9

9-2.

06)

Emer

genc

y 84

55.0

1c (41.

99-7

2.06

)

26.6

0.94

(0.7

6-1.

18)

a p

< 0.

05, b p

< 0

.005

, c p <

0.0

01

* n

repr

esen

ts th

e nu

mbe

r of r

espo

nden

ts in

eac

h qu

estio

n. T

he n

umbe

r var

ies d

ue to

diff

eren

ces i

n th

e re

spon

se ra

te fo

r eac

h qu

estio

n. T

he n

umbe

r of p

artic

ipan

ts re

spon

ded

“Yes

” ca

n be

obt

aine

d by

mul

tiply

ing

the

% b

y th

e n

for e

ach

row

. The

tota

l num

ber o

f the

ede

ntul

ous p

artic

ipan

ts is

145

0, a

nd th

e to

tal n

umbe

r of t

he p

artic

ipan

ts w

ith 9

0%

prob

abili

ty o

f dep

ress

ion

is 5

03.

46

Table 4. Logistic regression models predicting depression for all study sample, dentate group, and edentulous group

All study sample Dentate group Edentulous group

Variable AOR* (95% CI) AOR (95% CI) AOR (95% CI) Edentulism 0.94 (0.66-1.33) Gender (female) 1.41b (1.13-1.77) 1.50 b (1.18-1.91) - Age

45-54 yrs 55-64 yrs 65-74 yrs ≥ 75 yrs

1

0.56c (0.44-0.71) 0.24 c (0.17-0.34) 0.16 c (0.10-0.24)

1 c 0.54 c (0.42-0.69) 0.20 c (0.13-0.29) 0.12 c (0.07-0.19)

1 b

0.63 (0.27-1.47) 0.24 b (0.09-0.66) 0.25 c (0.09-0.56)

Marital status Married/ Common-law Widow/Separated/ Divorced Single/Never Married

1 c

1.69 c (1.34-2.14) 1.35 (0.95-1.93)

1 c 1.73 c (1.35)

1.28 (0.88-1.88)

-

Income (Low) 1.51a (1.10-2.07) - 2.02 a (1.04-3.90) Dental insurance 0.78 a (0.62-0.98) - 0.51 a (0.26-1.00) Avoided laughing/smiling because of the mouth condition 1.65 a (1.09-2.50) 1.94 b (1.26-2.97) -

Bleeding gum 1.36 a (1.00-1.85) - - Dry mouth 1.68 c (1.30-2.19) 1.72 c (1.31-2.27) - Orofacial pain 1.51 b (1.17-1.94) 1.57 b (1.21-2.04) 3.34 c (1.74-6.38) Participation and Activity Limitation (PAL)

Often Sometimes Never

1 b

1.65 b (1.21-2.26) 1.41 a (1.07-1.87)

1 b

0.66 b (0.49-0.89) 1.19 (0.90-1.59)

-

Need help with series of tasks 1.47 b (1.12-1.94) 1.56 b (1.17-2.07) - Difficulties in social situations 3.53 c (2.60-4.80) 2.88 c (2.04-4.07) 9.39 c (4.63-19.06) Has food allergies - - 3.27 b (1.41-7.58) Has fibromyalgia 1.74 b (1.17-2.60) - - Has back problems 1.62 c (1.31-2.02) 1.51 b (1.20-1.92) 2.41 b (1.28-4.51) Chronic fatigue syndrome - 1.77 a (1.07-2.93) - Has migraine headaches 1.41 a (1.08-1.83) 1.49 b (1.13-1.97) - Has cancer 1.68 a (1.09-2.58) - 2.76 a (1.03-7.41) Has glaucoma - 1.79 a (1.02-3.14) - Has stomach or intestinal ulcers - - - Has bowel disorder

1.71 b (1.19-2.45)

2.04 c (1.39-2.98)

-

* Adjusted Odds Ratio, a p < 0.05, b p < 0.005, c p < 0.001

47

CHAPTER 3:

MANUSCRIPT 2

A submission to Statistical Methods in Medical Research

48

“Comparing predictability of logistic regression models using different selection

methods” Muneera Al Shamrany BDS, MSc (Dental Sciences) 1

Herenia P Lawrence DDS, MSc, PhD 2

1 MSc candidate (Dental Public Health program)

Faculty of Dentistry, University of Toronto.

2 Associate Professor

Dept. of Biological and Diagnostic Sciences / Community Dentistry

Faculty of Dentistry, University of Toronto.

124 Edward Street, Rm 515D. Toronto, Ontario, Canada. M5G 1G6

Phone: 416-979-4908 ext. 4492

Fax: 416-979-4936

E-mail: herenia.lawrence@utoronto.ca

Contact HP Lawrence for reprint requests

Muneera Al Shamrany was sponsored by the Ministry of Higher Education, Saudi Arabia

to conduct this study and fulfill one of the requirements for the degree of Master of

Sciences in Dental Public Health.

49

Abstract: Researchers face the challenge of dealing with large number of variables when

they analyze data from large epidemiological surveys. Various multivariate modeling

methods are available to help eliminate noise variables and control for confounding

effects in order to identify true predictors for the outcome of interest. However, each

method has its limitations. Further, when different regression selection procedures are

applied to the same data set, they could yield different prediction models. One important

criterion to keep in mind when selecting among different models is the model’s ability to

predict the outcome of interest. In this paper we demonstrated a practical example of

utilizing the Receiver Operating Characteristics (ROC) curve as a decision tool to select

among different binary regression models constructed using three selection procedures.

The results from this study showed slight differences in the area under the curve (AUC);

nevertheless, it provided us with a yardstick to choose one model for each population. We

anticipate that the similarity in variable contents and the AUC is due to the large data set

we used. We recommend the ROC as a decision tool to help researchers select among

several candidate models. It is an easy, automated procedure, yet, it offers statistical

information that can be used along with the expert judgment.

50

1. Introduction

Researchers face the challenge of dealing with large number of variables when they

analyze data from large epidemiological surveys. Typically, such data are used to explore

underlying relationships between one or more outcomes to several predictors that could

be continuous, ordinal, categorical or simply binary. Several multivariate modeling

methods are available to help eliminate noise variables and control for confounding

effects in order to identify true predictors for the outcome of interest. None of those

methods is the perfect approach and each method has its limitations (1).

Binary logistic regression is a method of choice for outcomes that have two events,

typically caseness and non-caseness, with categorical and continuous predictors (2). It is

important to point out the sensitive nature of the regression algorithms in terms of

variable selection process. Statistical packages allow researchers to control entry and exit

of variables in analysis through different selection methods. Most of these packages have

the options of forward, backward and stepwise selection procedures to build a prediction

model. The final models constructed using different methods from the same data set are

extremely unlikely to be identical. Further, a particular selection method could produce

virtually an infinite number of models depending on what variables would be entered first

to the model and, subsequently, researchers often find themselves in a position to select

among different potential models (1). This, especially, is a bigger challenge in case of

large epidemiologic surveys with large number of predictors (3).

Checking model fit is an important step once a model has been developed in order to

verify model assumptions. However, this step does not provide a base to choose among

several models because it is highly likely that most of the models would have the same

51

degree of fit because they have all been built using the same data set (1). One criterion

that is important to keep in mind when selecting among different models is the model’s

ability to predict the outcome of interest. Different methods were proposed to facilitate

comparing predictability of two or more models such as Monte Carlo simulation (4),

bootstrapping (5) and the receiver operating characteristics (ROC) (6).

In this paper we demonstrated a practical example of utilizing the ROC as a decision

tool to select among different binary regression models constructed using three selection

procedures. The data set used for this demonstration was obtained from the Canadian

Community Health Survey (CCHS) Cycle 2.1 (7).

2. Methods

Description of study design and participants

This investigation was a secondary analysis of the CCHS Cycle 2.1 data in order to

identify predictors of depression in a sample of 10,036 Canadian adults who were 45

years of age or older. The CCHS Cycle 2.1 was collected on household Canadians 12

years or older from January 2003 to December 2003 through personal and telephone

interviews (7).

Measurements

Depressive disorder was defined according to the Diagnostic and Statistical Manual

of Mental Disorders, 4th edition (DSM)-V (8). The Composite International Diagnostic

Interview-Short Form (CIDI-SFMD) was used, which measures the occurrence of

depressive symptoms in the same 2-week period (in the same individuals) during the 12

months preceding the interview (9). Specifically, the item ‘dpscdpp’: Probability of

52

Caseness to Respondents calculates the probability (0-1) of caseness of depression where

0 was assigned to respondents who denied the two stem questions: feeling depressed or

loss of interest in things. We selected 0.9 as a cut-off value for this item indicating a 90%

probability of caseness that the respondent would have been diagnosed as having

experienced a major depressive episode in the past 12 months. From this point further, we

will refer to the participants who had 90% probability of depression as “depressed”, “had

depression” or “had depressive disorder”. Therefore, depression was coded as a binary

variable where each participant was classified into the ‘depressed’ or ‘non-depressed’

category. Moreover, depressed participants were identified as such according to their

responses to the DPS questionnaire with no clinical verification.

The CCHS included several modules measuring socio-demographic variables,

chronic conditions, physical disability, and smoking habits among a vast number of

variables. The literature was reviewed for evidence on epidemiologic correlates of

depression in this age group as part of another study which investigated the association

between complete loss of natural teeth (edentulism) and depression (10). All potential

predictors were categorical variables, mainly binary response variables.

Preliminary data management

First, we ensured that variables had the proper coding for the regression analysis to

predict the caseness of depression; for instance, we assigned 0 to “non-depressed” and 1

to “depressed”. Some examples of coding of predictors are smoking: 0= no and 1= yes,

gender: 0= male and 1= female, complete edentulism: 0= no and 1= yes and so forth.

Before we started the regression modeling, we had a large number of variables that

needed to be reduced due to small frequencies of responses. We elected to use the bi-

53

variable analysis to assess underlying patterns of association between predictor variables

and depression. We combined this approach with evidence from the literature on

epidemiologic depression correlates to guide us to keep important predictors even if they

had no significant association with depression at the bivariate level. Bivariate analyses

were done by performing cross-tabulations to analyze the variation in the distributions of

the variables. Statistical tests at this level included the Chi-squared-test, Fisher’s exact

test and estimates of the risks (odds ratio and 95% confidence intervals). Further,

multicolinearity was assessed and was found more evident among variables measuring

psychological impact. We used the authors consensus to select between highly correlated

variables and a third party, a psychiatrist, was consulted when the authors did not reach

an agreement.

Binary regression modeling

At the stage of multivariate analysis, we have already reduced the number of

predictors to about 30 variables at the bivariate analysis. A hierarchical logistic

regression approach was employed in order to decide the order of variables entry. Figure

1 shows the general order in which variables were entered into each model. We used the

SPSS to conduct different regression procedures including: 1) forward stepwise selection

where models were built by adding variables in blocks (FW-Block), 2) forward stepwise

selection item-by-item (FW-Item) and 3) backward stepwise selection (BW).

Details of selection procedures:

1. Forward stepwise selection block entry (FW-Block): The SPSS allows an

automated block entry of variables. We used Figure 3 as a guide to select what

group of variables would be block 1, block 2 and so forth. For example, block 1

54

included the predictor of interest (edentulism) and the demographic variables.

Block 2 included the socioeconomic variables such as income, education, marital

status and etc. Also, we elected to run the regression analysis every time we

added a block. For instance, regression analysis was conducted at the stage of

block 1 entry and before block 2 entry of variables. This allowed us to examine

block 1 variables that were retained in the model in order to manually remove

those that did not stay in the model before proceeding to adding block 2.

2. Forward stepwise selection Item-by-item entry (FW-Item): This model was built

by adding items one at a time and assessing the model at each entry to facilitate

manual removal of variables that were not significant.

3. Backward stepwise selection (BW): We followed the typical backward stepwise

procedures which consisted of starting a “full model” that included all variables

eligible for the multivariate analysis. After running the algorithm, we manually

removed variables that were not significant and re-run the algorithm. The process

of manual elimination of non-significant variables and re-running the logistic

regression was repeated until we obtain the final model that contained the

significant variables only.

Since we used SPSS, we chose to keep the default variable entry and retention

criterion of 0.05 and 0.10, respectively, for all regression algorithms (11).

First, the three procedures were applied to the entire study population (n= 10,036)

which included both dentate and edentulous individuals to facilitate the comparison

between the two groups. Because some variables were specific to the dentate cases such

as experiencing toothache, bleeding gums and frequency of tooth brushing, separate

55

models were also constructed for dentate (n= 8586) and edentulous (n= 1450)

populations to improve the fit of each model. Therefore, we ended up with a total of 9

candidate models; 3 for each population. We anticipated that the models could be

different in terms of number and what variables they included; thus the Receiver

Operating Characteristics (ROC) curve was used to compare the predictive abilities of

these models. Models with the greater area under the curve (AUC) were selected as final

models.

Since complex sampling process has been used in the original CCHS Cycle 2.1 where

every participant was assigned a probability value to be included in the survey,

coefficients of variation (CV) approximation tables were obtained from Statistics Canada

and were used in our study to estimate the precise 95% confidence interval around the

Adjusted Odds Ratios (AOR).

3. Results

The models developed for the entire study population were very similar but not

identical (Table 1). The model with the most predictors was the FW-Item (17 variables).

For the dentate participants, Table 2, all three models contained 14 predictors with some

variation on the variables retained. The BW method yielded the largest number of

predictors (8 variables) of depression in the model for edentulous individuals compared

to the FW-Block and FW-Item selection methods. Nevertheless, Tables 1, 2 and 3 show

striking similarity in the strength of the odds ratio across the three models for each

population.

56

The areas under the ROC curve for all the models are presented in Table 4. For each

population, the AUCs were very close to each other with overlapping confidence

intervals. The graphic presentation for the ROC comparisons, Figures 2, 3 and 4, reflect

the similar predictability of the three models constructed for the entire study population,

dentate and edentulous participants, respectively. In Table 4, the model with the largest

AUC was the FW-Item for the entire study population, FW-Block for the dentate

participants, and BW for the edentulous participants. All the areas under the ROC curve

were statistically different from the reference line which represents 0.5 chance

predictability. In general, the predictability for all models ranged from a low of AUC=

0.809 to as high as 0.863 which is considered as excellent predictability.

4. Discussion

When different regression selection procedures are applied to the same data set, they

could yield different prediction models (12). In the example we presented, the three

models constructed from specific data sets were not identical, although they were highly

similar in content and number of predictors. This particular finding addresses the

objective of this paper and emphasizes the need for a tool that assists researchers to

choose among more than one potential final model. Also, the results demonstrated the

sensitivity of commonly used regression algorithms (13). Automated regression analyses

in statistical packages are a convenient way of making predictions by eliminating the

noise and identifying true risk factors. Investigators need to realize that this convenience

comes at a price of robustness, especially when dealing with small data sets. The main

reason for poor robustness of regression modeling is the boundless possibilities of how a

57

variable could enter the analysis (12). In other words, one can choose from several

regression methods, and further; even within a chosen method one can choose the order

of variables entry in an unrestricted fashion. For example, if we have 10 variables at the

stage of multivariate modeling that include “preventive therapy” as a predictor for an

outcome of interest, we would be able to choose when this particular predictor enters the

model; we might decide to add it at the beginning, second, third, or forth, and so on. The

same applies to the rest of the variables. This leads to unlimited number of combinations

of variables entering the model at the same time, and subsequently, a huge number of

comparisons to be carried out by the regression algorithm.

The ability of the regression model to predict the outcome of interest should be the

guiding criterion to choose among candidate models. In the case of binary logistic

analysis, the model should have good ability to discriminate between people who have

the outcome and those who do not. In this practical example, we compared the ability of

the models to discriminate between those who had depression and those who did not.

Model validation methods can be classified as external and internal methods (14).

External validation has been described as the most stringent; where the developed model

is tested on different data set than that used in constructing it. In order to achieve this type

of validation, Harrell et al. (1996) described the application of the “frozen model” to a

new population (15). Nonetheless, unbiased internal validation should precede any

external validation.

The purpose of this paper was to assess the performance of logistic regression by

comparing the AUC of each of three models developed for separate study populations.

The area under the ROC curve has been used by others to assess the effectiveness of

58

several logistic regression models in making accurate prediction (6, 10, 16). The results

from this study showed slight difference in the AUC; nevertheless, it provided us with a

yardstick to choose one model for each population. We anticipated that the similarity in

variable contents and the AUCs among the three models is due to the large data set we

used. We are more inclined to expect larger variation, in both AUC and variable contents,

among regression models in case of small data sets. The reason for such a problem is the

fact that the number of the predictors in most of the studies is relatively large for the data

set leading to multiple comparisons that require larger sample to be accurate (17).

While forward variable selection is typically recommended to build a model;

backward selection is recommended as a “check method” against the result of the

forward procedures and not as a method of choice for building a regression model (11).

The result in this study indicated that none of the selection procedures used was

consistently superior to the others and, hence, each selection method is suitable for

variable selection. Instead, more attention should be given to checking the fit and the

performance of candidate regression models to select the best model. Careful testing of

the model fit is a very crucial step to ensure satisfaction of the regression model

assumptions; however, this subject is beyond the scope of the present paper.

Graphic presentation of the ROC curve offers a useful visual tool to examine the

AUC and can be obtained directly from most statistical packages. The three ROC curves

were overlapped for the entire study population and for the dentate individuals. On the

other hand, we observed relatively more variation in the AUC and the shape of the ROC

among the three models developed for the edentulous population. The sample size for the

latter is roughly one-fifth (14.9%) the size of the entire study population and less than a

59

quarter (18 %) of the size of the dentate population which indicates the impact of sample

size on the performance of regression models.

In general, we presented a way of using the ROC as a method of assessing regression

models performance and we used a real data example for this purpose. We recommend

this method as a decision tool to help researchers choose among several candidate

models. It is an easy, automated procedure, yet, it offers statistical information that can be

used to add to expert’s judgment.

Acknowledgements

The authors would like to express their gratitude to Laine Ruus, University of

Toronto, Data Library Service; Andrew Turvey, Statistics Canada / Statistique

Canada, Central Region / Région du Centre; and Chee Wong, Public Health

Branch, Ministry of Health Promotion, Onatrio, Canada for their assistance in

accessing and handling the CCHS data.

60

References

1. Greenland S. Modeling and variable selection in epidemiologic analysis.

Commentary. Am J Public Health 1989; 79(3):340-49.

2. Lemeshow S, Hosmer DW, Applied Logistic Regression, 2nd Ed. Wiley-

Interscience, 2001.

3. Dohoo IR, Ducrot C, Fourichon C, Donald A, Hurnik D. An overview of

techniques for dealing with large numbers of independent variables in

epidemiologic studies. Preventive Veterinary Medicine 1997; 29(3):221-239.

4. O’Gorman TW, Woolson RF. Variable selection to discriminate between two

groups: Stepwise logistic regression or stepwise discriminant analysis. The

American Statistician 1991; 45(3):187-93.

5. Wählby U, Jonsson EN, Karisson MO. Comparison of stepwise covariate model

building strategies in population pharmacokinetic- pharmacodynamic analysis.

AAPS PharmSci 2002; 4(4): article 27.

6. Hedgewald J, Pfahlberg A, Uter W. A backwards-manual selection macro for

binary logistic regression in the SAS v.8.02 PROC LOGESTIC procedure.

Available from the URL:

< http://www.imbe.med.uni-erlangen.de/issan/SAS/bms/bms_manuel.pdf >

7. Statistic Canada, Health Reports. Edentulism in Canada. The Daily, November

16, 2005. November issue of Health Reports, Vol. 17, No. 1.

8. American Psychiatric Association. Diagnostic and statistical manual of mental

disorders, 3rd edn. revised. American Psychiatric Association, Washington,

DC;1987.

61

9. Kessler RC, Andrews G, Mroczek D, Üstün TB, Wittchen H-U. The World

Health Organization Composite International Diagnostic Interview Short-Form

(CIDI-SF). Int J Methods Psychiatr Res. 1998;7:171-85.

10. Al Shamrany M, Lawrence HP, Meier RM, Jokstad A. Is depression associated

with edentulism in Canadian adults? Unpublished paper. (under editing)

11. SPSS 15.0 Base User’s Guide. SPSS.

12. Lagakos SW. Effects of mismodeling and mismeasuring explanatory variables on

tests of their association with a response variable. Stat Med 1988; 7:257-74.

13. Vandenbroucke JP. Should we abandon statistical modeling altogether? Am J

Epidemiol 1987; 126:10-13.

14. Efron B. How biased is the apparent error rate of a prediction rule? J Am Stat

Assoc 1986; 81:461-70.

15. Harrell F, Lee K, Mark D. Multivariable prognostic models: issues in developing

models, evaluating assumptions and adequacy, and measuring and reducing

errors. Stat in Med 1996; 15:361-87.

16. Jaimes F, Farbiarz J, Alvarez D, Marines C. Comparison between logistic

regression and neural networks to predict death in patients with suspected sepsis

in the emergency room. Cri Care 2005; 9(2):R150-55.

17. Styerberg EW, Eijkmans MJ, Harrell FE, Habbema JD. Prognostic modeling with

logistic regression analysis: in search of a sensible strategy in small data. Med

Decision making 2001; 21(1):45-56.

62

Table 1. Logistic regression models predicting depression for the study population using three

different techniques

FW-Block FW-Item BW Variable AOR (95% CI) AOR (95% CI) AOR (95% CI) Gender (female) - 1.41 b (1.13-1.77) 1.44 b (1.15-1.80) Age

45-54 yrs 55-64 yrs 65-74 yrs ≥ 75 yrs

1c

0.56 c (0.44-0.70) 0.24 c (0.17-0.34) 0.16 c (0.10-0.24)

1

0.56 c (0.44-0.71) 0.24 c (0.17-0.34) 0.16 c (0.10-0.24)

1 c

0.56 c (0.44-0.71) 0.24 c (0.17-0.34) 0.16 c (0.10-0.24)

Marital status Married/ Common-law Widow/Separated/ Divorced Single/Never Married

1 c

1.76 a (1.40-2.23) 1.34 (0.94-1.92)

1 c

1.69 c (1.34-2.14) 1.35 (0.95-1.93)

1 c 1.69 c (1.34-2.14) 1.35 (0.95-1.93)

Income (Low) 1.53 b (1.12-2.10) 1.51 a (1.10-2.07) 1.51 a (1.10-2.06) Dental insurance 0.78 a (0.62-0.98) 0.78 a (0.62-0.98) 0.78 a (0.62-0.98) Avoided laughing/smiling because of the mouth condition 1.64 a (1.08-2.48) 1.65 a (1.09-2.50) 1.70 a (1.13-2.57)

Bleeding gum - 1.36 a (1.00-1.85) -

Dry mouth 1.68 c (1.30-2.19) 1.68 c (1.30-2.19) 1.67 c (1.29-2.17)

Orofacial pain 1.62 c (1.27-2.07) 1.51 b (1.17-1.94) 1.60 c (1.25-2.05) Participation and activity limitation (PAL)

Often Sometimes Never

1 a

0.72 a (0.54-0.95) 1.13 (0.87-1.48)

1 b

1.65 b (1.21-2.26) 1.41 a (1.07-1.87)

1 b 1.69 b (1.24-2.31) 1.41 a (1.07-1.87)

Need help with series of tasks 1.60 b (1.22-2.08) 1.47 b (1.12-1.94) 1.49 b (1.14-1.96) Difficulties in social situations 3.50 c (2.58-4.76) 3.53 c (2.60-4.80) 3.62 c (2.67-4.91) Has fibromyalgia 1.88 b (1.26-2.78) 1.74 b (1.17-2.60) 1.83 b (1.23-2.71) Has back problems 1.60 c (1.29-2.00) 1.62 c (1.31-2.02) 1.66 c (1.34-2.07) Has migraine headaches 1.48 b (1.14-1.92) 1.41 a (1.08-1.83) 1.44 c (1.10-1.87) Has cancer 1.66 a (1.08-2.55) 1.68 a (1.09-2.58) 1.66 a (1.08-2.55) Has bowel disorder

1.78 b (1.24-2.56)

1.71 b (1.19-2.45)

-

* Adjusted Odds Ratio, a p < 0.05, b p < 0.005, c p < 0.001

63

Table 2. Logistic regression models predicting depression for the dentate participants using three

different techniques

FW-Block FW-Item BW Variable AOR (95% CI) AOR (95% CI) AOR (95% CI) Gender (female) 1.50 b (1.18-1.91) 1.53 c (1.21-1.94) 1.60 c (1.27-2.02) Age

45-54 yrs 55-64 yrs 65-74 yrs ≥ 75 yrs

1 c 0.54 c (0.42-0.69) 0.20 c (0.13-0.29) 0.12 c (0.07-0.19)

1 c 0.53 c (0.42-0.68) 0.23 c (0.16-0.33) 0.13 c (0.08-0.20)

1 c 0.54 c (0.42-0.69) 0.24 c (0.17-0.34) 0.14 c (0.08-0.22)

Marital status Married/ Common-law Widow/Separated/ Divorced Single/Never Married

1 c 1.73 c (1.35)

1.28 (0.88-1.88)

1 c

1.82 c (1.45-2.29) 1.45 c (1.02-2.07)

1 c 1.80 c (1.43-2.26) 1.41 (0.99-2.00)

Avoided laughing/smiling because of the mouth condition 1.94 b (1.26-2.97) 1.84 b (1.22-2.78) 1.85 b (1.23-2.79) Had bleeding gums – past mo. - 1.59 b (1.19-2.12) 1.59 b (1.19-2.12) Dry mouth 1.72 c (1.31-2.27) 1.94 c (1.52-2.47) 2.00 c (1.57-2.55) Had pain - jaw joints - past mo. - 1.42 a (1.05-1.93) 1.40 a (1.03-1.90) Orofacial pain 1.57 b (1.21-2.04) - - Participation and activity limitation (PAL)

Often Sometimes Never

1 b 0.66 b (0.49-0.89) 1.19 (0.90-1.59)

1 c 1.86 c (1.35-2.55) 1.59 b 1.20-2.11)

1 c 0.56 c (0.43-0.74) 1.27 (0.98-1.66)

Need help with series of tasks 1.56 b (1.17-2.07) 1.52 b (1.16-2.00) - Difficulties in social situations 2.88 c (2.04-4.07) 2.94 c (2.12-4.06) 3.01 c (2.18-4.17) Has fibromyalgia - 1.79 b (1.19-2.69) 1.82 b (1.21-2.74) Has back problems 1.51 b (1.20-1.92) 1.56 c (1.25-1.95) 1.58 c (1.27-1.98) Chronic fatigue syndrome 1.77 a (1.07-2.93) - - Has migraine headaches 1.49 b (1.13-1.97) 1.63 c (1.25-2.12) 1.61 a (1.23-2.10) Has bowel disorder 2.04 c (1.39-2.98) - 1.82 b (1.26-2.63) Has stomach or intestinal ulcers - 1.63 a (1.09-2.43) 1.56 a (1.04-2.34) Has glaucoma

1.79 a (1.02-3.14)

-

-

* Adjusted Odds Ratio, a p < 0.05, b p < 0.005, c p < 0.001

64

Table 3. Logistic regression models predicting depression for the edentulous participants using

three different techniques

FW-Block FW-Item BW Variable AOR (95% CI) AOR (95% CI) AOR (95% CI) Age

45-54 yrs 55-64 yrs 65-74 yrs ≥ 75 yrs

1 c

0.53 (0.24-1.19) 0.17 c (0.07-0.45) 0.19 c (0.08-0.45)

1 c

0.55 (0.25-1.24) 0.19 c (0.07-0.47) 0.15 c (0.06-0.38)

1 b 0.63 (0.27-1.47) 0.24 b (0.09-0.66)

0.25 c Income (Low) - - 2.02 a (1.04-3.90) Dental insurance - - 0.51 a (0.26-1.00) Dry mouth - 2.80 b (1.50-5.22) - Orofacial pain 2.56 b (1.41-4.67) - 3.34 c (1.74-6.38) Difficulty with social situations 8.65 c 4.53 16.53) 8.56 c 4.50-16.28) 9.39 c (4.63-19.06) Has food allergies 3.06 b (1.38-6.80) 3.21 b (1.45-7.15) 3.27 b (1.41-7.58) Has back problems 2.27 b 1.25-4.14) 2.21 b (1.22-3.98) 2.41 b (1.28-4.51) Has cancer

2.91a (1.12-7.59)

2.63 b (1.01-6.85)

2.76 a (1.03-7.41)

* Adjusted Odds Ratio, a p < 0.05, b p < 0.005, c p < 0.001

65

Table 4. Comparison of the area under the curve (AUC) for each population

Test Result Variable(s) AUC* 95% CI

Models for the entire study population

Predicted probability of Model using FW-Block 0.812 0.791-0.832

Predicted probability of Model using FW-Item 0.817 0.797-0.837

Predicted probability of Model using BW 0.816 0.796-0.836

Models for the dentate participants

Predicted probability of Model using FW-Block 0.813 0.793- 0.833 Predicted probability of Model using FW-Item 0.812 0.792- 0.832 Predicted probability of Model using BW 0.809 0.788- 0.829

Models for the edentulous participants

Predicted probability of Model using F- Block 0.859 0.804-0.914 Predicted probability of Model using FW-Item 0.850 0.795-0.906 Predicted probability of Model using BW 0.863 0.806-0.921

* All AUCs in this table are statistically significant (p<0.001)

66

Figure 3: Theoretical framework for variable selection

5th Horizontal Selection

4th Horizontal Selection 3rd Horizontal Selection 2nd Horizontal Selection 1st Horizontal Selection Outcome

Physical Disability variables

Chronic conditions

Socio-economic variables

Dentate status (main predictor) & Demographic variables

Depression

Oral health variables

67

1.00.80.60.40.20.0

1 - Specificity

1.0

0.8

0.6

0.4

0.2

0.0

Sens

itivi

ty Reference Line

Predicted Probability of FW-Item

Predicted probability of FW-Block

Predicted probability of BW

Source of the Curve

ROC Curve

Diagonal segments are produced by ties.

Figure 4: ROC of the logistic regression models predicting depression for the whole study

population

68

1.00.80.60.40.20.0

1 - Specificity

1.0

0.8

0.6

0.4

0.2

0.0

Sens

itivi

ty Reference Line

Predicted probability of FW-Item

Predicted probability of FW-Block

Predicted probability of BW

Source of the Curve

ROC Curve

Diagonal segments are produced by ties.

Figure 5: ROC of the logistic regression models predicting depression for the dentate participants

69

1.00.80.60.40.20.0

1 - Specificity

1.0

0.8

0.6

0.4

0.2

0.0

Sens

itivi

ty Reference Line

Predicted probability of FW-Item

Predicted probability of FW-Block

Predicted probability of BW

Source of the Curve

ROC Curve

Diagonal segments are produced by ties.

Figure 6: ROC of the logistic regression models predicting depression for the edentulous

participants

70

Chapter 4:

GENERAL DISCUSSION

71

4.1 Methodology

Measurement issues

The prevalence of edentulism and depression in this national sample was 14.4% and

5%, respectively. It is important to be extremely cautious when citing these statistics as

estimates for edentulism and depression in Canadian adults 45 years or older. The

estimates reported are restricted to the sample we selected through specific selection

criteria and, therefore, not absolutely applicable to this age group in Canada. During

synthesis of the data from the Public Use Microdata File (PUMF), we selected only

participants 45 years of age or older, who responded to the DPS, OH2 and other modules

to ensure that we can measure the outcome and main predictor. This step was necessary

because the DPS was an optional module which means not all participants in the original

CCHS had responded to it. Nevertheless, the estimates are in agreement with the

previously reported national statistics. About 9% of Canadians aged 15 or older reported

to the Canadian Community Health Survey in 2003 that they had no natural teeth. This

was down from 16% in 1990 (11). Also, the annual prevalence of MDD was 4.8% as

reported in the CCHS 1.2 (71).

Tables 3, 4 and 5 summarize contents of the OH1, OH2 and DPS modules. It is

noteworthy to indicate that the PUMF does not include data on each item of the DPS

modules. Alternatively, Statistics Canada released data from the DPS modules in a form

of pre-calculated scores. In other words, we were limited to the depression probability

score (0-0.9) according to the CIDI-SFMD without being allowed to view the individual

item responses for the participants.

72

On the contrary, data from the IADL index were released as items and not as a score.

In this case, using an overall score in our analysis would have been more sound

methodologically because the sum score would naturally reduce the variables entering

multivariate analysis and solve the issue of multicolinearity (72).

Logistic regression modeling

Regression modeling is known to be very sensitive to the selection strategies used and

the order of variable entry (73-75). Because we were aware of this challenge prior to

conducting the analysis, we decided to use two strategies to address this issue.

First, we developed a hierarchical framework for variable selection where variables

were systematically grouped into categories for horizontal entry. This strategy ensures a

more systematic approach based on the theoretical relationship between the outcome and

different predictors. Similar approach was described by Nicolau et al. (2003) (76).

Second, we explored different modeling methods and selected the model with the

highest predictability based on the area under the ROC curve. Nine models were

developed; 3 for the entire study population, 3 for the dentate participants and 3 for the

edentulous population. The ROC was constructed to compare among three models

separately for each population. The ROC has typical application in medical research

where it serves as a tool to assess accuracy of diagnostic tests (77). Regression models

can be powerful tools in predicting outcomes when they have good accuracy, a parallel

function to that of a predictive screening test. The area under the ROC curve was also

employed by researchers to test how regression models perform (78-80).

73

We observed high similarity in the variables content, strength of association and the

AUC among the three models. Also, the AUC for all models ranged from a low of 0.809

to as high as 0.863 indicating excellent predictability of those models. These two

observations could be explained by the large sample size available. Moreover, we can

conclude from the results of this study that none of the selection procedures was superior

to the others and each selection method is suitable for variable selection. Instead,

researchers should pay more attention to checking the fit and the performance of the

candidate regression models to select the best model. Careful testing of the model fit is a

crucial step to ensure satisfaction of the regression model assumptions; however, this

subject is beyond the scope of the present paper.

In the next sections, we only discussed the study results of the final models with the

highest predictability (see Table 4 in manuscript #1).

74

4.2 Study results

Depression and dentate status

The main purpose of this study was to determine the association between dentate

status and being depressed. The dentate status was measured by asking participants “do

you have one or more natural teeth?” Response options for this question were ‘yes’ or

‘no’. This question is considered a valid, objective and true measure for the “complete

edentulism” criterion because, conventionally, people would know if they still have any

of their natural teeth. However, we were limited in terms of ensuring better measurement

of the “dentate status” since we used secondary data. Consequently, the major limitation

of this study was the lack of discrimination among participants according to the number

of teeth they have. Such a distinction is very important since a participant who had only

one tooth left is expected to be different than the one with 20 teeth in terms of the extent

of disability. Unfortunately, the survey classifies both participants as dentate regardless

of the severity of disability. Few original studies have examined the relationship between

teeth number and depression, for example, Anttila (2003) found that depressive

symptoms were not associated with the mean number of teeth among middle-aged men or

women (81). In that particular study, the number of teeth was measured as a discrete

variable; however, in another paper Anttila et al. (2001) used the same data set and

presented participants as edentulous and dentates and reached similar conclusions (68). In

both papers, Anttila found no association between tooth loss and depression except for

non-smoker men. Hayashi et al. (2001) used a different definition of tooth loss where

participants were grouped into two categories using a cutoff point of having lost 4 or

75

more teeth that may affect chewing function (67). The results showed no association

between depression and tooth loss, but the study was limited to male adults.

In our study, findings confirmed previous evidence about the lack of association

between edentulism and depression. On the other hand, Drake’s et al. (1995) study

indicated that depression was significantly related to having less depressive symptoms in

Blacks (9), which is in disagreement with the hypothesis that tooth loss could increase

risk of depression. In our study, results from the bivariate analysis indicated that people

who are completely edentulous are less likely to be depressed but this borderline

association became non-significant once the joint effect of potential confounders was

controlled for in the multivariate analysis. This particular finding indicates that

edentulism was not significant correlate of depression and that other factors were more

important correlates of depression in this age group.

Depression and oral health

The model predicting depression for the entire study population included having

dental insurance, avoiding laughing/smiling because of the mouth condition, and

experiencing bleeding gums, dry mouth, and orofacial pain. This is an indication that

individuals experiencing oral symptoms such as bleeding gum, dry mouth, and pain may

be more likely to have depression. Also, dentists should keep in mind the likelihood of

depression when patients report avoiding laughing and smiling due to their oral

conditions because they might be twice as likely to be depressed. Regarding dental

insurance, the model shows that those who have dental insurance are, marginally, more

likely to be depressed.

76

Since our study population included dentate and edentulous individuals, this model

may have poor fit when applied to the edentulous individuals only. This assumption was

confirmed by the obvious difference between the models constructed for each population

separately. Dentate participants had more oral conditions that predict depression

compared to edentulous participants, but, the latter group had higher impact of the

orofacial pain on the probability of suffering depression. Similarly, edentulous

participants who had dental coverage were almost twice as likely to have depression

when compared to those who did not. Initially, we resorted to the apparent difference in

age between dentate and edentulous populations in our study, as shown in Table 2 in

manuscript #1, to explain the difference between the models for each group. The

bivariate analysis indicated that as age increased, edentulism prevalence increased but

depression prevalence decreased steadily. Yet, the interaction term between age,

edentulism and depression was not significant. This led us to become more inclined to

believe that retaining teeth is the only explanation for more dental-related predictors in

the dentate regression model. Specifically, depression was associated with symptoms

related to “teeth” in the dentate population.

Previous studies found that there is a relationship between depression and a range of

oral conditions. Dry mouth, temporomandibular joint pain and dysfunction syndrome,

burning mouth syndrome, and recurrent aphthous stomatitis have been linked to

depression (60-62).

77

Other correlates of depression

Our findings were consistent with previous evidence supporting the positive

relationship between depression and being female, younger, having less income, and

suffering chronic conditions and physical disabilities. The evidence was summarized and

presented in Table 2 at the tables section.

Although smoking was not related to depression or edentulism at the bivariate level,

we included it in the multivariate modeling because there is evidence supporting the

relationship among smoking, tooth loss and depression (68). Also, experts advise against

using the bivariate associations as a method of screening risk factors for use in

multivariate analysis because important predictors may be excluded if their effect is

masked by another (75). Nevertheless, smoking was not retained in any of the models

indicating that it was not an important correlate of depression in our sample. This finding

was in contrast to that reported by Ohayon et al. (1999) who showed a dose-response

relationship where the risk of depression increased with an increase in the number of

cigarettes consumed (82).

Finally, it is important to elaborate on the discrepancy in depression measurement

among epidemiological surveys, which is considered a huge limitation of cross-studies

comparability. In our study, depression was measured using what is considered a scale -

score; however, measurement of depression varied across the other studies we cited, from

number of symptoms to continuous/scale-score system. The scale used in the original

CCHS survey, and subsequently in our study, was the CIDI-SFMD; however, other

studies used the ZSDS, HAD, and BDI (59-61). Moreover, we decided to choose an

78

extreme cutoff point (90% probability of depression) based on the previous work of

Patten et al. (83) to ensure the accuracy of identifying cases of depression.

It would have been interesting to explore the relationship between the two stem

depression symptoms, feeling depressed or loosing interest in things. Unfortunately,

Statistics Canada restricts access to individual items of the CIDI-SFMD to prevent the

release of estimates which may contain distortion of data.

79

Chapter 5:

SUMMARY AND CONCLUSION

80

Depression is a public health problem because it is a major cause of disability

globally, ranks fourth in the ten leading causes of global burden of disease and is the

leading cause for suicide (36). Individuals who suffer depression are at high risk of

severe relapse in the next 10 years if they do not receive proper medical attention (35).

Fortunately, several therapies are available with high success rate. Depression should be

one of the conditions included in the medical history taking prior to dental treatment. One

study has shown that self-reported depression was second only to hypertension in

frequency of all diseases and disorders recorded in the medical history (84). Dental

professionals need to be aware of what oral health factors are associated with depression

in order to identify those at high risk. Findings from this study indicate that having dental

symptoms such as dry mouth, pain and cosmetic concerns that interfere with social

activities are linked with depression, but being completely edentulous, without these

symptoms, was not found to be a risk factor for depression. Dental factors along with

other factors known to increase the probability of depression should be kept in mind and

used as screening tools to identify potentially depressed patients visiting the dental office.

Having said so, it is important to be always cautious when extrapolating clinical data, in

this instance clinical correlates of depression, from community surveys due to the

methodological limitations inherited to such surveys.

Nevertheless, dental professionals are encouraged to refer potential patients for

psychiatric and mental health care and utilize already available community and local

resources targeted to prevent and treat depression. An inter-disciplinary approach is an

important determinant of successful, long term dental intervention outcomes. This

approach has a significant implication in dentistry given that depressed patients require

81

special management because of the negative behavioral and biological impacts of

depression (85). Depressed patients may be uncooperative and irritable during dental

treatment, appear unappreciative and have numerous complaints that are inconsistent

with objective findings. Also, precautions should be taken to avoid adverse interaction

between antidepressants and some medications used in dentistry. Depressed patients may

be more likely to view efficacious interventions as less beneficial for them because

depression is known to influence major outcomes such as satisfaction with TMD

treatment success (86), periodontal treatment outcome (87), or dental fear treatment (88).

Recently, John et al reported that depression has negative effects on the satisfaction of

older patients with their dentures (89).

Therefore, we highly recommend that dental educators should include depression in

the dental curriculum; especially how to identify undiagnosed depressed individuals in

the dental office. Also, some patients who receive psychiatric treatment for depression

may be reluctant to admit it because of the perceived stigma associated with mental

illness (85). Hence, it is important to teach dental students interaction tactics to overcome

those barriers such as how to exhibit a supportive, nonjudgmental attitude and advise

patients that such information will be held confidential and is indispensable to the

provision of safe dental care (85).

As a final note, we found that the lack of association between depression and

edentulism is a counter-intuitive. However, this finding was consistent with what has

been previously reported. We suspect that depression could be more related to the

incidence of edentulism rather than the prevalence. In another word, we hypothesized that

depression could manifest with rapid onset of edentulism where the individual does not

82

have enough time to adapt to the condition. The previous studies that investigated the

relationship between depression and edentulism did not control for the onset of

edentulism. Unfortunately, the CCHS surveys do not contain items that allow us to

assess this hypothesis as well.

The CCHS surveys do not contain clinical data however some of the oral health items

can be improved. Thus, we believe that Statistics Canada could benefit from this study by

introducing modifications to the oral health modules in order to enhance research in this

area. Specifically, transforming the item OH2_22 (Do you have one or more of your own

teeth? Yes/no) to account for the number of remaining natural teeth in such away that it

can be grouped into 3 or 4 categories and also indicating whether or not there is at least

one opposing pair of teeth for those retaining their natural teeth. It will be also valuable to

record whether dentures are conventional or implant-supported. We expect that such

modifications can be introduced without compromising the validity of these questions.

83

REFERENCES

1. US Department of Health and Human Services. Oral health in America: a report

of the Surgeon General. Rockville (MD): US Department of Health and Human

Services, national Institute of Dental and Craniofacial Research, National Institute

of Health, 2000.

2. Kelly M, Steele J, Nuttall N, Bradnock G, Morris J, Nunn J, et al. Adult Dental

Health Survey: Oral Health in the United Kingdom 1998. London: The Stationery

Office. 2000.

3. World Health Organization. WHO oral health country/ area profile programme.

Malmo University, Sweden: WHO Division of Non-communicable Disease/Oral

Health, WHO Collaborating Center; 2003.

4. Douglass CW, Shih A, Ostry L. Will there be a need for complete dentures in the

United States in 2020? J Prosthet Dent 2002;87(1):5–8.

5. Jones JA, Orner MB, Spiro III A, Kressin NR. Tooth loss and dentures: patients’

perspectives. Int Dent J 2003; 53: 327-34.

6. Chalmers JM, Ettinger RL, Thomson WM, Spencer AJ. Aging and dental health.

Australian Institute of Health and Welfare. Dental Statistics and Research Series

1999; Number 19.

7. Beltrán-Aguilar ED, Baker LK, Canto MT, Dye BA, Gooch BF, Griffin SO and

others. Surveillance for Dental Caries, Dental Sealants, Tooth Retention,

Edentulism, and Enamel Fluorosis -United States, 1988-1994 and 1999-2002.

MMWR August 26, 2005; 54(03):1-44.

84

8. Slade GD, Gansky SA, Spencer AJ. Two-year incidence of tooth loss among

South Australians aged 60+ years. Community Dent Oral Epidemiol. 1997

Dec;25(6):429-37.

9. Drake CW, Hunt RJ, Koch GG. Three-year tooth loss among black and white

older adults in North Carolina. J Dent Res. 1995; 74(2):675-80.

10. Hunt RJ, Drake CW, Beck JD. Eighteen-month incidence of tooth loss among

older adults in North Carolina. Am J Public Health. 1995 Apr;85(4):561-63.

11. Statistic Canada, Health Reports. Edentulism in Canada. The Daily, November

16, 2005. November issue of Health Reports, Vol. 17, No. 1.

12. Atchison KA, Dolan TA. Development of the Geriatric Oral Health Assessment

Index. J Dent Educ. 1990 Nov;54(11):680-87.

13. Joshipura K, Ritchie C, Douglass C. Strength of evidence linking oral conditions

and systemic disease. Compend Contin Educ Dent Suppl. 2000;(30):12-23.

14. Slade GD, Spencer AJ. Development and evaluation of the Oral Health Impact

Profile. Community Dent Health. 1994 Mar;11(1):3-11.

15. Hutton B, Feine J, Morais J. Is there an association between edentulism and

nutritional state? J Can Assoc 2002; 68(3): 182-87.

16. Wayler AH, Chauncey HH. Impact of complete dentures and impaired natural

dentition on masticatory performance and food choice in healthy aging men. J

Prosthet Dent 1983; 49(3): 427-33.

17. Hartsook EI. Food selection, dietary adequacy, and related dental problems of

patients with dental prostheses. J Prosthet Dent 1974; 32(1): 32-40.

85

18. Joshipura KH, Willett WC, Douglass CW. The impact of edentulousness on food

and nutrient intake. J Am Dent Assoc 1996; 127(4): 459-67.

19. Österberg T, Steen B. relationship between dental state and dietary intake in 70-

years-old males and females in Göteborg, Sweden: a population study. J Oral

Rehabil 1982; 9(6): 509-21.

20. Sheiham A, Steele JG, Marcenes W, Lowe C, Finch S, Bates CJ, and others. The

relationship among dental status, nutrient intake, and nutritional status of older

people. J Dent Res 2001; 80(2): 403-13.

21. Smith JM, Sheiham A. How dental conditions handicap the elderly. Community

Dent Oral Epidemiol 1979; 7(6): 305-10.

22. Fiske J, Davis DM, Frances C, Gelbier S. The emotional effects of tooth loss on

edentulous people. Br Dent J 1998; 184(2):90-3.

23. Bergendal B. The relative importance of tooth loss and denture wearing in

Swedish adults. Community Dent Health 1989; 6: 103-11.

24. Al Shamrany M. Oral health-related quality of life: a broader perspective. East

Mediterr Health J 2006; 12(6):894-901.

25. Steele JG, Sanders AE, Slade GD, Allen PF, Lahti S, Nuttal N, Spencer AJ. How

do age and tooth loss affect oral health impacts and quality of life? A study

comparing two national samples. Community Dent Oral Epidemiol 2004; 32:

107-14.

26. Tsakos G, Steele JG, Marcenes W, Walls AWG, Sheiham A. Clinical correlates

of oral health-related quality of life: evidence from a national sample of British

older people. Eur J Oral Sci 206; 114: 391-95.

86

27. Astrøm AN, Haugejorden O, Skaret E, Trovik TA, Klock KS. Oral Impacts on

Daily Performance in Norwegian adults: the influence of age, number of missing

teeth, and socio-demographic factors. Eur J Oral Sci 2006; 114: 115–21.

28. Locker D. Measuring oral health: a conceptual framework. Community Dent

Health. 1988;5(1):3-18.

29. Kressin NR, Spiro A 3rd, Atchison KA, Kazis L, Jones JA. Is depressive

symptomatology associated with worse oral functioning and well-being among

older adults? J Public Health Dent 2002; 62(1):5-12.

30. The World Health Report 2001 - Mental Health: New Understanding, New Hope.

Geneva World Health Organization, 2001.

31. American Psychiatric Association. Diagnostic and statistical manual of mental

disorders, 3rd edn. American Psychiatric Association, Washington, DC;1980.

32. American Psychiatric Association. Diagnostic and statistical manual of mental

disorders, 3rd edn. revised. American Psychiatric Association, Washington,

DC;1987.

33. American Psychiatric Association. Diagnostic and statistical manual of mental

disorders, 4th edn. American Psychiatric Association, Washington, DC;1994.

34. American Psychiatric Association. Diagnostic and statistical manual of mental

disorders, 4th edn., text revision. American Psychiatric Association, Washington,

DC; 2000.

35. Parikh SV, Lam RW. Clinical Guidelines for the Treatment of Depressive

Disorders. I. Definitions, Prevalence, and Health Burden. Available from:

http://www.cpa-

87

apc.org/Publications/Clinical_Guidelines/depression/clinicalGuidelinesDepressio

n.asp/

36. Murray CJL, Salomon JA, Mathers CD, Lopez AD. Summary measures of

population health: concepts, ethics, measurement and applications. (eds.) (2002).

WHO, Geneva.

37. Weissman MM, Bland RC, Canino GJ, et al. Cross-national epidemiology of

major depression and bipolar disorder. JAMA 1996;276(4):293–99.

38. Patten SB, Wang JL, Beck CA, Maxwell CJ. Measurement issues related to the

evaluation and monitoring of major depression prevalence in Canada. Chronic Dis

Can. 2005 Fall;26(4):100-6.

39. Kessler RC, Ustun TB. The World Mental Health (WMH) Survey Initiative

Version of the World Health Organization (WHO) Composite International

Diagnostic Interview (CIDI). Int J Methods Psychiatr Res. 2004;13(2):93-121.

40. Regier DA, Boyd JH, Burke JD Jr, Rae DS, Myers JK, Kramer M, One month

prevalence of mental disorders in the United States. Based on 5 epidemiologic

catchments area sites. Arch Gen Psychiatry 1988;45:977–86.

41. Bland RC, Newman SC, Orn H. Prevalence of psychiatric disorders in the elderly

in Edmonton. Acta Psychiatr Scand Suppl 1988;338:57–63.

42. National population health survey–public use micro data files–house hold

component. Ottawa: Statistics Canada; 1998. Cat. No. 82M0009XCB.

43. Katona C, Livingston G. Impact of screening old people with physical illness for

depression? Lancet. 2000 Jul 8;356(9224):91-2.

88

44. Aromaa A, Raitasalo R, Reunanen A, Impivaara O, Heliovaara M, Knekt P, et al.

Depression and cardiovascular diseases. Acta Psychiatr Scand Suppl.

1994;377:77-82.

45. Pratt LA, Ford DE, Crum RM, Armenian HK, Gallo JJ & Eaton WW. Depression,

psychotropic medication, and risk of myocardial infarction. Circulation 1996;94:

3123–29.

46. Anderson RJ, Freedland KE, Clouse RE & Lustman PJ. The prevalence of

comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care

2001;24: 1069–78.

47. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960

Feb;23:56-62.

48. Hamilton M. Development of a rating scale for primary depressive illness. Br J

Soc Clin Psychol. 1967 Dec;6(4):278-96.

49. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for

measuring depression. Arch Gen Psychiatry. 1961 Jun;4:561-71.

50. Zung WW. A Self-rating depression scale. Arch Gen Psychiatry. 1965 Jan;12:63-

70.

51. Radloff LS. The CES-D scale: a self-report depression scale for research in the

general population. Appl Psychol Measurement 1977;1: 385–401.

52. Wing JK, Cooper JE & Sartorius N. The measurement and classification of

Psychiatric Symptoms: An instructional manual for the PSE and CATEGO

Program. Cambridge University Press, New York;1974.

89

53. Robins LN, Helzer JE, Croughan J & Ratcliff KS. National Institute of Mental

Health Diagnostic Interview Schedule. Its history, characteristics, and validity.

Arch Gen Psychiatry 1981;38: 381–89.

54. World Health Organization. Composite International Diagnostic Interview

(CIDI). WHO, Division of Mental Health 1990, Geneva.

55. World Health Organization. SCAN: Schedules for Clinical Assessment in

Neuropsychiatry. WHO 1994, Geneva.

56. Kessler RC, Andrews G, Mroczek D, Üstün TB, Wittchen H-U. The World

Health Organization Composite International Diagnostic Interview Short-Form

(CIDI-SF). Int J Methods Psychiatr Res. 1998;7:171-85.

57. Patten SB. Performance of the Composite International Diagnostic Interview

Short Form for major depression in community and clinical samples. Chronic Dis

Can. 1997;18(3):109-12.

58. Striner DL, Normal GR. Validity. In: Striner DL, Normal GR. Health

measurement scales. A practical guide to their development and use (2nd ed). New

York, NY, Oxford University Press, Oxford. 1995.

59. Sheiham A, Cushing AM, Maizels J. the social impacts of dental disease. In:

Slade GD. Measuring oral heath and quality of life. University of North Carolina,

Dental Ecology, Chapel Hill. 1997.

60. Anttila SS, Knuuttila ML, Sakki TK. Depressive symptoms as an underlying

factor of the sensation of dry mouth. Psychosom Med. 1998 Mar-Apr;60(2):215-

18.

90

61. Soto Araya M, Rojas Alcayaga G, Esguep A. Association between psychological

disorders and the presence of Oral lichen planus, Burning mouth syndrome and

Recurrent aphthous stomatitis. Med Oral. 2004; Jan-Feb;9(1):1-7.

62. Gatchel RJ, Stowell AW, Buschang P. The relationships among depression, pain,

and masticatory functioning in temporomandibular disorder patients. J Orofac

Pain. 2006 Fall;20(4):288-96.

63. Monteiro da Silva AM, Oakley DA, Newman HN, Nohl FS, Lloyd HM.

Psychological factors and adults onset rapidly progressive periodontitis. J Clin

Periodontol 1996; 23: 789-94.

64. Saletu A, Pirker-Fru¨hauf H, Saletu F, Linzmayer L, Anderer P, Matejka M.

Controlled clinical and psychometric studies on the relation between periodontitis

and depressive mood. J Clin Periodontol 2005; 32: 1219–25.

65. Solis ACO, Lotufo RFM, Pannuti CM, Brunheiro EC, Marques AH, Lotufo-Neto

F: Association of periodontal disease to anxiety and depression symptoms, and

psychosocial stress factors. J Clin Periodontol 2004; 31: 633–38.

66. Persson GR, Persson RE, MacEntee CI, Wyatt CCJI, Hollender LG, Kiyak HA:

Periodontitis and perceived risk for periodontitis in elders with evidence of

depression. J Clin Periodontol 2003; 30: 691–96.

67. Hayashi N, Tamagawa H, Tanaka M, Hanioka T, Maruyama S, Takeshita T and

others. Association of tooth loss with psychological factors in male Japanese

employee. J Occup Health 2001; 43:351-55.

91

68. Anttila SS, Knuuttila MLE & Sakki TK. Relationship of depressive symptoms to

edentulousness, dental health, and dental health behavior. Acta Odontol Scand

2001; 59:406–12.

69. Heydecke G, Locker D, Awad MA, Lund JP, Feine JS. Oral and general health-

related quality of life with conventional and implant dentures. Community Dent

Oral Epidemiol. 2003 Jun;31(3):161-8.

70. Canadian Community Health Survey 2003. User Guide for the Public Use

Microdata File. Statistics Canada, Catalogue no. 82M0021GPE.

71. Patten SB, Wang JL, Williams J VA, Currie S, Beck CA, Maxwell CJ, el-Guebaly

N. Descriptive Epidemiology of Major Depression in Canada. Can J Psychiatry

2006; 51(2): 84-90.

72. Dohoo IR, Ducrot C, Fourichon C, Donald A, Hurnik D. An overview of

techniques for dealing with large numbers of independent variables in

epidemiologic studies. Preventive Veterinary Medicine 1997; 29(3):221-239.

73. Lagakos SW. Effects of mismodeling and mismeasuring explanatory variables on

tests of their association with a response variable. Stat Med 1988; 7:257-74.

74. Vandenbroucke JP. Should we abandon statistical modeling altogether? Am J

Epidemiol 1987; 126:10-13.

75. Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen

risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49:907-16.

76. Nicolau B,Marcenes W; Bartley M; Sheiham, A. A Life Course Approach to

Assessing Causes of Dental Caries Experience: The Relationship between

92

Biological, Behavioural, Socio-Economic and Psychological Conditions and

Caries in Adolescents. Caries Res 2003;37:319-326.

77. Pape MS. Receiver operating characteristic methodology. J Am Stat Assoc 2000;

95: 308-11.

78. Hedgewald J, Pfahlberg A, Uter W. A backwards-manual selection macro for

binary logistic regression in the SAS v.8.02 PROC LOGESTIC procedure.

Available from the URL:

< http://www.imbe.med.uni-erlangen.de/issan/SAS/bms/bms_manuel.pdf >

79. SPSS 15.0 Base User’s Guide. SPSS.

80. Jaimes F, Farbiarz J, Alvarez D, Marines C. Comparison between logistic

regression and neural networks to predict death in patients with suspected sepsis

in the emergency room. Cri Care 2005; 9(2):R150-55.

81. Anttila SS. Depressive symptoms in relation to oral health and related factors in a

middle-aged population. Analytical approach. 2003 (thesis) Available from:

http://herkules.oulu.fi/isbn9514270215/

82. Ohayon MM, Priest RG, Guilleminault C, Caulet M. The prevalence of

depressive disorders in the United Kingdom. Biol Psychiatry 1999; 45:300-07.

83. Patten SB, Beck CA, Kassam A, Williams JV, Barbui C, Metz LM. Long-term

medical conditions and major depression: strength of association for specific

conditions in the general population. Can J Psychiatry. 2005 Mar; 50(4):195-202.

84. Woods CD. Self-Reported Mental Illness in a Dental School Clinic Population.

Journal of Dental Education 2003; 67(5): 500-504.

93

85. Friedlander AH, Mahler ME. Major depressive disorder. Psychopathology,

medical management and dental implications. JADA 2001; 132: 629-38.

86. Riley JL 3rd, Myers CD, Robinson ME, Bulcourf B, Gremillion HA. Factors

predicting orofacial pain patients satisfaction with improvement. J Orofac Pain

2001; 15:29-35.

87. Elter JR, White BA, Gaynes BN, Bader JD. Relationship of clinical depression to

periodontal treatment outcome. J Periodontal 2002; 73:441:9.

88. Abrahamsson KH, Berggen U, Hakeberg M, Carlsson SG. The importance of

dental beliefs for the outcome of dental-fear treatment. Euro J Oral Sci 2003;

111:99-105.

89. John M, Micheelis W, Steele J G. Depression as a Risk Factor for Denture

Dissatisfaction. Journal of Dental Research; Sep 2007; 86(9): 852-7.

94

TABLES

95

Table 1. Cross-national lifetime prevalence of Major Depression Disorder*

Lifetime Ratio/ 100

Country Overall Females Males F/M Ratio

Mean Age at

Onset United States (ECA, 1980) 5.2 7.4 2.8 2.6 25.6

United States (NCS, 1990) 17.1 21.3 12.7 1.7 23.8

Canada 9.6 12.3 6.8 1.9 24.8 Puerto Rico 4.3 5.5 3.1 1.8 29.5 France 16.4 21.9 10.5 2.1 29.2 Germany 9.2 13.5 4.4 3.1 29.7 Italy 12.4 18.1 6.1 3.0 34.8 Lebanon 19.0 23.1 14.7 1.6 25.2 Taiwan (Taipei) 1.5 1.8 1.1 1.6 29.3 Korea (Seoul) 2.9 3.8 1.9 2.0 29.3 New Zealand 11.6 15.5 7.5 2.1 27.3

ECA: Epidemiologic Catchment Area Study, NCA: National Comorbidity Survey.

All studies used the Diagnostic Interview Schedule (DIS) with the exception of the NCS, which used the

Composite International Diagnostic Interview (CIDI)

* Source: Parikh SV, Lam RW. Clinical Guidelines for the Treatment of Depressive Disorders. I.

Definitions, Prevalence, and Health Burden.

96

Tab

le 2

. Dep

ress

ive

sym

ptom

s and

thei

r epi

dem

iolo

gic

corr

elat

es

Aut

hor(

s)

Stud

y de

sign

O

bjec

tive

Po

pula

tion

Mea

sure

men

t Fi

ndin

gs

Rem

arks

Cana

da

Patte

n SB

,

1999

1

Cro

ss-s

ectio

nal

• To

eval

uate

asso

ciat

ion

betw

een

long

-term

med

ical

cond

ition

s and

MD

• Exa

min

e th

e

impa

ct o

f the

se

cond

ition

s on:

1.

Hea

lth

care

serv

ice

use

2.

Dis

abili

ty

• Su

bjec

ts o

ver 1

2yrs

who

par

ticip

ated

in

the

first

wav

e of

NPH

S w

ho h

ad M

D.

• D

epre

ssio

n pr

eval

ence

: CID

I-

SFM

D, u

sing

pred

ictiv

e

instr

umen

t (+

at 9

0% c

ut-o

ff)

• Lo

ng-te

m m

edic

al c

ondi

tion:

self-

repo

rted,

not

inde

pend

ently

con

firm

ed b

y

the

inte

rvie

wer

s, ho

wev

er, i

t

was

spec

ified

that

DX

shou

ld

have

bee

n m

ade

by a

hea

lth

prof

essi

onal

.

• Ove

rall

prev

alen

ce o

f MD

=

6.7%

• Mos

t chr

onic

con

ditio

ns

eval

uate

d in

the

NPH

S

wer

e as

soci

ated

with

stat

istic

al si

gnifi

canc

e

incr

ease

in M

D p

reva

lenc

e.

• Mig

rain

e he

adac

hes a

nd

stom

ach

or in

test

inal

ulc

ers

had

the

stron

gest

asso

ciat

ion

with

MD

.

• Th

ose

who

hav

e M

D w

ith

co-m

orbi

d co

nditi

ons d

id

NO

T ha

ve e

xces

sive

hea

lth

serv

ices

util

izat

ion

BUT

had

mor

e di

sabi

lity

days

than

the

othe

r gro

up.

• Evi

denc

e le

vel I

I-3

• Sec

onda

ry a

naly

sis

• Sta

tistic

s: pr

eval

ence

Rat

io

and

Prev

alen

ce d

iffer

ence

.

• Pr

oble

m w

ith a

naly

sis d

ue

to sm

all s

ize

cells

.

• Con

trolle

d fo

r con

foun

ding

effe

ct o

f age

and

gen

der.

Patte

n SB

,

2000

2

Cro

ss-s

ectio

nal

surv

ey

To e

valu

ate

asso

ciat

ion

betw

een

depr

essi

ve

epis

odes

and

a se

t

• Su

bjec

ts ≥

18

yrs

who

wer

e re

side

nts

of te

leph

one-

cont

aini

ng

• D

epre

ssio

n pr

eval

ence

: CID

I-

SFM

D (2

bran

ches

: 2w

ks o

f

depr

esse

d m

ood

vs. 2

wks

of

loss

of i

nter

est)

• D

epre

ssio

n pr

eval

ence

:

15.6

%.

• Bi

olog

ical

cor

rela

tes:

stre

et

drug

s, be

ing

fem

ale,

fam

ily

• Evi

denc

e le

vel I

I-3

• Sta

tistic

s: pr

eval

ence

Rat

io.

Con

foun

ding

by

age

and

sex

cont

rolle

d fo

r in

97

of b

io-p

sych

o-

soci

al d

eter

min

ants

in C

alga

ry,

Can

ada.

hous

ehol

d in

Cal

gary

.

• Po

tent

ial d

eter

min

ants

: sel

f-

repo

rted.

Hx

then

alc

ohol

.

• So

cial

cor

rela

tes:

low

inco

me,

less

edu

catio

n,

mar

ital s

tatu

s, la

ck o

f

soci

al su

ppor

t, th

en

disa

bilit

y or

unem

ploy

men

t.

• Psy

chol

ogic

al c

orre

late

s: #

of R

LE \

goin

g on

wel

fare

,

incr

ease

d ar

gum

ent w

ith

partn

er, u

nwan

ted

preg

nanc

y th

en m

ajor

finan

cial

cris

is ha

d th

e

stro

nges

t ass

ocia

tion.

logi

stic

regr

essi

on m

odel

.

• Val

idity

of t

he C

IDI-

SFM

D:

posi

tive

pred

ictiv

e va

lue

is

betw

een

75-9

0%. N

egat

ive

pred

ictiv

e va

lue

is n

early

100

%.

Patte

n SB

,

2001

3

Coh

ort

With

a

com

paris

on

grou

p.

To d

eter

min

e

whe

ther

subj

ects

with

one

or m

ore

long

-term

med

ical

cond

ition

had

an

elev

ated

inci

denc

e

of m

ajor

depr

essi

on.

• Sub

ject

s ove

r 12y

rs

who

par

ticip

ated

in

the

first

and

seco

nd

wav

es o

f NPH

S.

• “N

on-e

xpos

ed

grou

p”: n

o m

ajor

depr

essi

on +

no

long

-term

med

ical

cond

ition

s

• “Ex

pose

d gr

oup”

: no

• Dep

ress

ion

Inci

denc

e: C

IDI-

SFM

D, u

sing

pred

ictiv

e

instr

umen

t (+

at 9

0% c

ut-o

ff)

• Lon

g-te

m m

edic

al c

ondi

tion:

self-

repo

rted,

not

inde

pend

ently

con

firm

ed b

y

the

inte

rvie

wer

s, ho

wev

er, i

t

was

spec

ified

that

DX

shou

ld

have

bee

n m

ade

by a

hea

lth

prof

essi

onal

.

• Ove

rall

inci

denc

e of

MD

=

3.5%

• H

avin

g a

on-te

rm m

edic

al

cond

ition

~ d

oubl

e th

e ris

k

of M

D

• Mig

rain

e he

adac

hes,

sinu

sitis

and

bac

k pa

in h

ad

the

stron

gest

ass

ocia

tion

with

MD

.

• Evi

denc

e le

vel I

I-2

• Sec

onda

ry a

naly

sis

• Sta

tistic

s: C

umul

ativ

e

Inci

denc

e Ra

tio.

• Pro

blem

with

ana

lysi

s due

to sm

all s

ize

cells

.

• Ins

ensi

tive

mea

sure

of

depr

essi

on (1

2 vs

. 24

mon

ths)

fa

lse

nega

tives

• Sel

f-rep

orte

d da

ta

98

maj

or d

epre

ssio

n +

yes o

ne o

r mor

e

long

-term

med

ical

cond

ition

(s).

• C

ontro

lled

for c

onfo

undi

ng

effe

ct o

f age

and

gen

der i

n

a lo

gist

ic re

gres

sion

mod

el.

Patte

n SB

et a

l,

2001

4

Cro

ss-s

ectio

nal

surv

ey

To e

valu

ate

asso

ciat

ion

betw

een

MD

and

long

-term

med

ical

cond

ition

s in

Cal

gary

, Can

ada.

Subj

ects

≥ 1

8 yr

s

who

wer

e re

side

nts o

f

tele

phon

e-co

ntai

ning

hous

ehol

d in

Cal

gary

.

• D

epre

ssio

n pr

eval

ence

: CID

I-

SFM

D (2

bran

ches

: 2w

ks o

f

depr

esse

d m

ood

vs. 2

wks

of

loss

of i

nter

est)

• Lon

g-te

m m

edic

al c

ondi

tion:

self-

repo

rted,

not

inde

pend

ently

con

firm

ed b

y

the

inte

rvie

wer

s, ho

wev

er, i

t

was

spec

ified

that

DX

shou

ld

have

bee

n m

ade

by a

hea

lth

prof

essi

onal

.

• 21%

had

MD

with

≥1

long

-

term

med

ical

con

ditio

n

com

pare

d to

15%

who

had

MD

with

out l

ong-

term

med

ical

con

ditio

n.

• Hav

ing ≥1

long

-term

med

ical

con

ditio

n w

as

asso

ciat

ed w

ith in

crea

sed

prev

alen

ce o

f MD

(PR=

1.44

, 95%

CI 1

.17-

1.76

).

• Th

is a

ssoc

iatio

n re

mai

ned

stro

ng a

fter s

tratif

icat

ion

by

age,

gen

der,

empl

oym

ent,

soci

al su

ppor

t and

stre

ssfu

l

life

even

ts, E

XCE

PT in

subj

ects

age

d ≥

45yr

s.

• Evi

denc

e le

vel I

I-3

• Sta

tistic

s: pr

eval

ence

Rat

io.

• Con

foun

ding

or m

odify

ing

effe

ct b

y ag

e, g

ende

r,

empl

oym

ent,

soci

al su

ppor

t

and

stres

sful

life

eve

nts

cont

rolle

d fo

r by

stra

tific

atio

n an

alys

is.

Patte

n SB

et a

l,

2005

5

Cro

ss-s

ectio

nal

surv

ey

To e

xplo

re

prev

alen

ce o

f MD

in th

e ge

nera

l

popu

latio

n in

• Sub

ject

s ≥ 1

8yrs

who

parti

cipa

ted

in th

e

CC

HS

Cyc

le 1

.2.

• D

epre

ssio

n pr

eval

ence

: CID

I-

SFM

D p

redi

ctiv

e in

strum

ent.

• Lo

ng-te

m m

edic

al c

ondi

tion:

self-

repo

rted,

not

• Ann

ual M

D p

reva

lenc

e w

as

7.4%

(95%

CI=

7.2-

7.6%

).

• MD

pre

vale

nce

in th

ose

with

≥ 1

long

-term

• Evi

denc

e le

vel I

I-3

• sec

onda

ry a

naly

sis

• Sta

tistic

s: Pr

eval

ence

• Rep

orte

d pr

oble

ms w

ith

99

rela

tion

to v

ario

us

long

-term

med

ical

cond

ition

s.

inde

pend

ently

con

firm

ed b

y

the

inte

rvie

wer

s, ho

wev

er, i

t

was

spec

ified

that

DX

shou

ld

have

bee

n m

ade

by a

hea

lth

prof

essi

onal

.

cond

ition

was

9.2

% (9

5%

CI=

8-9.

4%) c

ompa

red

to

4% (9

5% C

I= 3

.7-4

.3%

)

for t

hose

with

out

cond

ition

(s).

logi

stic

regr

essi

on (O

Rs)

anal

ysis

due

to d

ata

rele

ase

proh

ibiti

ons p

ut in

pla

ce b

y

Stat

Can

to d

eter

the

rele

ase

of h

ighl

y im

prec

ise

estim

ates

.

Stei

n M

B

et a

l,

2006

6

Cro

ss-s

ectio

nal

surv

ey

To e

xam

ine

the

rela

tive

and

com

bine

d im

pact

of d

epre

ssiv

e an

d

chro

nic

phys

ical

cond

ition

s on

func

tiona

l sta

tus

and

heal

th c

are

use

in th

e ge

nera

l

popu

latio

n.

Subj

ect ≥

12yr

s who

parti

cipa

ted

in th

e

CC

HS

Cyc

le 1

.2.

• M

D p

reva

lenc

e: th

e C

IDI-

SFM

D p

redi

ctiv

e in

strum

ent

(at 9

0% p

roba

bilit

y).

• O

ther

cor

rela

tes:

self-

repo

rted.

• The

pre

senc

e of

co-

mor

bid

MD

was

ass

ocia

ted

with

sign

ifica

ntly

gre

ater

(~ X

2)

likel

ihoo

d of

hea

lth c

are

utili

zatio

n an

d in

crea

sed

func

tiona

l disa

bilit

y an

d

wor

k ab

senc

e co

mpa

red

to

the

pres

ence

of a

chr

onic

phys

ical

illn

ess w

ithou

t

MD

.

• Evi

denc

e le

vel I

I-3

• Sec

onda

ry a

naly

sis

• Sta

tistic

s: A

ORs

\ log

istic

regr

essi

on.

• Con

serv

ativ

e P=

0.00

5 w

as

used

due

to la

rge

sam

ple

size

and

mul

tiple

com

paris

ons.

• Det

aile

d sta

tistic

al a

naly

ses

wer

e re

porte

d in

clud

ing

mis

sing

data

.

USA

Regi

er

et a

l,

1984

7

(EC

A)

Cro

ss-s

ectio

nal

surv

ey

To e

stim

ate

6-

mon

ths p

reva

lenc

e

of M

D in

the

gene

ral p

opul

atio

n

Subj

ects

≥18

yrs

who

parti

cipa

ted

in th

e

Epid

emio

logi

c

Cat

chm

ent A

rea

(EC

A) s

tudy

in

Balti

mor

e, M

d.

• M

D p

reva

lenc

e: D

iagn

ostic

Inte

rvie

w S

ched

ule

(DIS

)

base

d on

DSM

-III

• 6-m

onth

s MD

pre

vale

nce

was

2.2

%.

• MD

pre

vale

nce

is lo

wer

(%)

for ≥

65y

rs.

• Evi

denc

e le

vel I

I-2

• Sec

onda

ry a

naly

sis

• Sta

tistic

s: Pr

eval

ence

• Met

hodo

logy

was

crit

iciz

ed

10

0

Blaz

er

et a

l,

1994

8

(NC

S)

Cro

ss-s

ectio

nal

surv

ey

To e

stim

ate

prev

alen

ce

distr

ibut

ion

and

risk

fact

ors f

or M

D

in n

atio

nal

com

mun

ity sa

mpl

e.

Subj

ects

15-

54 y

rs o

f

age

who

par

ticip

ated

in th

e N

atio

nal C

o-

mor

bidi

ty S

urve

y

(NC

S).

• M

D p

reva

lenc

e:

CID

I.

• Oth

er c

orre

late

s: se

lf-re

porte

d.

• Ove

rall

curr

ent 3

0-da

ys

prev

alen

ce o

f MD

was

2.2%

.

• The

hig

hest

MD

pre

vale

nce

was

in th

e 15

-24

age

(6.1

%) w

ith a

smal

ler p

eak

in th

e 35

-44

age

band

(5.3

%)

• Cor

rela

tes a

re: b

eing

fem

ale,

low

er e

duca

tion,

low

er in

com

e.

• Evi

denc

e le

vel I

I-2

• Sec

onda

ry a

naly

sis

• Sta

tistic

s: Pr

eval

ence

&

AO

Rs\ l

ogis

tic re

gres

sion

.

UK

Jenk

ins e

t

al, 1

9989

(BPM

S)

Cro

ss-s

ectio

nal

surv

ey

To p

rovi

de d

ata

on

the

prev

alen

ce a

nd

corr

elat

es o

f

psyc

hiat

ric d

isor

der

on a

nat

ionw

ide

sam

ple.

Subj

ects

16-

65 y

rs o

f

age

who

par

ticip

ated

in th

e Br

itish

Psyc

hiat

ric M

orbi

dity

Surv

ey

(BPM

S).

• 1-w

k M

D p

reva

lenc

e:

by th

e

Revi

sed

Clin

ical

Inte

rvie

w

Sche

dule

(CIS

-R) a

t a c

ut-o

ff

poin

t of 1

2. A

lso,

the

ICD

-10

was

use

d to

est

ablis

hed

singl

e

prim

ary

diag

nosi

s.

• Oth

er c

orre

late

s: se

lf-re

porte

d.

• Ove

rall

1-w

k pr

eval

ence

of

MD

was

2.7

% fo

r fem

ales

and

1.8%

for m

ales

.

• Evi

denc

e le

vel I

I-2

• Sta

tistic

s: Pr

eval

ence

Austr

alia

10

1

Wilh

elm

K e

t al,

2003

10

Cro

ss-s

ectio

nal

surv

ey

To e

xam

ine

the

curr

ent (

30-d

ay)

prev

alen

ce a

nd

corr

elat

es o

f

men

tal d

isor

der

incl

udin

g M

D in

the

adul

ts

Aus

tralia

n

popu

latio

n.

Subj

ects

≥18

yrs w

ho

parti

cipa

ted

in th

e

Nat

iona

l Sur

vey

of

Men

tal H

ealth

and

Wel

l-Bei

ng

(NSM

HW

B)

• D

epre

ssio

n Pr

eval

ence

the

full

CID

I

• D

isab

ility

: SF-

12 &

“D

ays

out o

f rol

e” –

self-

repo

rted

• Oth

er c

orre

late

s: se

lf-re

porte

d. •

Ove

rall

depr

essi

on

Prev

alen

ce w

as 3

.2%

.

• Th

e hi

ghes

t pre

vale

nce

was

in th

e 45

-54

age.

• Be

ing

fem

ale,

sepa

rate

d

/wid

owed

/div

orce

d,

unem

ploy

men

t has

the

stro

nges

t ass

ocia

tion.

• Ev

iden

ce le

vel I

I-3

• Se

cond

ary

anal

ysis

• St

atis

tics:

Cru

de &

Adj

uste

d O

R\ lo

gist

ic

regr

essi

on (b

ack

elim

inat

ion)

Ref

eren

ce:

1.

Patte

n SB

. Lo

ng-te

rm m

edic

al c

ondi

tions

and

maj

or d

epre

ssio

n in

the

Can

adia

n po

pula

tion.

Can

J P

sych

iatr

y. 1

999

Mar

;

44(2

):151

-7.

2.

Patte

n SB

. M

ajor

dep

ress

ion

prev

alen

ce in

Cal

gary

. Can

J Ps

ychi

atry

. 200

0 D

ec; 4

5(10

):923

-6.

3.

Patte

n SB

. Lon

g-te

rm m

edic

al c

ondi

tions

and

maj

or d

epre

ssio

n in

a C

anad

ian

popu

latio

n st

udy

at w

aves

1 a

nd 2

. J A

ffect

Diso

rd. 2

001

Mar

; 63(

1-3)

:35-

41.

4.

Patte

n SB

. Des

crip

tive

epid

emio

logy

of

a de

pres

sive

syn

drom

e in

a W

este

rn C

anad

ian

com

mun

ity p

opul

atio

n. C

an J

Pub

lic

Hea

lth. 2

001

Sep-

Oct

; 92(

5):3

92-5

.

5.

Patte

n SB

, Bec

k C

A,

Kas

sam

A, W

illia

ms

JV, B

arbu

i C, M

etz

LM.

Long

-term

med

ical

con

ditio

ns a

nd m

ajor

dep

ress

ion:

stre

ngth

of a

ssoc

iatio

n fo

r spe

cific

con

ditio

ns in

the

gene

ral p

opul

atio

n. C

an J

Psyc

hiat

ry. 2

005

Mar

; 50(

4):1

95-2

02.

10

2

6.

Stei

n M

B, C

ox B

J, A

fifi T

O, B

elik

SL,

Sar

een

J. D

oes

co-m

orbi

d de

pres

sive

illn

ess

mag

nify

the

impa

ct o

f chr

onic

phy

sica

l

illne

ss?

A p

opul

atio

n-ba

sed

pers

pect

ive.

Psy

chol

Med

. 200

6 M

ay; 3

6(5)

:587

-96.

7.

Reg

ier

DA

, Sha

piro

S, K

essl

er L

G, T

aube

CA

. Epi

dem

iolo

gy a

nd h

ealth

ser

vice

res

ourc

e al

loca

tion

polic

y fo

r al

coho

l, dr

ug

abus

e, a

nd m

enta

l diso

rder

s. Pu

blic

Hea

lth R

ep. 1

984

Sep-

Oct

; 99(

5):4

83-9

2.

8.

Bla

zer

DG

, K

essl

er R

C,

McG

onag

le K

A,

Swar

tz M

S. T

he p

reva

lenc

e an

d di

strib

utio

n of

maj

or d

epre

ssio

n in

a n

atio

nal

com

mun

ity s

ampl

e: th

e N

atio

nal C

omor

bidi

ty S

urve

y. A

m J

Psyc

hiat

ry. 1

994

Jul;

151(

7):9

79-8

6.

9.

Jenk

ins

R, L

ewis

G, B

ebbi

ngto

n P,

Bru

gha

T, F

arre

ll M

, Gill

B, M

eltz

er H

. The

Nat

iona

l Psy

chia

tric

Mor

bidi

ty S

urve

ys o

f

Gre

at B

ritai

n--in

itial

find

ings

from

the

hous

ehol

d su

rvey

. Int

Rev

Psy

chia

try.

200

3 Fe

b-M

ay; 1

5(1-

2):2

9-42

.

10.

Wilh

elm

K,

Mitc

hell

P, S

lade

T, B

row

nhill

S,

And

rew

s G

. Pr

eval

ence

and

cor

rela

tes

of D

SM-I

V m

ajor

dep

ress

ion

in a

n

Aus

tralia

n na

tiona

l sur

vey.

J A

ffect

Diso

rd. 2

003

Jul;

75(2

):155

-62.

103

Table 3. Item contents of the Oral Health1 (OH1) module

1. Self-rated oral health

2. Usually able to:

• Chew firm meat

• Bite off and chew piece of fresh apple

• Chew boiled vegetables

3. Frequency of pain or discomfort in teeth or gums in the past month

104

Table 4. Item contents of the Oral Health2 (OH2) module

1. When usually visit dentist

2. Dental insurance coverage

3. Any teeth removed and reason for removal

4. Any natural teeth

5. Wears dentures or false teeth

6. Because of condition of teeth, mouth or dentures:

• Has difficulty pronouncing words or speaking clearly

• Avoids conversation or contact with others

• Avoids laughing or smiling

7. In past month, had

• Toothache

• Pain in teeth with hot or cold

• Pain around jaw joints

• Other pain in mouth or face

• Bleeding gums

• Dry mouth

• Bad breath

8. Frequency of brushing teeth

105

Table 5. Item contents of the Depression (DPS) module

1. Past 12 month frequency of two-week period of feeling sad, blue or

depressed or loss of interest

2. In worst two-week period, frequency of loss of interest, feeling tired,

weight gain or loss, trouble falling asleep, trouble concentrating,

feelings of worthlessness, thoughts about death