Open Access

Positive and negative affect and oral health-related quality of life

  • David S Brennan1Email author,
  • Kiran A Singh1,
  • A John Spencer1 and
  • Kaye F Roberts-Thomson1
Health and Quality of Life Outcomes20064:83

https://doi.org/10.1186/1477-7525-4-83

Received: 17 July 2006

Accepted: 20 October 2006

Published: 20 October 2006

Abstract

Background

The aims of the study were to assess the impact of both positive (PA) and negative affect (NA) on self-reported oral health-related quality of life and to determine the effect of including affectivity on the relationship between oral health-related quality of life and a set of explanatory variables consisting of oral health status, socio-economic status and dental visiting pattern.

Methods

A random sample of 45–54 year-olds from metropolitan Adelaide, South Australia was surveyed by mailed self-complete questionnaire during 2004–05 with up to four follow-up mailings of the questionnaire to non-respondents (n = 986 responded, response rate = 44.4%). Oral health-related quality of life was measured using OHIP-14 and affectivity using the Bradburn scale. Using OHIP-14 and subscales as the dependent variables, regression models were constructed first using oral health status, socio-economic characteristics and dental visit pattern and then adding PA and NA as independent variables, with nested models tested for change in R-squared values.

Results

PA and NA exhibited a negative correlation of -0.49 (P < 0.01). NA accounted for a larger percentage of variance in OHIP-14 scores (3.0% to 7.3%) than PA (1.4% to 4.6%). In models that included both PA and NA, PA accounted for 0.2% to 1.1% of variance in OHIP-14 scores compared to 1.8% to 3.9% for NA.

Conclusion

PA and NA both accounted for additional variance in quality of life scores, but did not substantially diminish the effect of established explanatory variables such as oral health status, socio-economic status and dental visit patterns.

Background

Health-related quality of life is a multidimensional concept that includes patient-driven measures such as perceptions and functional status [1]. Oral health-related quality of life measures emerged out of the development of socio-dental indicators to capture non-clinical aspects of oral health that broadened the focus of oral epidemiological research [2].

Self-reported health measures have been demonstrated to reflect a pervasive mood disposition of negative affectivity [3]. Negative affectivity (NA) has been described as a general disposition to experience negative mood states, and has been found to be consistently negatively related to health-related quality of life indicating that personality as well as underlying health can influence self-ratings of health-related quality of life [4]. NA has also been related to oral health-related quality of life, with higher negative affect consistently associated with worse scores on oral health-related quality of life measures [5].

While affectivity has typically been measured in terms of NA, it is also possible to measure positive affect (PA). The terms PA and NA are not considered as opposites, but as distinctive dimensions with high PA a state of high energy, full concentration and pleasurable engagement and low NA a state of calmness and serenity [6]. Given the distinct nature of PA and NA it is important to distinguish their respective potential influences on self-ratings of health-related quality of life. A range of other factors has also been related to oral health-related quality of life, including oral health status, dental visit pattern, socio-demographics and socio-economic status. Differences in prevalence of social impact have been reported for oral health factors such as between dentate and edentulous persons [7], and numbers of missing teeth [8]. Dental attendance has been associated with the perception of an enhanced quality of life [9], as well as other dental visit factors such as place of visit [10], and reason for visit [8, 11]. Specific dental interventions that have been associated with improved oral health-related quality of life include implant-retained dental prostheses [1215], conventional fixed prosthodontics [16, 17], third molar removal [18], orthodontic treatment [19], orthognathic surgery [2022], occlusal splints in therapy for temporomandibular disorder [23], and surgery for oral cancer [24]. Socio-demographic and socio-economic factors related to oral health-related quality of life include age and cultural background [25], as well as gradients in oral conditions in relation to social status [26]. The aims of the study were to assess the impact of both PA and NA on self-reported oral health-related quality of life and to determine the effect of including affectivity on the relationship between oral health-related quality of life and a set of explanatory variables consisting of oral health status, socio-economic status and dental visiting pattern so as to determine the independent effect of PA and NA when adjusting for a set of established explanatory variables.

Methods

Sampling and data collection

A total of 2469 persons aged 45–54 years were randomly sampled from metropolitan Adelaide, South Australia, using the Electoral Roll as a sampling frame. Sampled persons were surveyed by mailed self-complete questionnaire during 2004-05. A primary approach letter was initially mailed, followed a week later by the questionnaire, and then by a reminder card and up to four follow-up mailings of the questionnaire to non-respondents in order to achieve a higher response rate [27].

Variables measured

A range of variables were collected spanning self-reported number of teeth, dental visiting, dental behaviours, socio-demographics, socio-economic status and psycho-social variables. Oral health-related quality of life was measured using the OHIP-14 instrument [10]. The Oral Health Impact Profile (OHIP) is a disease-specific measure of people's perceptions of the social impact of oral disorders on their well-being [28]. OHIP contains 49 questions that capture seven conceptually formulated dimensions based on Locker's theoretical model of oral health [29], and the OHIP-14 was developed as a shorter version of the OHIP for settings where the full battery is inappropriate [10]. Subjects were asked to rate their experience of dental problems in the last year using the OHIP-14. For each of the OHIP-14 questions subjects were asked how frequently they had experienced an impact in the preceding 12 months using a Likert-type response scale coded as 0 = never, 1 = hardly ever, 2 = occasionally, 3 = fairly often and 4 = very often. OHIP sub-scale scores were produced by applying the published item weights and then summing the responses to the 2 weighted items for each sub-scale [10]. Note that the item weights sum to 1.0 for each sub-scale, so that weighted scores will have a smaller range than if they were unweighted. The seven dimensions of OHIP are functional limitation, physical pain, psychological discomfort, physical disability, psychological disability, social disability and handicap. The total OHIP scale score was produced by summing the weighted sub-scale scores. Affectivity was measured using the Bradburn scale of positive and negative affect [30]. The 18 items consisted of 9 items measuring PA and 9 items measuring NA that asked how often each item occurred during the last year using a 5-point scale coded as 0 = never, 1 = hardly ever, 2 = occasionally, 3 = fairly often and 4 = very often. Scores for both PA and NA were produced by summing the responses to the 9 items for each scale.

Analysis

Representativeness of the sample respondents was assessed by comparison to a range of oral health status, sociodemographic and dental visiting pattern variables from a population survey [31]. OHIP-14 scores formed the main dependent variable in regressions which were analysed by comparing the change in R-squared values between nested models [32], comprising model 1 (independent variables of oral health status, socio-economic status and dental visiting pattern), model 2 (independent variables of oral health status, socio-economic status and dental visit pattern plus PA), model 3 (independent variables of oral health status, socio-economic status and dental visiting pattern plus NA), and model 4 (independent variables of oral health status, socio-economic status and dental visiting pattern plus both PA and NA). All models included number of teeth as a continuous variable, card holder status as a socio-economic indicator variable coded as 1 for card holders (such as unemployed and aged pensioners) and 0 for no card, and reason for visit as an indicator variable coded as 1 if the last dental visit was for relief of pain and 0 if not for relief of pain. PA and NA scales were included as continuous variables.

The set of explanatory variables included in the models was determined on the basis of producing a parsimonious model that covered a range of subject domains (i.e., oral health, visit pattern, socio-economic status). In doing so variables were selected to represent each subject domain on the basis that they had dental relevance through their common inclusion in other studies, and consistent and strong associations with dental service provision, analytic applicability through minimal missing data and limited collinearity, and policy relevance to dental public health. Number of teeth was included as this variable has been associated with oral health related quality of life [8, 10, 33], and was considered more appropriate than denture wearing, which has a relatively low prevalence in this age group. While demographic variables are not included directly in the models, the study population is restricted in age to 45–54 year-olds and in geographic location to metropolitan Adelaide, South Australia. Card holder status was selected over income to represent socio-economic status on the basis of minimising missing data, as income may suffer non-response bias due to sensitivity in disclosure [34], and due to the policy relevance of card holder status. In Australia, health concession card holders represent a low-income group who may be eligible for public dental care. Dental visit pattern was represented by the variable of last dental visit being for relief of pain, as this variable has shown consistent and strong associations with provision of dental services [3538].

Results

Response

A total 986 persons responded giving a response rate of 44.4% after adjusting for out of scope persons such as those who could not be contacted. The study participants generally showed a close approximation of the population profile (Table 1). Study participants had slightly lower numbers of teeth, but there was no difference in denture wearing. Study participants had a slightly lower percentage visiting in the last 12 months and slightly fewer numbers of visits in the last 12 months, as well as a lower percentage that visited privately at the last visit, but there was no difference in the percentage receiving check-ups at the last dental visit. There were no differences in the percentage of females, Australian-born, or Indigenous status, but study participants had a slightly higher percentage who spoke English as the main language at home as well as a slightly higher percentage who were concession card holders, but there was no difference in the percentage of persons from higher income households.
Table 1

Comparison of study participants with population profile

 

*Population

Study participants

Oral health status

  

(95% CI)

Number of teeth – mean

26.9

25.4

(25.0–25.8)

Denture (upper jaw) – %

13.7

13.5

(11.4–15.7)

Denture (lower jaw) – %

5.8

6.4

(4.7–7.9)

Dental visit pattern

   

Last dental visit <12 months – %

65.4

61.5

(58.4–64.5)

Check-up at last dental visit – %

41.7

42.8

(39.7–45.9)

Number of dental visits in last 12 months – mean

1.8

1.5

(1.4–1.7)

Visited private at last dental visit – %

95.2

86.4

(84.3–88.6)

Socio-demographics

   

Female sex – %

51.2

52.9

(49.8–56.0)

Australian born – %

70.8

71.3

(68.4–74.2)

Indigenous – %

1.3

0.4

(0–6.8)

English main language at home – %

91.9

95.4

(94.1–96.7)

Socio-economic status

   

Concession card holder – %

15.4

18.0

(15.6–20.4)

Household income $80,000+ – %

24.5

23.9

(21.2–26.7)

*National Dental Telephone Interview Survey 2002: South Australia – Adelaide 45–54 year-olds

Distribution of scale items

The distribution of responses to the OHIP scale items is presented in Table 2. The majority of responses to the items were in the category of 'never', with the exception of the two items from the physical pain sub-scale. The two items from the psychological discomfort sub-scale also had showed relatively high levels of problems compared to the remaining items. The distribution of responses to the affectivity scale items is presented in Table 3. Generally, there were a high percentage of responses in the 'occasionally' category for all items. The items comprising the positive affect scale also had a high percentage of responses in the 'fairly often' category. The items comprising the negative affect scale tended to have a high percentage of responses in the 'hardly ever' category, with the exception of the items 'annoyed with someone' and 'really tired', which had more responses in the 'fairly often' than the 'hardly ever' categories.
Table 2

Distributions of responses to OHIP scale items (%)

How often in the past year have you had the following problems?

Never

Hardly ever

Occasionally

Fairly often

Very often

Functional limitation

     

trouble pronouncing any words

83.8

8.4

5.7

1.4

0.7

sense of taste has worsened

81.6

8.6

6.9

1.7

1.1

Physical pain

     

painful aching in your mouth

49.3

25.4

21.2

2.6

1.4

uncomfortable to eat any foods

43.5

23.9

24.9

3.6

4.1

Psychological discomfort

     

felt self conscious

51.4

16.6

17.1

7.7

7.1

felt tense

61.9

16.6

13.8

4.6

3.1

Physical disability

     

diet been unsatisfactory

81.3

11.7

4.3

1.9

0.8

had to interrupt meals

74.6

15.3

7.7

1.7

0.7

Psychological disability

     

found it difficult to relax

71.9

16.2

8.5

2.1

1.2

been a bit embarrassed

64.1

13.2

15.3

3.4

4.0

Social disability

     

been a bit irritable with other people

78.5

11.7

7.6

1.1

1.1

had difficulty doing your usual jobs

86.5

9.2

3.6

0.5

0.2

Handicap

     

life in general was less satisfying

75.6

11.5

9.2

1.8

2.0

been totally unable to function

90.5

6.7

2.2

0.4

0.3

Table 3

Distributions of responses to affectivity scale items (%)

During the last year how often have you felt...

Never

Hardly ever

Occasionally

Fairly often

Very often

annoyed with someone?*

0.9

10.5

57.9

22.7

8.0

very lonely or remote from other people?*

12.5

38.1

34.3

9.4

5.6

that things were going your way?

1.4

9.1

35.9

46.7

6.9

very worried?*

1.9

21.4

46.7

20.6

9.4

pleased because you've got good friends?

1.4

8.6

28.2

39.4

22.5

afraid of what might happen?*

4.8

30.8

41.0

15.3

8.2

particularly excited or interested in something?

0.6

5.8

39.6

42.7

11.3

depressed or very unhappy?*

9.5

36.1

36.8

10.5

7.1

full of energy?

0.8

11.4

37.3

42.7

7.9

really tired?*

0.8

10.8

47.4

29.9

11.1

so restless that you couldn't sit long in a chair?*

10.0

38.0

33.9

13.0

5.1

that you were really enjoying yourself?

0.6

6.8

38.1

45.7

8.7

really cheerful?

0.6

5.3

34.7

49.7

9.7

like crying?*

7.8

43.0

37.4

8.1

3.8

on top of the world?

4.5

19.6

46.7

25.9

3.4

confident about the future?

2.8

12.3

35.0

42.7

7.2

bored?*

13.1

34.8

37.0

12.2

2.9

pleased about having accomplished something?

0.7

3.9

37.8

48.7

8.9

*items 1,2,4,6,8,10,11,14,17 measure negative affect, the remaining items measure positive affect

Distribution of key continuous variables

Table 4 shows the overall OHIP-14 scale had a mean ± SD of 3.55 ± 4.50, with subscales ranging from 0.29 ± 0.64 for functional limitation to 0.94 ± 0.93 for pain/discomfort, with higher scores indicating a greater number of impacts on quality of life. PA had a higher value (22.41 ± 5.51) compared to NA (16.73 ± 5.75), indicating a perception of more frequent positive than negative feelings. Note that PA and NA exhibited a negative correlation of -0.49 (P < 0.01).
Table 4

Distribution of OHIP and affectivity variables

 

Mean

S.D.

Min.

Max.

OHIP subscale

    

Functional limitation

0.29

0.64

0.0

4.0

Physical pain

0.94

0.93

0.0

4.0

Psychological discomfort

0.85

1.07

0.0

4.0

Physical disability

0.34

0.67

0.0

4.0

Psychological disability

0.55

0.84

0.0

4.0

Social disability

0.29

0.62

0.0

4.0

Handicap

0.31

0.65

0.0

4.0

OHIP scale

    

OHIP score

3.55

4.50

0.0

25.7

Affectivity variables

    

Positive affect

22.41

5.51

0.0

36.0

Negative affect

16.73

5.75

2.0

36.0

Bivariate associations

Table 5 shows bivariate associations between OHIP scale and subscale scores and explanatory variables covering oral health status, socio-economic status, dental visiting pattern, and affectivity. Numbers of teeth and PA both showed consistent negative correlations with OHIP scale and subscale scores, while NA had consistent positive correlations with OHIP scale and subscale scores. Card holders had higher scores for all subscales and the overall OHIP scale compared to those did not have a concession card. If the last dental visit was made for relief of pain, scores on all subscales and the overall OHIP scale were higher compared to those who had made their last visit for reasons other than relief of pain.
Table 5

Bivariate relationships between OHIP and explanatory variables

 

Functional limitation

Physical pain

Psychol. discomfort

Physical disability

Psychol. disability

Social disability

Handicap

OHIP scale

 

rho

rho

rho

rho

rho

rho

rho

rho

Number of teeth

**-0.33

**-0.25

**-0.29

**-0.30

**-0.29

**-0.17

**-0.24

**-0.32

Positive affect

**-0.22

**-0.27

**-0.28

**-0.27

**-0.30

**-0.26

**-0.29

**-0.33

Negative affect

**0.27

**0.28

**0.31

**0.30

**0.35

**0.28

**0.30

**0.36

 

Mean (SE)

Mean (SE)

Mean (SE)

Mean (SE)

Mean (SE)

Mean (SE)

Mean (SE)

Mean (SE)

Card status

**

**

**

**

**

**

**

**

   Card holder

0.56 (.07)

1.35 (.08)

1.40 (.01)

0.72 (.07)

1.03 (.09)

0.53 (.06)

0.67 (.07)

6.23 (.46)

   Not card holder

0.23 (.02)

0.85 (.03)

0.72 (.03)

0.25 (.02)

0.44 (.03)

0.23 (.02)

0.22 (.02)

2.92 (.13)

Reason for visit

**

**

**

**

**

**

**

**

   Relief of pain

0.47 (.07)

1.42 (.09)

1.27 (.10)

0.63 (.07)

0.92 (.08)

0.56 (.07)

0.54 (.07)

5.78 (.46)

   Not relief of pain

0.25 (.02)

0.85 (.03)

0.76 (.04)

0.28 (.02)

0.47 (.03)

0.23 (.02)

0.26 (.02)

3.10 (.14)

**(P < 0.01)

OHIP scale models

Table 6 presents models for OHIP scale scores by oral health status, socio-economic status, dental visiting pattern and affectivity. In each model number of teeth had negative coefficients indicating more teeth were associated with lower numbers of oral health impacts, and holding a health card and visiting for relief of pain both had positive coefficients indicating they were associated with higher numbers of oral health impacts. PA had negative coefficients indicating an association with lower numbers of oral health impacts, while NA had positive coefficients indicating an association with higher numbers of oral health impacts. Comparison of regression coefficients across models indicated that the addition of affectivity tended to attenuate the effects of oral health status, socio-economic status, and dental visiting pattern. This attenuation was only slight in the case of number of teeth, but was more pronounced for card holder status and a dental visit pattern of relief of pain visits. PA and NA also tended to be attenuated in the presence of each other, but this attenuation was more pronounced for PA when NA was included.
Table 6

Regression coefficients from models of OHIP scale scores

 

Model 1

Model 2

Model 3

Model 4

Variables

    

Number of teeth

**-0.19

**-0.19

**-0.18

**-0.18

Card holder (ref. No card)

**2.58

**1.99

**1.81

**1.64

Relief of pain visit (ref. not relief of pain)

**2.05

**1.74

**1.81

**1.69

Positive affect

-

**-0.17

-

**-0.09

Negative affect

-

-

**0.21

**0.17

Model statistics

    

P value

<0.0001

<0.0001

<0.0001

<0.0001

R-sq

0.2022

0.2489

0.2757

0.2862

R-sq change

    

(model 2 vs model 1)

-

**0.0467

-

-

(model 3 vs model 1)

-

-

**0.0735

-

(model 4 vs model 2)

-

-

-

**0.0373

(model 4 vs model 3)

-

-

-

**0.0105

**(P < 0.01)

In model 1, which included oral health status, socio-economic status, and dental visiting pattern the overall R-squared value was 20.2%. The addition of PA (model 2) contributed an additional 4.7% increase in R-squared over model 1, while the addition of NA (model 3) contributed an additional 7.4% increase in R-squared over model 1. The addition of PA (model 4) to the model already containing NA (model 3) contributed to a 1.1% increase in R-squared while addition of NA (model 4) to the model already containing PA (model 2) contributed to a 3.7% increase in R-squared value.

When expressed as a relative increase, the additional R-squared of 0.0467 obtained from adding PA (model 2) to the model containing oral health status, socio-economic status, and dental visiting pattern (model 1) which had an R-squared of 0.2022 provided a 23.1% increase in R-squared, while the additional R-squared of 0.0735 obtained from adding NA (model 3) to the R-squared of 0.2022 observed for model 1 provided a 36.4% increase in the R-squared value.

OHIP subscale models

Table 7 presents R-squared values from models for OHIP subscale scores by oral health status, socio-economic status, dental visit pattern and affectivity. Note that across all 7 subscales and all 4 models of each subscale there were consistent negative coefficients for number of teeth, and positive coefficients for card holder status and reason for visit. For each subscale there was some attenuation of effect in strength of coefficients for card holder status and reason for visit when comparing simpler models with more complex models (i.e., moving from model 1 through to model 4), but each effect remained statistically significant. The addition of PA (model 2 versus model 1) contributed to a significant increase of between 1.4% and 3.9% in amount of variance explained for all subscales, as did the addition of NA (model 3 versus model 1), which added between 3.0% and 6.9%. In models that included both PA and NA, the addition of PA (model 4 versus model 3) contributed to a significant increase in 6 of the 7 subscales of between 0.5% and 1.1%, while the addition of NA (model 4 versus model 2) contributed to a significant increase in variance explained in all 7 subscales of between 1.8% and 3.9%.
Table 7

Summary of R-squared values from regression models of OHIP subscale scores

 

Model 1

Model 2

Model 3

Model 4

OHIP subscale used as dependent variable

    

Functional limitation

0.1494

(a)0.1634

(b)0.1793

(c)0.1815

Physical pain

0.1366

(a)0.1690

(b)0.1778

(c, d)0.1879

Psychological discomfort

0.1539

(a)0.1885

(b)0.2080

(c, d)0.2166

Physical disability

0.1747

(a)0.1977

(b)0.2166

(c, d)0.2212

Psychological disability

0.1704

(a)0.2082

(b)0.2396

(c, d)0.2472

Social disability

0.0856

(a)0.1139

(b)0.1359

(c, d)0.1419

Handicap

0.1264

(a)0.1637

(b)0.1756

(c, d)0.1868

Note: model 1 independent variables include number of teeth, card holder status, relief of pain visit, model 2 includes number of teeth, card holder status, relief of pain visit plus positive affect, model 3 includes number of teeth, card holder status, relief of pain visit plus negative affect, and model 4 includes number of teeth, card holder status, relief of pain visit plus both positive affect and negative affect.

(a): model 2 vs model 1 (P < 0.05)

(b): model 3 vs model 1 (P < 0.05)

(c): model 4 vs model 2 (P < 0.05)

(d): model 4 vs model 3 (P < 0.05)

Discussion

Representativeness

While the overall response yield of n = 986 provided sufficient numbers for analysis, the response rate was lower than anticipated. This was particularly so, since multiple follow-ups were employed to increase the response rate as per the Total Design Method [27]. The use of the Electoral Roll should provide an adequate sampling frame for a population survey of 45–54 year-olds. Bias can distort the design, execution, analysis and interpretation of research [39]. Sampling bias is unlikely, since voting is compulsory for adults in Australia and the sample was drawn at random, the Electoral Roll this should provide a representative sampling frame. Although it is possible that some sub-groups of the population (eg. the homeless) would be under-enumerated, such sub-groups are not the focus of the study. Also the use of restriction can limit selection bias in the design of a study [40], and a restricted age range was adopted in this study. Generally, a response rate of 60% is considered adequate [41], with lower response rates requiring evidence to determine whether bias has been introduced. The issue is whether a lower response rate involves differential response among population sub-groups that could produce bias.

While direct comparison of respondents and non-respondents would be desirable to assess response bias we were only able to compare the profile of respondents with population data. This comparison showed generally small differences between the population data and the study participants based on the range of variables that were compared, the main difference observed being the lower percentage of survey respondents that visited privately at the last dental visit compared to the population consistent with the slightly higher percentage who were concession card holders. Since card holders are eligible for public dental care this is also consistent with observed slightly lower numbers of teeth, lower percentage visiting in the last 12 months, and fewer numbers of visits in the last 12 months. Selection bias can be controlled in the analysis of a study through adjustments such as stratification or multivariate analysis [40]. Hence, card holder status was included in the statistical models along with two key oral health and visit variables, number of teeth and reason for visit.

While the relatively low response rate did not appear to produce bias in the respondents compared with the population, it raises the issue of the difficulty in achieving high response rates in population studies. Data from the USA has shown a trend toward substantial increases in total non-response, primarily due to increased percentages of respondents who refused to be interviewed that was related to the level of urbanisation [42]. However, response rate is considered only an indirect indication of the extent of non-response bias, and more attention is required to assessments of bias rather than to specific response rate thresholds [43]. It should be noted that although the response was relatively low in this study, it was obtained using the Total Design Method [27], incorporating aspects such as repeated contacts and return postage that have been shown to increase response behaviour [44].

Oral health, visit pattern and socio-economic status

While PA and NA were the focus of the study, the set of additional explanatory variables included in the models (i.e., oral health, visit pattern, socio-economic status) warrant some discussion. Regardless of the inclusion of PA or NA, significant effects were observed between oral health-related quality of life and number of teeth, card holder status and dental visit pattern. The negative relationship between number of teeth and worsening oral health-related quality of life has been observed previously [8, 10], and underscores the importance of tooth retention. The positive association between card holder status and poorer oral health-related quality of life is consistent with previous observations that card holders in Australia suffer from problems with access to dental care, and these access problems have been associated with high levels of emergency visits, and high rates of extraction [45]. Problem-oriented dental visit patterns have been associated with less favourable patterns of dental service provision in the private [38] and public sector [46], poorer oral health status [47], and worse quality of life [33]. Similar observations have been made in other countries, such as higher recovery from quality of life decrements among regular dental attenders and those making check-up visits in the USA [48].

Positive and negative affect

PA and NA have been found to relate to different classes of variables, with NA (but not PA) related to stress and coping, health complaints and frequency of unpleasant events, and PA (but not NA) related to social activity and satisfaction, and frequency of pleasant events [6]. While PA and NA are considered separate dimensions, and both are associated with quality of life in the expected directions and in the presence of each other – the influence of PA was attenuated in the presence of NA, indicating the more dominant role for NA in relation to quality of life. The attenuation may be expected given the observed correlation between PA and NA. While the observed correlation was in the range of low to moderate [49], this size of effect is unlikely to result in collinearity problems. Collinearity refers to relationships among the independent variables and is used to indicate that one predictor is an exact linear combination of the others. Near collinearity arises when there is a high degree of association between independent variables and may result in inaccurate estimates of regression coefficients, standard errors and hypothesis test statistics [32].

Not all studies find such correlations between PA and NA and this most likely reflects the adoption of a longer time frame of "last year" with the Bradburn scale in this study, as affectivity scales have been reported to be more reliable over longer time frames [6], such that the scales should represent relatively fixed personality dispositions rather than recent life events [30]. The relationship of personality, particularly negative affect, and well-being has been explained through a dynamic equilibrium model involving both personality and life events [50], whereby life events may alter well-being temporarily before personality traits draw people back to their usual level of life events and well-being. Other studies looking at cognitive functioning have found NA to be more strongly related to self-appraisal of cognitive functioning than PA [51]. Affectivity in terms of PA and NA has been suggested as a mediator of the link between outcome expectancies related to optimism and pessimism and psychological adjustment in terms of depressive symptoms and life satisfaction [52]. Self-esteem has also been shown to be strongly negatively correlated with neuroticism/NA and moderately to strongly related to extraversion/PA [53].

Subjectivity of quality of life measures and affectivity

It may be expected that more subjective indices such as the OHIP subscales psychological discomfort and psychological disability would have stronger associations with NA than less subjective indices such as physical disability and functional limitations [5]. In this study NA either alone (model 3) or in the presence of PA (model 4) contributed the least amount of additional variance in OHIP-14 scores for the less subjective functional limitation subscale and the most for the more subjective psychological disability subscale. In the absence of NA (model 2) PA also contributed the least amount of additional variance for the functional limitation subscale and the most for the psychological disability subscale. In the presence of NA (model 4) PA showed little change in additional variance between subscales, but did not contribute any significant additional variance to the functional limitation subscale. As previously reported this provides some support for the hypothesis that more subjective indices are more strongly influenced by affectivity [5], including PA, but that the influence of PA on quality of life measures is attenuated when controlling for NA.

Symptom perception

One main path of symptom perception of common physical symptoms has been identified as involving more NA via a stronger tendency to selective attention [54]. A study of chronically ill patients found that optimism did not tend to bias their perceptions of their health status but that positive efficacy expectancies encouraged self-care behaviour [55]. Another study of self-reported symptoms among asthmatics found that patients with high NA were more influenced by suggestion than patients with low NA [56]. However, the tendency to overestimate the likelihood of ambiguous symptoms as being indicative of serious illness appears to be unique to hypochondriasis and not attributable to high NA [57].

While experience of oral symptoms may vary between individuals, some specific oral symptoms such as toothache are considered more likely to influence quality of life and alter behaviour through the seeking of professional care than other symptoms such as sore gums or sensitive teeth [58]. There is also evidence of specific relationships between oral health status and reported impact on quality of life, such as number of teeth and chewing ability, missing front teeth with self-esteem and going out, as well as fewer functioning teeth and more decayed teeth with aesthetic dissatisfaction, altered eating, diminished communication and pain [58].

Personality, through affectivity, may influence quality of life measures through symptom perception, whereby high NA individuals are more sensitive to health conditions and therefore more likely to perceive and/or complain about health concerns resulting in worse quality of life scores and possibly inflated health-related complaints [5]. More subjective indices of quality of life may therefore be more prone to being influenced by personality variables such as affectivity. This may warrant the need to control for personality factors, but also provides additional insight into the understanding how health conditions influence functioning and well-being [5]. There is also evidence that positive mental states are more than the absence of symptoms, and may play an independent role in health outcomes [59].

Conclusion

PA and NA both accounted for additional variance in quality of life scores indicating that personality factors have independent effects on self-ratings of health-related quality of life. Futhermore, the addition of both PA and NA revealed some attenuation of the effects of oral health status, socio-economic status, and dental visiting pattern indicating that some of the association of these variables with health-related quality of life reflects underlying mood states. However, PA and NA did not substantially diminish the effect of established explanatory variables such as oral health status, socio-economic status and dental visiting pattern confirming their independent effects on oral health-related quality of life.

Declarations

Acknowledgements

The data collection was funded by a National Health and Medical Research Council project grant (250316) for 2003–2005.

Authors’ Affiliations

(1)
Australian Research Centre for Population Oral Health, School of Dentistry, The University of Adelaide

References

  1. Gift HC, Atchison KA: Oral health, health, and health-related quality of life. Med Care 1995, 33: NS57–77. 10.1097/00005650-199511001-00008PubMedView ArticleGoogle Scholar
  2. Cohen LK, Jago JD: Toward the formulation of sociodental indicators. Int J Health Serv 1976, 6: 681–98.PubMedView ArticleGoogle Scholar
  3. Watson D, Pennebaker JK: Health complaints, stress, and distress: exploring the central role of negative affectivity. Psychol Rev 1989, 96: 234–54. 10.1037/0033-295X.96.2.234PubMedView ArticleGoogle Scholar
  4. Kressin NR, Spiro A 3rd, Skinner KM: Negative affectivity and health-related quality of life. Med Care 2000, 38: 858–67. 10.1097/00005650-200008000-00009PubMedView ArticleGoogle Scholar
  5. Kressin NR, Reisine S, Spiro A 3rd, Jones JA: Is negative affectivity associated with oral quality of life? Community Dent Oral Epidemiol 2001, 29: 412–23. 10.1034/j.1600-0528.2001.290602.xPubMedView ArticleGoogle Scholar
  6. Watson D, Clark LA, Telegen A: Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol 1988, 54: 1063–70. 10.1037/0022-3514.54.6.1063PubMedView ArticleGoogle Scholar
  7. Cushing AM, Sheiham A, Maizels J: Developing socio-dental indicators – the social impact of dental disease. Community Dent Health 1986, 3: 3–17.PubMedGoogle Scholar
  8. Slade GD, Spencer AJ, Locker D, Hunt RJ, Strauss RP, Beck JD: Variations in the social impact of oral conditions among older adults in South Australia, Ontario and North Carolina. J Dent Res 1996, 75: 1439–50.PubMedView ArticleGoogle Scholar
  9. McGrath C, Bedi R: An evaluation of a new measure of oral health related quality of life-OHQoL-UK(W). Community Dent Health 2001, 18: 138–43.PubMedGoogle Scholar
  10. Slade GD: Derivation and validation of a short-form oral health impact profile. Community Dent Oral Epidemiol 1997, 25: 284–90. 10.1111/j.1600-0528.1997.tb00941.xPubMedView ArticleGoogle Scholar
  11. Chen MS, Hunter P: Oral health and quality of life in New Zealand: a social perspective. Soc Sci Med 1996, 43: 1213–22. 10.1016/0277-9536(95)00407-6PubMedView ArticleGoogle Scholar
  12. Awad MA, Locker D, Korner-Bitensky N, Feine JS: Measuring the effect of intra-oral implant rehabilitation on health-related quality of life in a randomized controlled clinical trial. J Dent Res 2000, 79: 1659–63.PubMedView ArticleGoogle Scholar
  13. Allen PF, McMillan AS, Walshaw D: A patient-based assessment of implant-stabilised and conventional complete dentures. J Prosthet Dent 2001, 85: 141–7. 10.1067/mpr.2001.113214PubMedView ArticleGoogle Scholar
  14. Att W, Stappert C: Implant therapy to improve quality of life. Quintessence Int 2003, 34: 573–81.PubMedGoogle Scholar
  15. Strassburger CG, Heydecke G, Kerschbaum T: Influence of prosthetic and implant therapy on satisfaction and quality of life: a systematic literature review. Part 1-Characteristics of the studies. Int J Prosthodont 2004, 17: 83–93.PubMedGoogle Scholar
  16. John MT, Slade GD, Szentpetery A, Setz JM: Oral health-related quality of life in patients treated with fixed, removable, and complete dentures 1 month and 6 to 12 months after treatment. Int J Prosthodont 2004, 17: 503–11.PubMedGoogle Scholar
  17. 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, 31: 161–8. 10.1034/j.1600-0528.2003.00029.xPubMedView ArticleGoogle Scholar
  18. McGrath C, Comfort MB, Lo EC, Luo Y: Can third molar surgery improve quality of life? A 6-month cohort study. J Oral Maxillofac Surg 2003, 61: 759–63. 10.1016/S0278-2391(03)00150-2PubMedView ArticleGoogle Scholar
  19. de Oliveira CM, Sheiham A: Orthodontic treatment and its impact on oral health-related quality of life in Brazilian adolescents. J Orthod 2004, 31: 20–7. 10.1179/146531204225011364PubMedView ArticleGoogle Scholar
  20. Cunningham SJ, Crean SJ, Hunt NP, Harris M: Preparation, perceptions, and problems: a long-term follow-up study of orthognathic surgery. Int J Adult Orthodon Orthognath Surg 1996, 11: 41–7.PubMedGoogle Scholar
  21. Hatch JP, Rugh JD, Clarke GM, Keeling SD, Tiner BD, Bays RA: Health-related quality of life following orthognathic surgery. Int J Adult Orthodon Orthognath Surg 1998, 13: 67–77.PubMedGoogle Scholar
  22. Bennett ME, Phillips CL: Assessment of health-related quality of life for patients with severe skeletal disharmony: A review of the issues. Int J Adult Orthodon Orthognath Surg 1999, 14: 65–75.PubMedGoogle Scholar
  23. Dao TTT, Lavigne GJ, Charbonneau A, Feine JS, Lund JP: The efficacy of oral splints in the treatment of myofascial pain of the jaw muscles: A controlled clinical trial. Pain 1994, 56: 85–94. 10.1016/0304-3959(94)90153-8PubMedView ArticleGoogle Scholar
  24. Schliephake H, Ruffert K, Schneller T: Prospective study of the quality of life of cancer patients after intraoral tumour surgery. J Oral Maxillofac Surg 1996, 54: 664–9. 10.1016/S0278-2391(96)90676-XPubMedView ArticleGoogle Scholar
  25. Steele JG, Sanders AE, Slade GD, Allen PF, Lahti S, Nuttall 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. 10.1111/j.0301-5661.2004.00131.xPubMedView ArticleGoogle Scholar
  26. Sanders AE, Slade GD, Turrell G, Spencer AJ, Marcenes W: The shape of the socio-economic-oral health gradient: implications for theoretical explanations. Community Dent Oral Epidemiol 2006, 34: 310–9. 10.1111/j.1600-0528.2006.00286.xPubMedView ArticleGoogle Scholar
  27. Dillman DA: Mail and telephone surveys. The total design method. NY: Wiley; 1978.Google Scholar
  28. Slade GD, Spencer AJ: Development and evaluation of the oral health impact profile. Community Dent Health 1994, 11: 3–11.PubMedGoogle Scholar
  29. Locker D: Measuring oral health: a conceptual framework. Community Dent Health 1988, 5: 5–13.Google Scholar
  30. Warr P, Barter J, Brownbridge G: On the independence of positive and negative affect. J Pers Soc Psychol 1983, 44: 644–51. 10.1037/0022-3514.44.3.644View ArticleGoogle Scholar
  31. Carter KD, Stewart JF: National Dental Telephone Interview Survey 2002. AIHW cat. no. DEN 128. Adelaide: AIHW Dental Statistics and Research Unit. 2003.Google Scholar
  32. Kleinbaum DG, Kupper LL, Muller KE, Nizam A: Applied regression analysis and other multivariable methods. CA: Duxbury Press; 1998.Google Scholar
  33. Brennan DS, Spencer AJ: Comparison of a generic and a specific measure of oral health related quality of life. Community Dent Health 2005, 22: 11–8.PubMedGoogle Scholar
  34. Armstrong BK, White E, Saracci R: Principles of exposure measurement in epidemiology. Oxford: OUP; 1994.Google Scholar
  35. Brennan DS, Spencer AJ, Szuster FSP: Provision of extractions by main diagnoses. Int Dent J 2001, 51: 1–6.PubMedView ArticleGoogle Scholar
  36. Brennan DS, Spencer AJ: Influence of patient, visit and oral health factors on dental service provision. J Public Health Dent 2002,62(3):148–57.PubMedView ArticleGoogle Scholar
  37. Brennan DS, Spencer AJ: Provision of diagnostic and preventive services in general dental practice. Community Dent Health 2003, 20: 5–10.PubMedGoogle Scholar
  38. Brennan DS, Spencer AJ: The role of dentist, practice and patient factors in the provision of dental services. Community Dent Oral Epidemiol 2005, 33: 181–95. 10.1111/j.1600-0528.2005.00207.xPubMedView ArticleGoogle Scholar
  39. Sackett DL: Bias in analytic research. J Chron Dis 1979, 32: 51–63. 10.1016/0021-9681(79)90012-2PubMedView ArticleGoogle Scholar
  40. Fletcher RH, Fletcher SW, Wagner EH: Clinical epidemiology. The essentials. Second edition. Baltimore: Williams and Wilkins; 1988.Google Scholar
  41. Mangione TW: Mail surveys. Improving the quality. CA: Sage; 1995.View ArticleGoogle Scholar
  42. Steeh CG: Trends in nonresponse rates, 1952–1979. Public Opinion Quarterly 1981, 45: 40–57. 10.1086/268633View ArticleGoogle Scholar
  43. Asch DA, Jedrziewski MK, Christakis NA: Response rates to mail surveys published in medical journals. J Clin Epidemiol 1997, 50: 1129–36. 10.1016/S0895-4356(97)00126-1PubMedView ArticleGoogle Scholar
  44. Yammarino FJ, Skinner SJ, Childers TL: Understanding mail survey response behaviour. A meta-analysis. Public Opinion Quarterly 1991, 55: 613–99. 10.1086/269284View ArticleGoogle Scholar
  45. Brennan DS, Spencer AJ, Slade GD: Service provision among adult public dental service patients: baseline data from the Commonwealth Dental Health Program. Aust N Z J Public Health 1997, 21: 40–4.PubMedView ArticleGoogle Scholar
  46. Brennan DS, Spencer AJ: Evaluation of service provision patterns during a public-funded dental program. Aust N Z J Public Health 1999, 23: 140–6.PubMedView ArticleGoogle Scholar
  47. Brennan DS, Spencer AJ: Changes in caries experience among public dental patients between 1995/96 and 2001/02. Aust N Z J Public Health 2004, 28: 542–8. [http://www.phaa.net.au/anzjph/journalpdf_2004/dec_2004/p.%20542–48.pdf]PubMedView ArticleGoogle Scholar
  48. Fisher MA, Gilbert GH, Shelton BJ: Effectiveness of dental services in facilitating recovery from oral disadvantage. Qual Life Res 2005, 14: 197–206. 10.1007/s11136-004-3929-2PubMedView ArticleGoogle Scholar
  49. Martin P, Bateson P: Measuring behaviour. An introductory guide. Cambridge: CUP; 1986.Google Scholar
  50. Ormel J, Lindenberg S, Steverink N, Vonkorff M: Quality of life and social production functions: a framework for understanding health effects. Soc Sci Med 1997, 45: 1051–63. 10.1016/S0277-9536(97)00032-4PubMedView ArticleGoogle Scholar
  51. Seidenberg M, Taylor MA, Haitner A: Personality and self-report of cognitive functioning. Arch Clin Neuropsychol 1994, 9: 353–61. 10.1016/0887-6177(94)90023-XPubMedView ArticleGoogle Scholar
  52. Chang EC, Sanna LJ: Optimism, pessimism, and positive and negative affectivity in middle-aged adults: a test of a cognitive-affective model of psychological adjustment. Psychol Aging 2001, 16: 524–31. 10.1037/0882-7974.16.3.524PubMedView ArticleGoogle Scholar
  53. Watson D, Suls J, Haig J: Global self-esteem in relation to structural models of personality and affectivity. J Pers Soc Psychol 2002, 83: 185–97. 10.1037/0022-3514.83.1.185PubMedView ArticleGoogle Scholar
  54. Kolk AM, Hanewald GJ, Schagen S, Gijsbers van Wijk CM: A symptom perception approach to common physical symptoms. Soc Sci Med 2003, 57: 2343–54. 10.1016/S0277-9536(02)00451-3PubMedView ArticleGoogle Scholar
  55. de Ridder D, Fournier M, Bensing J: Does optimism affect symptom report in chronic disease? What are its consequences for self-care behaviour and physical functioning? J Psychsom Res 2004, 56: 341–50. 10.1016/S0022-3999(03)00034-5View ArticleGoogle Scholar
  56. Put C, Van den Bergh O, Van Ongeval E, De Peuter S, Demedts M, Verleden G: Negative affectivity and the influence of suggestion on asthma symptoms. J Psychosom Res 2004, 57: 249–55. 10.1016/S0022-3999(03)00541-5PubMedView ArticleGoogle Scholar
  57. Marcus DK, Church SE: Are dysfunctional beliefs about illness unique to hypochondriasis? J Psychosom Res 2003, 54: 543–7. 10.1016/S0022-3999(02)00526-3PubMedView ArticleGoogle Scholar
  58. Gift HC: Oral health outcomes research – challenges and opportunities. In Measuring oral health and quality of life. Volume Chapter 3. Edited by: Slade GD. Chapel Hill: University of North Carolina, Dental Ecology; 1997.Google Scholar
  59. Huppert FA, Whittington JE: Evidence for the independence of positive and negative well-being: Implications for quality of life assessment. Br J Health Psychol 2003, 8: 107–22. 10.1348/135910703762879246PubMedView ArticleGoogle Scholar

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© Brennan et al; licensee BioMed Central Ltd. 2006

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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