Authors: Zoe Marshman, School of Clinical Dentistry, Sheffield, uk



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Title: What influences the use of dental services by adults in the UK?

Running title: Influences on dental service use


Authors:

Zoe Marshman, School of Clinical Dentistry, Sheffield, UK.

Jenny Porritt, School of Clinical Dentistry, Sheffield, UK.

Tom Dyer, School of Clinical Dentistry, Sheffield, UK.

Ceri Wyborn, Yorkshire and Humber Public Health Observatory, York, UK.

Jenny Godson, Yorkshire and Humber Strategic Health Authority, UK.

Sarah Baker, School of Clinical Dentistry, Sheffield, UK.
Abstract
Objectives:

Optimising access to and utilization of dental services remains a major public health challenge. The aim of this study was to use Andersen’s behavioural model to investigate the factors that influence utilisation of dental services and predict oral health outcomes and to identify how access could be improved.

Methods:

Secondary analysis was conducted of data from a regional postal survey (n = 10,864) of adults in the UK. Items were chosen to reflect variables of Andersen’s behavioural model including predisposing characteristics (deprivation), enabling resources (perceived difficulty accessing a dentist), need (perceived treatment need), health behaviours (reason for attendance and time since last visit to the dentist) and oral health outcomes (oral health impacts (symptoms, functional limitation and social) and global oral health). Structural equation modelling was used to estimate the direct and indirect pathways between the variables within the model.

Results:

When a combination of indirect and direct effects were taken into account, perceived difficulty accessing the dentist was associated with higher perceived treatment need (β=.25, p<0.01), increased oral health impacts (β=-.23, p<0.01) and worse global oral health (β=-.21, p<0.01).


Overall, the variables included within this model explained 17.4% of the variance for dental attendance, 55.4% of the variance for the length of time since people had last visited the dentist, 21.7% of the variance for oral health impacts and 42.9% of the variance for people’s global oral health.
Conclusions:

Perceived treatment need and difficulty accessing dental services were found to be key predictors of oral health outcomes. No significant pathways were found between deprivation and the other variables in the model. Further research is needed to develop and evaluate effective interventions to improve access to dental services.




Introduction

Optimising access to and utilization of dental services remains a major public health challenge internationally with significant impacts on individuals, communities and costs to society . For example, in the UK access to dental services has been a political imperative since the 1990s with many policy initiatives to improve the situation. In 2006 a new National Health Service (NHS) primary dental care contract was introduced in England to expand capacity and ensure services were ‘easy–to-access’ for patients . However, a review of the impact of the new contract concluded that the aim to improve access had not been realised with fewer courses of treatment carried out and fewer people seeing an NHS dentist . Little research has been conducted in the UK to investigate factors that influence service use or to identify ways to optimise utilisation.


Several studies have investigated influences on dental service utilisation using models of healthcare as theoretical frameworks . The most well known of these are the behavioural models developed by Andersen and colleagues . The original model suggested that people’s use of health services is dependent on their predisposition to use of services, factors which enable or impede use and their need for care. This model was later refined to describe the interrelationships between population characteristics (predisposing factors, enabling resources and need), health behaviours and health outcomes (Figure 1) and has been used to investigate variables that explain service use and ways to improve it.
Reisine applied the original model to dental services utilization to university employees in the US assessing predisposing factors (including age, sex, education, dental attitudes), enabling factors (family income, participant’s perception of difficulty visiting the dentist), clinical measures of need and use of dental services (the number of visits over the past two years). She found sex to be the most influential variable with income and age having no significant effects, although she acknowledged that enabling factors may be more important in a general population .

Andersen’s model has also been tested in relation to oral health in the general population with support found for the hypotheses proposed . Baker tested the revised model and the interrelationships between predisposing factors (qualifications, income, social class), enabling factors (oral health education, perceptions of treatment expense) and need and whether their influence on oral health behaviours (toothbrushing, dental attendance) and outcomes (oral health-related quality of life) were direct or mediated based on data from a national adult dental health survey in the UK. She found support for the model but that the impact of the predisposing factors on need, use of services and oral health outcomes was indirect. Debates continue about the nature of the pathways (direct and indirect) through which socio-economic factors influence oral health and the appropriateness of the different approaches to measurement taken (area-based indicators versus income or occupation measures) . Baker suggested further work with Andersen’s models to explore the contribution of the socioeconomic profile of the individual, the importance of age and development of the model’s dental public health applications.


Other studies have also used versions of Andersen’s models to investigate utilization of oral health services with older people in the US and New Zealand , young people in Denmark , and adults in sub-Sahara Africa adding further support for the use of this model.
In summary, support has been found for the application of Andersen’s behavioural models to dental service utilisation in employed and general populations using a range of different indicators to reflect the variables in the model . Research is required to establish the influence of factors, particularly sex , age and socio-economic status and to develop the dental public health application of this research .
The aim of this study was to use Andersen’s model in a secondary analysis to investigate the factors that influence access and utilisation of dental services and oral health outcomes, from a survey of adults in the UK and to identify where improvements in access could be made. The version of Andersen’s model selected was based on hypothesized pathways which have been supported within the dental literature.
Materials and Methods

In 2008 a postal survey of adults in the Yorkshire and Humber region of the UK was conducted to investigate oral health and service utilization . The questionnaire covered adults’ experience of using dental services, self-reported oral health status and the impact of the mouth on everyday life. The questionnaire was sent to a sample of 25,200 adults (16 years and over), representative of those in the region, using the database of patients registered with a general medical practitioner. Non-respondents were sent two reminders (at three to four week intervals). Ethical approval was provided by Bradford Research Ethics Committee and research governance approval was also obtained from the hosting health organisations.


Measures

Items from the questionnaire were chosen to reflect seven variables of Andersen’s behavioural model of service utilisation . The variables included population characteristics (predisposing characteristics, enabling resources and need), oral health behaviours and oral health outcomes (Table 1).


Population characteristics

The population characteristics were predisposing characteristics (deprivation), enabling resources (perceived difficulty accessing a dentist) and need (perceived treatment need). Deprivation was assessed based on the participant’s postcode using a composite area-based measure called the Index of Multiple Deprivation 2007 which measures seven domains of deprivation at the small area level (with populations of around 1,500) . The domains include income, employment, health, education, skills and training, barriers to housing and services, living environment and crime. Areas are ranked according to quintiles with the ‘least deprived’ and ‘most deprived’ quintiles comprising those neighbourhoods falling among the least or most deprived 20% in England.


Perceived difficulty accessing a dentist was assessed using the question ‘Is it difficult for you to get routine (e.g. check-up and fillings) dental care?’ Responses were scored using ‘Yes’=1 ‘Don’t know/can’t remember’=2; and ‘No’=3, with higher scores representing increased perceived ease accessing dental services. Participants perceived treatment need was measured by responses to the question ‘If you went to the dentist tomorrow, do you think you would need treatment?’ Responses included: ‘I would need treatment’=1; ‘Don’t know’=2; and ‘I would not need treatment’=3, with higher scores reflecting less perceived treatment need .
Health behaviours

Health behaviours relating to dental care utilisation were assessed using two separate items within the questionnaire. Participants were asked ‘About how long ago was your last visit to the dentist?’ Responses included: ‘Up to 1 year ago’=1; ‘Between 1 and 2 years ago’=2; ‘Between 2 and 5 years ago’=3; ‘More than 5 years ago’=4; and ‘Never been’=5 . Participants were also asked about their reasons for visiting the dentist: ‘In general why do you go to the dentist?’ to which responses included: ‘To have a regular check up’=1; ‘To have an occasional check up’=2; ‘Only when you have trouble with your teeth’=3; and ‘Never been’=4.


Oral health outcomes

The oral health outcomes measured included specific oral health impacts and global oral health. Oral health impacts were assessed by calculating the total score from three questions taken from the Oral Health Impact Profile : ‘In the last 12 months have you had a painful aching in your mouth?’ (oral symptoms); ‘In the last 12 months, have you found it uncomfortable to eat any foods because of problems with your teeth, mouth or dentures?’ (functional limitations); and ‘In the last 12 months, have you been self-conscious because of your teeth, mouth or dentures?’ (social impacts). Responses included: Never=1; Hardly ever=2; Occasionally=3; Fairly often=4; Very often=5. Global oral health was assessed using the question: ‘Would you say the health of your teeth, lips, jaws and mouth is?’ Responses included: Excellent=1; Very good=2; Good=3; Fair=4; Poor=5; Very poor=6 .


Analysis

Structural equation modelling (SEM) with observed variables using AMOS 18.0 was used to estimate the direction of direct and indirect paths between the seven variables within the model. SEM with observed variables is a technique which measures the extent to which a pre-specified structural model fits a data set. Two alternative models of dental care utilization were compared using four goodness-of-fit indices and two statistics used for the comparison of two models (Table 2) . Model 1 included 14 direct pathways and was based on a simple linear version of Andersen’s behavioural model (Figure 1) . However, findings from the dental literature have revealed significant associations between population characteristics, such as pre-disposing characteristics , enabling resources and need , and oral health outcomes in the adult population. Therefore, model 2 included 17 hypothesized direct pathways, which included additional pathways between population characteristics (deprivation, perceived difficulties accessing a dentist and perceived treatment need) and oral health outcomes (Figure 2). Criteria which addressed the issue of parsimony within models and imposed penalties for model complexity were employed (e.g. Incremental Fit Index and Browne-Cudeck criterion).


The parameters of each model were estimated using the Asymptotic Distribution-Free (ADF) estimator which is a methodology specifically designed for dealing with categorical variables and a total of 900 bootstraps were created from the dataset with the aim of deriving less biased standard errors . The 95% confidence interval (CI) bootstrap percentiles were used to interpret the results as these have been shown to be more accurate . However, bootstrapping procedures cannot be performed on data with missing values so data imputation was used and missing values were replaced by median scores for each variable.
In order to test whether the demographic variables of age and gender acted as moderators of the direct pathways within the accepted model, two multi-group analyses were conducted. This involved creating two separate models for gender (male and female) and three separate models for age .
Results

Sample


Of the 25,200 questionnaires, 10,864 were returned completed (43.1% response rate). The sample consisted of 6,032 females (55.5%) and 4,832 males (44.5%). The age of participants ranged from 16 to 104 years (mean=52.5, SD=18.5). Missing data for each of the variables can be seen in Table 1. Based on the measure of deprivation, 1,447 participants in the sample were in the least deprived (13.3%) and 2,470 were in the most deprived quintiles (22.7%) in England. Table 1 describes the characteristics of the study variables.
Model fit

When examining the overall fit of the two hypothesised models, the analysis revealed that model 2 was a better fit of the data (Table 2). Model 2 fit all four goodness of fit indices and was therefore the accepted model of dental service utilisation with all subsequent analyses based on model 2 (Figure 2). When multi-group analyses were conducted using this model, the results revealed that both the male and female models and the three models for the different age groups were an acceptable fit (Table 2). The model was also re-run using only those individuals who provided complete data (N=10,220). The results revealed that the model fit was still acceptable, suggesting data imputation did not influence the overall acceptability of the model (Table 2). To test the robustness of the model the sample was also split into two equal groups (at random) and the acceptability of the model re-analysed. Examination of fit indices revealed the model fit to be an acceptable for both groups.


Overall, the variables included within this model explained 17.4% of the variance for reason for dental attendance, 55.4% of the variance for the length of time since people had last visited the dentist, 21.7% of the variance for oral health impacts and 42.9% of the variance for people’s global oral health (Figure 2).
Significant direct pathways

When the direct effects were taken into account, all but one of the 17 pathways hypothesised in model 1 were significant; length of time since last visiting the dentist did not significantly predict oral health impacts (Table 3). Significant direct predictors of oral health impacts included deprivation (β=.07, p<.01), perceived difficulties with accessing a dentist (β=-.12, p<.01) and perceived treatment need (β=-.41, p<.01). Significant direct predictors of global oral health included difficulties with access (β=-.04, p<.01) perceived treatment need (β=-.27, p<.01), length of time since last visit (β=.09, p<.01), and oral health impacts (β=.44, p<.01). Therefore, the results revealed that a higher perceived treatment need and perceived problems accessing the dentist were associated with increased oral health impacts and worse global oral health. Increased deprivation was also associated with worse oral health outcomes but to a lesser extent.


All of the significant predictors of oral health outcomes existed when gender and the three age groups were analysed separately, with just two exceptions; for the older age group (over 75 years) perceived difficulties accessing care was not a significant predictor of global oral health and deprivation was no longer a significant predictor of oral health impacts. Some of the weaker relationships between the variables within the model (Table 3) also ceased to exist within the multi-group analysis. There was no significant relationship between perceived access and length of time since last dental visit for females and those in the youngest age group (16-44 years) or between perceived access and reason for dental visit in the group aged between 45 and 74 years. There was also no relationship between deprivation and perceived access for males and between deprivation and perceived treatment need for those over the age of 75 years. The only additional significant pathway in the multi-group analysis was between the length of time since last dental visit and oral health impacts, with more recent dental visits being associated with increased impacts for those aged between 16 and 44 years (β =-.06, p<.01).
Significant indirect pathways

When examining indirect pathways separately, 12 of the indirect pathways which existed within the model were significant (Table 3). Significant indirect predictors of oral health impacts included deprivation (β=.06, p<.01) and difficulties with accessing the dentist (β=.-.10, p<.01). Significant indirect predictors of global oral health included deprivation (β=.11, p<.01), difficulties with accessing the dentist (β=-.17, p<.01), perceived treatment need (β=-.21, p<.01) and reason for visiting the dentist (β=.06, p<.01). Whilst a number of variables indirectly predicted oral health outcomes, perceived difficulties accessing the dentist and perceived treatment were the two variables which most influenced oral health through a variety of indirect pathways (Figure 2).




Discussion

Data from a large (>10,000) sample of adults in the UK were analysed. Perceived difficulty accessing a dentist and perceived treatment need were found to be key factors predictive of oral health outcomes in adults. The model fit was acceptable for males, females and all of the three age groups analysed and interestingly perceived difficulty accessing a dentist and perceived treatment need remained significant predictors of oral health impacts irrelevant of age and gender. The model explained a large amount of variance for both dental service utilization and oral health outcomes confirming the support for using the adapted behavioural model as applied to oral health . Perceived treatment need was the main predictor of oral health behaviours and outcomes. Individuals who had higher level of perceived treatment need were less likely to attend regular dental appointments. In contrast, Reisine found sex to be the most influential variable with income and age having no significant effects . While clinical measures of need were included in her analyses, perceived need for treatment was not. Baker found the pathway between perceived treatment need and oral health outcomes to be significant but did not include perceived difficulty accessing a dentist .


Perceived difficulty accessing a dentist was a predictor of oral health outcomes, and influenced dental service utilisation indirectly through perceived need. This is the first study to indicate the importance of this factor in oral health research, although the influence of healthcare availability on health was discussed by Lalonde . While there had been an historical lack of dental service provision in this region, at the time of data collection, traditional oral healthcare needs assessments suggested that there was sufficient volume of NHS dental services to meet the needs of patients in most areas. Further research is needed to explore this concept of perceived difficulty accessing a dentist which may be related to lack of availability or other factors such as anxiety, difficulties with physical access or affordability. However, identification of the importance of this variable also offers an opportunity for intervention to improve service utilisation and outcomes. Such interventions would need to be effective at changing the public perceptions of the accessibility of dental services. Information would need to be designed to make people aware of how to access dental services from sources and in a format appropriate to the public’s needs. Social marketing techniques , that identify what will motivate the public to change oral health behaviours, may be useful to provide insights into suitable media and methods to dispel myths about access difficulties. In dental service management terms, the dental contract introduced in England in 2006 removed the ability of a patient to register with a dentist . There is some evidence to suggest that being registered with a dentist has a positive impact on perceptions of access (22). A new dental contract will be piloted in England from April 2011 with the concept of registration restored .
Andersen highlighted the high degree of mutability of enabling resources such as perceived access to health services and contrasted this with immutable variables such as deprivation . This study used an area-based composite measure of deprivation, rather than an individual indicator of socio-economic status and found that deprivation was not as strong a predictor of oral health impacts as perceived access or perceived treatment need. A similar study in the UK which used social class, income and education also found enabling resources (oral health education, treatment expense, dental anxiety) and need to be stronger predictors of health behaviour and outcome than predisposing factors . Reisine in the US study also found income to have no significant effect on dental service use . Further research is needed to identify other factors that might mediate the relationships between pre-disposing factors such as deprivation and other variables within the model. Indeed, psychosocial resources (such as optimism, coping style or personal control) and social support have been shown to influence the relationship between socio-economic status and health . These findings contribute to debates about the degree to which socio-economic status and deprivation explains inequalities in access and utilisation of oral healthcare .
Limitations

While the use of structural equation modelling with observed variables provides a sophisticated analytical procedure to test the causal processes hypothesised by theoretical models , it should be recognised there are some limitations of its use in this study. First, a median imputation method was used to handle missing data which will have had the potential to reduce the variance of the variables, possibly resulting in underestimation of correlation between these variables and other variables within the model . However, it is not possible to use ADF with missing data. Second, the use of categorical indicators (e.g. ordinal data) within the model is not ideal, however the categorical variable methodology of ADF was employed to deal with this issue .


While the variables tested in this study explained a large amount of variance for both dental service utilization and oral health outcomes, half of the variance remains unexplained. Therefore other factors need to be considered in future including dental anxiety and costs of treatment . Due to the constraints of carrying out a secondary analysis of survey data it was not possible to examine these and other factors such as education, and occupation. It was also not appropriate to include ethnicity as 96.0% of the sample were White British. Further research is needed to identify and investigate the contribution of such factors. In addition, as the survey data were collected in the UK, the contribution of factors relating to the organisation and delivery of dental services also need to be considered.
Finally, although the response rate of 43.1% compares favourably with other UK postal surveys the possibility of non-response bias is an additional limitation of this study which may have influenced the significant factors identified. It may be that as the response rate was lower from those in more deprived areas [13] that the patterns of access seen in this study reflect the access of less deprived people who were more likely to respond.
Conclusions and implications

This was the first study to identify the public’s perceptions of difficulties accessing dental services as a key predictor of oral health outcomes. Despite the limitations of the study, this finding offers an opportunity for interventions to address public perceptions through the use of social marketing techniques or changes to the ways dental contracts are developed. An area-based composite measure of deprivation was used and found deprivation to be a weak predictor of oral health impacts compared to other variables within the model. The study builds on previous theoretical work using Andersen’s behavioural model to discuss the application of this finding by public health practitioners and policy makers. Further work is needed to develop effective interventions to change public perceptions and so improve service use and oral health outcomes.




Figure Legends:

Table 1. Responses to items included within the Adult Dental Health Survey (N=10864)

Table 2. Goodness of fit indices and multi-group analysis

Table 3. Significant direct and indirect pathways within the accepted model (Model 2)

Figure 1. Model of dental service use and oral health outcomes based on Andersen’s behavioural model (1995)
Figure 2. Adapted model of dental service use and oral health outcomes (derived from Andersen’s (1995) behavioural model and dental literature)
Table 1. Responses to items included within the Adult Dental Health Survey (N=10864)


Variable

N (%)

Predisposing factors

Deprivation (Index of Multiple Deprivation)

  1. Least deprived

  2. Less deprived

  3. Average

  4. More deprived

  5. Most deprived


Enabling resources–perceived difficulties accessing a dentist

Difficulty accessing routine care

  1. Yes

  2. Don’t know/can’t remember

  3. No


Need

Perceived treatment need

  1. Would need treatment

  2. Don’t know

  3. Would not need treatment


Use of health services

Reason for attending dentist

  1. To have a regular check up

  2. To have an occasional check up

  3. Only when trouble with teeth

  4. Never been


Length of time since last visit

  1. Up to 1yr ago

  2. Between 1 and 2yrs ago

  3. Between 2 and 5yrs ago

  4. More than 5yrs ago

  5. Never been


Health outcomes

Oral health related impacts

Painful aching in mouth (oral symptoms)



  1. Never

  2. Hardly ever

  3. Occasionally

  4. Fairly often

  5. Very often

Discomfort eating food (functional limitations)



  1. Never

  2. Hardly ever

  3. Occasionally

  4. Fairly often

  5. Very often

Self conscious (social impacts)



  1. Never

  2. Hardly ever

  3. Occasionally

  4. Fairly often

  5. Very often

Global oral health



  1. Excellent

  2. Very good

  3. Good

  4. Fair

  5. Poor

  6. Very poor



10849


1447 (13.3%)

2503 (23.0%)

2219 (20.4%)

2210 (20.3)

2470 (22.7%)

10769


2232 (20.5%)

854 (7.9%)

7547 (69.5%)

10585


2628 (24.2%)

2621 (24.1%)

5336 (49.1%)

10627


7352 (67.7%)

818 (7.5)

2145 (19.7%)

312 (2.9%)


10706

7761 (71.4%)

678 (6.2%)

801 (7.4%)

1334 (12.3%)

132 (1.2%)


10797


5424 (49.9%)

2352 (21.6%)

2339 (21.5%)

414 (3.8%)

268 (2.5%)
10810

4746 (43.7%)

2455 (22.6%)

2677 (24.6%)

539 (5.0%)

393 (3.6%)


10788

6108 (56.2%)

1611 (14.8%)

1831 (16.9%)

643 (5.9%)

595 (5.5%)


10769

994 (9.1%)

3160 (29.1%)

3856 (35.5%)

1975 (18.2%)

588 (5.4%)

196 (1.8%)


Table 2. Goodness of fit indices and multi-group analysis



Model fitting


Goodness of fit


CFI


(> 0.95)

IFI


(> 0.95)

RMSEA


(<0.08)

SRMR


(<0.05)

Comparison of two models



Criteria fitted




BCC


(Lowest value better fit)

ECVI


(Lowest value better fit)

Model 1 (N=10864)


0.74

0.74

0.15

-- *

1680.10

0.16

0/4

Model 2+ (N=10864)

(Accepted model)



0.98


0.98


0.05




0.02


175.83


0.02

4/4


Multi-group analysis+


Gender

Males (N=4832)

Females (N=6032)



0.98

0.98


0.98

0.98


0.06

0.05


0.02

0.02

--

--


--

--


4/4


4/4


Age

16-44 years (N=3920)

45-74 years (N=5647)

75 years and over (N=1297)





0.96

0.98

0.99


0.96

0.98

0.99


0.08

0.05

0.04


0.03

0.01

0.01

--

--



--

--

--



--

4/4


4/4

4/4


Random sub-groups

Group 1 (N=5432)

Group 2 (N=5432)



0.98

0.98


0.98

0.98


0.05

0.05


0.02

0.02






4/4


4/4

Alternative sample

Following list-wise deletion (N=10220)





0.98


0.98


0.06



0.02






4/4



+Multi-group analysis was based on model 2 (accepted model)

*Model not successfully fitted

Note: Figures in bold are those which meet the model-fitting criteria.

Model 1: 17 direct pathways hypothesised. Model 2: 14 direct pathways hypothesised

CFI=Comparative Fit Index; IFI=Incremental Fit Index; RMSEA=Root Mean Squared Error of Approximation; SRMR=Standardised Root Mean Square Residual; BCC=Browne & Cudeck Criterion; ECVI=Expected Cross-validation Index.

Table 3. Significant direct and indirect pathways within the accepted model (Model 2)




Significant pathways



Direct pathways
β value Bootstrap bias

corrected 95% CI




Indirect pathways
β value Bootstrap bias

corrected 95% CI




Total pathways
β value Bootstrap bias

corrected 95% CI


Deprivation → difficulties with access

Deprivation → perceived treatment need

Deprivation → reason for visiting dentist+

Deprivation → length of time since visit+

Deprivation → oral health impacts+

Deprivation → global oral health+

Difficulties with access → perceived treatment need

Difficulties with access → reason for visiting dentist

Difficulties with access → length of time since visit+

Difficulties with access → oral health impacts+

Difficulties with access → global oral health+

Perceived treatment need → length of time since visit

Perceived treatment need → reason for visiting dentist

Perceived treatment need → oral health impacts+

Perceived treatment need → global oral health+

Reason for visiting dentist → length of time since visit

Reason for visiting dentist → oral health impacts

Reason for visiting dentist → global oral health+

Length of time since visit → oral health impacts

Length of time since visit → global oral health

Oral health impacts → global oral health


-.03**


-.12**

.15**


--

.07**

--


.25**

.06**


.04**

-.12**

-.04**

-.11**


-.38**

-.41**

-.27**

.70**


--

--

-.01



.09**

.44**

-.05 to -.01

-.14 to -.11

.13 to .16

--

.05 to .09



--

.23 to .27

.03 to .09

.02 to .07

-.15 to -.11

-.05 to -.02

-.13 to -.09

-.40 to -.36

-.43 to -.39

-.29 to -.25

.68 to .71

--

--



-.03 to .01

.08 to .11

.43 to .46

--

-.01**



.05**

.15**


.06**

.11**


--

-.10**


-.05**

-.10**


-.17**

-.27**


--

.00


-.21**

--

-.01



.06**

--

-.01



--

--

-.01 to -.00



.04 to .06

.13 to .16

.05 to .07

.09 to .12

--

-.11 to -.09



-.07 to -.03

-.11 to -.09

-.18 to -.15

-.28 to -.25

--

-.00 to 01



-.23 to -.20

--

-.02 to .01



.05 to .08

--

-.01 to .00



--

-.03**


-.13**

.19**


.15**

.13**


.11**

.25**


-.04**

-.01


-.23**

-.21**


-.38**

-.38**


-.40**

-.48**


.70**

-.00


.06**

-.01


.09**

.44**

-.05 to -.01

-.15 to -.11

.17 to .21

.13 to .16

.11 to .15

.09 to .12

.23 to .27

-.07 to -.01

-.04 to .02

-.25 to -.21

-.23 to -.18

-.40 to -.36

-.40 to -.36

-.42 to -.39

-.50 to -.47

.68 to .71

-.02 to .01

.05 to .08

-.03 to .01

.07 to .11

.43 to .46

*p <0.05, **p <.0.01 + Various indirect pathways possible. Note: Significant predictors of oral health outcomes represented by direct pathways are highlighted in bold.



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