Social Stratification,Oral Hygiene,and Trajectories of Dental Caries Among Old Americans

Abstract

Objective: This study analyzed how oral hygiene (i.e., brushing, rinsing, and flossing) influences the trajectories of dental caries (i.e., numbers of decayed, missing, and filled teeth) among older Americans within the context of social stratification. Method: Data came from Piedmont Dental Study that involved a sample of 810 older Americans who were dentate in 1988 with up to four repeated observations through 1994. Hierarchical linear models were used for data analysis. Results: Brushing, flossing, and rinsing were associated with the trajectories of dental caries in distinct ways. In addition, oral hygiene was correlated with race, education, household income, and use of dental care. The effects of brushing and flossing on decayed and missing teeth remained robust, even when socio-demographic and health attributes were controlled. Conversely, socioeconomic disparities in dental caries persisted, when oral hygiene was adjusted. Discussion: Both social stratification and oral hygiene need to be considered in promoting oral health.

Keywords

oral hygiene dental caries hierarchical linear models Piedmont Dental Study social stratification

Introduction

In parallel with the extensive research on disparities in general health and health behavior, there is mounting evidence regarding oral health disparities and how they interface with health behaviors. Disparities in oral health have been well documented across developed nations (Sabbah, Tsakos, Chandola, Sheiham, & Watt, 2007; Sabbah, Tsakos, Sheiham, & Watt, 2009; Wu, Liang, Plassman, Remle, & Bai, 2011, Wu, Liang, Plassman, Remle, & Luo, 2012). In addition, poor oral hygiene is more prevalent among those with lower socioeconomic status (SES; Sabbah et al., 2009; Wiener et al., 2012). Finally, there is evidence that poor oral hygiene (e.g., brushing, flossing, and rinsing) is associated with worse oral health (Boehmer, Kressin, & Spiro, 1999; Kressin, Boehmer, Nunn, & Spiro, 2003; Sabbah et al., 2009).

These findings have led to the suggestion that improved oral hygiene and access to dental care play an important role in reducing oral health disparities (Wamala, Merlo, & Bostrom, 2006), which provides a major rationale for oral health preventive programs for decades (M. Chen, 1995; Pine et al., 2004). Nonetheless, according to current research on general health as well as oral health, improved health behaviors may reduce but do not eliminate health disparities (Lantz et al., 2006; Sabbah et al., 2009; Sanders, Spencer, & Slade, 2006). Therefore, attention needs to be directed to upstream (e.g., SES) as well as downstream (e.g., health behaviors) determinants in reducing health disparities (McKinlay & Marceau, 2000).

Much of the current research on oral health disparities and oral hygiene is based on cross-sectional data, which do not allow the researcher to distinguish intra-personal changes in oral health over time from inter-personal differences (e.g., age, gender, education, income) in oral status. Even when longitudinal studies are available, they tend to focus on transitions between two points in time, particularly the incidence of dental diseases and their risk factors (e.g., Kressin et al., 2003). To the best of our knowledge, there is no research that analyzes how oral hygiene is linked with the level of oral health and their rates of change over time. Furthermore, we know nothing about how these linkages vary by social stratification.

In this study, we examine the role of oral hygiene in explaining the linkages between social stratification and the trajectories of dental caries (i.e., decayed teeth, missing teeth, and filled teeth) among older Americans by using longitudinal data derived from a population-based sample of older Americans during a 5-year period (1988-1994). We conceptualize social stratification more broadly to encompass not only achieved status (i.e., education and income) but also ascribed status (i.e., age, gender, and race). This conceptualization is consistent with a long tradition in social sciences, particularly sociology. For instance, age stratification refers to the hierarchical ranking of people into age groups, a major source of inequality in the access to society’s rewards, power, and privileges (Riley, 1987). On the other hand, gender stratification cuts across all social class, and refers to men and women’s unequal access to power, prestige, and wealth on the basis of their sex (Lengermann & Niebrugge, 1996). Finally, race is a significant element of social stratification, particularly in the United States (Kao & Thompson, 2003). Minority groups are often excluded from full participation in economic and political institutions, and have less access to power, resources, and prestige. Because of historical and institutional reasons, ascribed status such as age, gender, and race, can be regarded as determinants of achieved SES (e.g., education and income).

There is compelling evidence that persons of lower SES (e.g., education and income) have worse health in terms of morbidity, functional limitation, disability, and mortality (Link & Phelan, 2010). SES influences health through several mechanisms, including (1) psychosocial resources (e.g., social networks and support, stress, sense of control); (2) health behaviors (e.g., smoking, physical activities, and alcohol use); (3) access to health care; and (4) environmental exposure (e.g., physical hazards and social capital; Adler & Newman, 2002; Robert & House, 2000; Ross & Mirowsky, 2010). In the present research, we focus on the role of oral hygiene as health behaviors in mediating the effects of social stratification on oral health, particularly in terms of the trajectories of dental caries.

On the other hand, increasing research has documented that oral health, particularly periodontal disease and number of teeth, is closely linked with cardiovascular disease, cause-specific mortality, and all-cause mortality (Abnet et al., 2005; Jansson, Lavestedt, & Frithof, 2002; Sabbah, Mortensen, Sheiham, & Batty, 2013). Hence, we include measures of general health (i.e., chronic conditions, cognitive impairment, and emotional functioning) as covariates in evaluating the robustness of linkages between social stratification, oral hygiene, and trajectories of dental caries.

Extrapolating from prior research, we pose the following general hypotheses regarding the linkages between oral hygiene and the trajectories of dental caries over time in the context of social stratification. First, the number of untreated decayed teeth decreases over time, partially due to an increase in missing teeth in old age (Griffin, Griffin, Swann, & Zlobin, 2005; Thomson, 2004). In addition, good oral hygiene is associated with not only fewer decayed teeth but also a greater rate of decline (Boehmer et al., 1999; Hypothesis 1). Second, whereas the number of missing teeth increases over time in old age (Lawrence, Hunt, & Beck, 1995; X. Chen & Clark, 2011), persons with good oral hygiene have fewer missing teeth and a slower rate of increase (Kressin et al., 2003; Hypothesis 2). Third, good dental hygiene is associated with more filled teeth, but makes no difference in the rate of increase (Hypothesis 3). This is partially based on the fact that number of filled teeth is a reflection of better access to dental care associated with higher SES. In addition, the number of filled teeth is negatively correlated with missing teeth and decayed teeth (Liang, Wu, Plassman, Bennett, & Beck, 2013).

Fourth, social stratification is significantly associated with the trajectories of dental caries, even when oral hygiene is taken into account (Hypothesis 4; Lantz et al., 2006; Sabbah et al., 2009). Specifically, we hypothesize that education and income are directly and indirectly (via oral hygiene) associated with the trajectories of dental caries. Age, gender, and race are associated with dental caries partially through their linkages with education and income as well as oral hygiene. However, oral hygiene and health conditions at the baseline are insufficient in accounting for all correlations between social stratification and dental caries.

Materials and Methods

Design and Sample

Data came from the Piedmont Dental Study (PDS), a random subsample of the parent study, the Duke Established Populations for Epidemiologic Studies of the Elderly (Duke EPESE), which was based on a stratified random clustered sample of all people aged 65 and over in the five adjacent counties in the Piedmont area of North Carolina in 1986. The PDS began in 1988 with a random subsample of 810 dentate individuals from the Duke EPESE. These respondents were asked to participate again at 18 months, 36 months, and 60 months follow-up, except for those who became edentulous or died. The final analytical sample consisted of 810 participants at the baseline with 2,926 observations over a period of 5 years.

Measures

Numbers of decayed, missing, and filled teeth were obtained from dental examinations in 1988, 1990, 1991, and 1994 as a part of PDS. Three measures of dental hygiene at baseline were included in the analysis. Brushing reflected the number of times the respondent brushed his or her teeth on the prior day (none, once, twice, or three or more times). Using a mouthwash or rinsing regularly was also assessed (1 = yes, 0 = no). The frequency of using dental floss was reported as not at all (0), less than once a week (1), once per week (2), several times per week (3), and daily (4). As a covariate, dental care utilization was assessed by the length of time since the last dental visit (i.e., in the last 6 months).

Measures of all covariates at the baseline were obtained from Duke EPESE in 1986. Indicators of social stratification included age, gender (male =1), and race (White =1). In addition, education was indexed by years of schooling, whereas household income at the baseline was indicated by quartiles, with the first quartile reflecting the lowest income. Because of the significant linkages between general and dental health (Griffin, Jones, Brunson, Griffin, & Bailey, 2012; Petersen & Yamamoto, 2005), several measures of physical and mental at the baseline were also included as covariates. Disease was obtained by a sum of the presence of the respondents reported diseases, that is, diabetes, high blood pressure, heart, stroke, and cancer. Depressive symptoms were assessed by using the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). Finally, cognitive functioning was ascertained by the number of incorrect answers to the Short Portable Mental Status Questionnaire (SPMSQ), which is a 10-item screening measure focusing mainly on orientation, serial subtraction, and memory (Pfeiffer, 1975). All measures were coded such that a higher score reflected poorer health, greater number of reported depressive symptoms, and some cognitive impairment. A more extended description of the sample, collection of clinical data, and interview measures can be found elsewhere (Graves, Beck, Disney, & Drake, 1992; Hunt, Drake, & Beck, 1995).

Data Analysis

Hierarchical linear models (HLMs) were used to chart the trajectories of decayed, missing, and filled teeth (Raudenbush & Bryk, 2002). The numbers of dental caries were positively skewed and contained many zeros, partially due to the large proportions of individuals with no decayed or filled teeth. Statistically, it might be better to treat them as counts or ordinal variables instead of continuous variables with a normal distribution (Long, 1997; Winship & Mare, 1984). Although we applied linear regression, ordered-logistic regression models, and regression with a negative binomial distribution, we obtained very similar results, which will be discussed later. For the ease of presentation, we only include the results based on linear regression models here. Results based on ordered-logistic regression models and regression models with a negative binomial distribution are available from the authors.

The intra-individual differences in dental caries (e.g., number of decayed teeth) were modeled as follows in the Level 1 equation:

Y_{i T} = π_{0 i} + π_{1 i} T i m e + ε_{i T},

where Y_iT is the number of decayed teeth of individual i at time T. π_0i is the intercept (i.e., level) and π_1i is the slope (i.e., rate of change) over time. Time is the distance (in years) of assessment from the baseline in 1988, when the respondent was first examined, and ϵ_iT is a random error. Time was centered on its grand mean (around 2.5 years). We also explored non-linear changes with time by incorporating a quadratic term of the time variable (i.e., ${Time}_{i j}^{2}$ in Equation 1.

Inter-personal variations in the trajectory of decayed teeth (i.e., intercept and slope) were specified in the Level 2 equation:

π_{p i} = β_{p 0} + Σ β_{p q} X_{q i} + r_{p i},

where X_qi is the qth time-constant covariate (e.g., brushing, flossing, rinsing, education, and income) associated with individual i, and β_pq represents the effect of variable X_q on the pth growth parameter ( $π$ _p; i.e., intercept and slope). r_pi is a random effect with a mean of 0. All time-constant covariates (Level 2) were not centered. Bayesian information criterion (BIC) was used as the goodness-of-fit index to select the optimal models. All models were fitted by using HLM version 6.06 (Raudenbush, Bryk, & Congdon, 2008).

To minimize the loss of participants due to item non-response, multiple imputation (MI) was undertaken. In particular, five complete data sets were imputed with the NORM software developed by Schafer (1997) and analyses were run on each of these five data sets with parameter estimates derived by averaging across five imputations and by adjusting for their variance. As a major advantage, multilevel models can include all participants in the estimation, regardless how many observations they contributed to the data set. With reference to attrition, multilevel models are predicated on the assumption of missing at random (MAR) that the probability of missing depends upon only the observed data for either the covariates or the outcome variables, hence permitting valid inference (Raudenbush & Bryk, 2002). In addition to MAR, to adjust for the selection bias due to attrition, we included dummy variables in the level-2 equation to differentiate those with complete data during the period of study from those who dropped out of the study. They were viewed as confounding variables instead of predictors of dental caries experience.

Results

Descriptive statistics of dental caries, oral hygiene, and their covariates are presented in Table 1. In particular, the number of observations at the baseline was 810 that declined to 363 at the 60-month follow-up, largely due to edentulism, mortality, or loss to follow-up. The reduction of mean number of missing teeth at 36 months follow-up may be a result of increasing number of individuals who became edentulous and thus were removed from the sample. With reference to oral hygiene, the respondents brushed their teeth on average 1.6 times per day. Fifty-five percent of them used mouth rinse, whereas 68% of them did not floss their teeth at all.

Table 1.

Descriptive Statistics (N = 810).

Baseline (N = 810)	M	SD	%	Time varying	M	SD	N
Age	73.33	5.71		Number of decayed teeth
Male	—	—	40.74	Baseline 1988	1.74	2.98	810
White	—	—	44.69	18-months follow-up	1.52	2.65	622
Education	9.65	4.53		36-months follow-up	1.46	2.44	452
Income (in quartiles)				60-months follow-up	1.23	2.60	363
1st (lowest)	—	—	31.11	Number of missing teeth
2nd	—	—	24.81	Baseline 1988	13.73	8.04	810
3rd	—	—	22.59	18-months follow-up	14.07	8.42	622
4th (highest)	—	—	21.48	36-months follow-up	13.64	7.86	452
Married	—	—	46.67	60-months follow-up	14.19	8.05	363
Cognitive impairment	1.43	1.65		Number of filled teeth
# total health conditions	1.06	0.93		Baseline 1988	5.97	6.88	810
Depressive symptoms	2.89	3.29		18-months follow-up	6.28	6.98	622
Last dental visit	2.91	1.33		36-months follow-up	6.62	6.96	452
In the last 6 months	—	—	13.33	60-months follow-up	6.67	7.00	363
6 months to < 1 year	—	—	31.48
1 to 2 years	—	—	24.69
2 to 3 years	—	—	10.62
3 years or more	—	—	19.88
# times brush teeth yesterday	1.60	0.84
Use mouth rinse	—	—	55.01
Times floss in week
Not at all	—	—	68.40
<Once per week	—	—	3.46
Once per week	—	—	5.56
Several times per week	—	—	10.25
Daily	—	—	12.35
Dropout for good	—	—	34.07
Return after dropout	—	—	9.51
Death	—	—	20.37
Proxy interview	—	—	8.15

Oral Hygiene and Trajectory of Decayed Teeth

The respondents had an average of 1.59 decayed teeth at the middle of the 5-year period of observation that decreased slightly over time (b = −.059, p = .005; Model 0, Table 2). As illustrated in Model 1 in Table 2, more brushing (b = −.524, p < .001) and flossing (b = −.295, p < .001) were associated with fewer decayed teeth but not the rate of change in decayed teeth. Rinsing was uncorrelated with the trajectory of decayed teeth. Those who brushed and flossed regularly had -.524 and -.295 fewer decayed teeth respectively than those who did not, and these differences persisted over time (Figure 1). As a covariate, a longer interval since the last dental visit was associated with more decayed teeth (b = .297, p < .001) but not the rate of change in decayed teeth (Model 1, Table 2). These results provide partial support for Hypothesis 1 in that brushing and flossing were associated with fewer decayed teeth, although they were not correlated with the rate of decline in decayed teeth.

Table 2.

Multilevel Linear Regression Analysis of Number of Decayed Teeth (N = 810 With 2,926 Observations).

	Model 0		Model 1		Model 2		Model 3
Fixed effects	Coefficient	p value	Coefficient	p value	Coefficient	p value	Coefficient	p value
For the intercept
Intercept	1.589	<.001	1.739	<.001	3.953	.003	4.263	.002
Dropout for good (vs. complete data)					−0.019	.921	−0.090	.630
Returned after dropout (vs. complete data)					0.090	.768	0.096	.757
Death (vs. survivor)					0.640	.058	0.533	.109
Proxy interview (vs. non-proxy)					0.381	.475	0.380	.463
Age (in years)					−0.019	.183	−0.028	.059
Male (vs. female)					0.696	<.001	0.487	.012
White (vs. Black)					−0.463	.021	−0.172	.387
Education (in years)					−0.041	.130	−0.010	.713
Household income (in quartiles)					−0.497	<.001	−0.385	<.001
BMI					0.011	.636	0.001	.962
Depression					0.029	.339	0.021	.475
Cognitive function					0.064	.456	0.050	.554
Diseases					−0.061	.559	−0.027	.785
Last dental visit (higher = longer time)			0.297	<.001			0.211	.024
Brush			−0.524	<.001			−0.338	.020
Rinse			0.209	.248			0.139	.463
Floss			−0.295	<.001			−0.152	<.001
For the slope
Intercept	−0.059	.005	0.009	.910	−0.097	.764	−0.011	.973
Dropout for good (vs. complete data)					−0.042	.489	−0.034	.570
Returned after dropout (vs. complete data)					−0.116	.095	−0.110	.114
Death (vs. survivor)					−0.052	.592	−0.042	.667
Proxy interview (vs. non-proxy)					0.058	.659	0.062	.639
Age (in years)					0.004	.288	0.004	.265
Male (vs. female)					−0.003	.937	0.004	.930
White (vs. Black)					0.075	.122	0.060	.246
Education (in years)					−0.002	.774	−0.003	.649
Household income (in quartiles)					−0.009	.767	−0.015	.590
BMI					−0.008	.059	−0.008	.065
Depression					−0.006	.412	−0.006	.429
Cognitive function					−0.017	.409	−0.018	.405
Diseases					0.043	.056	0.042	.062
Last dental visit (higher = longer time)			−0.030	.184			−0.028	.226
Brush			0.005	.895			−0.008	.823
Rinse			0.009	.843			0.015	.741
Floss			0.012	.256			0.008	.506
Random effect	Variance	p value	Variance	p value	Variance	p value	Variance	p value
Intercept	6.185	<.001	5.228	<.001	5.251	<.001	5.042	<.001
Slope	0.109	<.001	0.110	<.001	0.106	<.001	0.108	<.001
Level 1	1.331		1.333		1.334		1.334

Note. BMI = body mass index.

Figure 1.

Trajectory of decayed teeth by brushing and flossing.

Social stratification was significantly associated with the number of decayed teeth as well as oral hygiene. Specifically, men (b = .696, p < .001), Blacks (b = −.463, p = .021), and those with lower income (b = −.497, p < .001) had more decayed teeth, although there was no age and education difference in decayed teeth (Model 2, Table 2). With reference to oral hygiene, education evidenced a positive relationship with the frequencies of brushing (b = .035, p < .001) and flossing (b = .080, p < .001). A similar pattern was noted when examining household income. However, neither education nor income was associated with the use of a mouth rinse (Table 3).

Table 3.

Regression Analysis of Dental Visit and Oral Hygiene in 1988 (N = 810).

	Time since last dental visit (higher = longer)^a		No. of times brushed teeth yesterday^a		How often floss teeth^a		Uses a mouth rinse^b
Covariates	β	p value	β	p value	β	p value	β	p value	Exp(B)
Constant	2.927	<.001	1.617	<.001	0.704	.264	1.182	.250	3.261
Dropout for good (vs. complete data)	0.182	.072	−0.016	.792	−0.186	.087	0.124	.482	1.132
Returned after dropout (vs. complete data)	0.204	.182	0.166	.085	0.003	.978	0.477	.088	1.612
Death (vs. survivor)	0.171	.159	−0.085	.262	−0.335	.011	−0.508	.016	0.602
Proxy interview (vs. non-proxy)	−0.058	.633	−0.136	.181	−0.010	.802	−0.752	.008	0.472
Age (in years)	0.013	.087	−0.008	.109	−0.013	.102	−0.006	.663	0.994
Male (vs. female)	0.152	.128	−0.290	<.001	−0.528	<.001	−0.332	.042	0.717
White (vs. Black)	−0.555	<.001	0.304	<.001	0.471	<.001	−0.825	<.001	0.438
Education years (in years)	−0.049	<.001	0.035	<.001	0.080	<.001	0.014	.520	1.014
Household income (in quartiles)	−0.198	<.001	0.095	.006	0.261	<.001	−0.038	.698	0.963

Linear regression.

Logistic regression.

When social stratification and baseline health status were controlled, the effects of brushing and flossing attenuated somewhat but remained significant (Models 1 and 3, Table 2). This suggests that the effects oral hygiene cannot be explained by variations in social stratification and health status. At the same time, the effects of gender, race, education, and income on the level of decayed teeth attenuated with oral hygiene taken into account (Models 2 and 3, Table 2). This indicates that the effects of social stratification on the number of decayed teeth are partially mediated by oral hygiene, particularly brushing and flossing (Hypothesis 4).

Oral Hygiene and Trajectory of Missing Teeth

With an average of 14.60 missing teeth 2.5 years after the baseline, the number of missing teeth increased by .359 teeth per year (b = .359, p < .001 in Model 0, Table 4). More brushing was associated with fewer missing teeth (b = −1.121, p = .006) and a smaller increase in missing teeth (b = −.077, p = .047). More flossing was correlated with fewer missing teeth (b = −1.363, p < .001) as well as a lower rate of increase (b = −.069, p < .001), while rinsing, in contrast, was uncorrelated with the trajectory of missing teeth (Model 1, Table 4). As shown in Figure 2, at the middle of the follow-up, those who brushed and flossed regularly had respectively 1.12 and 1.36 fewer missing teeth than those who did not, although such differences diminished somewhat over time (Figure 2). These results are consistent with the hypothesized effects of oral hygiene on the number of missing teeth (Hypothesis 2).

Table 4.

Multilevel Linear Regression Analysis of Number of Missing Teeth (N = 810 With 2,926 Observations).

	Model 0		Model 1		Model 2		Model 3
Fixed effects	Coefficient	p value	Coefficient	p value	Coefficient	p value	Coefficient	p value
For the intercept
Intercept	14.605	<.001	15.667	<.001	−2.981	.502	0.033	.994
Dropout for good (vs. complete data)					2.128	.003	1.890	.006
Returned after dropout (vs. complete data)					0.810	.368	0.911	.308
Death (vs. survivor)					0.922	.244	0.498	.522
Proxy interview (vs. non-proxy)					−0.526	.658	−0.533	.646
Age (in years)					0.268	<.001	0.242	<.001
Male (vs. female)					0.199	.748	−0.629	.318
White (vs. Black)					−2.322	.001	−1.506	.031
Education (in years)					−0.190	.039	−0.082	.384
Household income (in quartiles)					−0.987	.011	−0.604	.108
BMI					0.093	.147	0.046	.469
Depression					−0.065	.454	−0.100	.238
Cognitive function					−0.224	.320	−0.269	.223
Diseases					0.153	.629	0.250	.424
Last dental visit (higher = longer time)			0.538	.029			0.179	.483
Brush			−1.121	.006			−0.848	.037
Rinse			0.867	.127			0.393	.495
Floss			−1.363	<.001			−1.033	<.001
For the slope
Intercept	0.359	<0.001	0.504	<.001	1.082	.013	1.378	.002
Dropout for good (vs. complete data)					0.218	.020	0.205	.026
Returned after dropout (vs. complete data)					0.116	.177	0.130	.132
Death (vs. survivor)					0.019	.760	−0.009	.885
Proxy interview (vs. non-proxy)					−0.062	.587	−0.055	.614
Age (in years)					−0.009	.061	−0.011	.026
Male (vs. female)					0.032	.538	−0.028	.606
White (vs. Black)					−0.212	.002	−0.166	.020
Education (in years)					−0.022	.060	−0.014	.209
Household income (in quartiles)					0.037	.345	0.061	.117
BMI					0.006	.356	0.002	.746
Depression					−0.011	.136	−0.013	.071
Cognitive function					−0.019	.255	−0.023	.147
Diseases					0.022	.512	0.032	.327
Last dental visit (higher = longer time)			0.007	.710			−0.004	.825
Brush			−0.077	.047			−0.074	.074
Rinse			0.052	.321			0.016	.791
Floss			−0.069	<.001			−0.066	<.001
Random effect	Variance	p value	Variance	p value	Variance	p value	Variance	p value
Intercept	70.301	<.001	61.695	<.001	61.339	<.001	58.965	<.001
Slope	0.307	<.001	0.273	<.001	0.294	<.001	0.280	<.001
Level 1	0.953		0.971		0.938		0.944

Note. BMI = body mass index.

Figure 2.

Trajectory of missing teeth by brushing and flossing.

The number of missing teeth was significantly associated with social stratification (Model 2, Table 4). Adjustment of social stratification and health status at the baseline attenuated the effects of brushing and flossing on the level of missing teeth somewhat, but they remained significant (Models 1 and 3, Table 4). While the association between flossing and a lower rate of increase in missing teeth persisted, brushing was no longer significant in affecting the rate of change in missing teeth. Equally important, the effects of social stratification also attenuated when oral hygiene was taken into account (Models 2 and 3, Table 4). This supports the mediating role of oral hygiene with reference to the effects of social stratification on the trajectory of missing teeth.

Oral Hygiene and Trajectory of Filled Teeth

On average, the participants had 5.89 filled teeth at the middle of the follow-up period (i.e., 2.5 years after 1988). The number of filled teeth remained stable over time (b = −.033, p = .066; Model 0, Table 5). The number of filled teeth was positively associated with more brushing (b = 1.487, p < .001) and flossing (b = 1.701, p < .001), and these differences remained throughout the 5-year period of observation (Model 1, Table 5 and Figure 3). In contrast, more rinsing (b = −1.637, p < .001) and a longer interval since the last dental visit (b = −1.444, p < .001) were associated with fewer filled teeth. Oral hygiene was uncorrelated with the rate of change in filled teeth. These findings are somewhat mixed and only offer limited support for Hypothesis 3.

Table 5.

Multilevel Linear Regression Analysis of Number of Filled Teeth (N = 810 With 2,926 Observations).

	Model 0		Model 1		Model 2		Model 3
Fixed effects	Coefficient	p value	Coefficient	p value	Coefficient	p value	Coefficient	p value
For the intercept
Intercept	5.891	<.001	6.986	<.001	2.206	.421	1.938	.454
Dropout for good (vs. complete data)					−0.579	.182	−0.233	.562
Returned after dropout (vs. complete data)					−0.275	.671	−0.175	.770
Death (vs. survivor)					−0.396	.412	0.085	.855
Proxy interview (vs. non-proxy)					0.232	.691	0.198	.706
Age (in years)					−0.038	.238	−0.007	.829
Male (vs. female)					−1.291	.001	−0.497	.185
White (vs. Black)					4.738	<.001	3.652	<.001
Education (in years)					0.338	<.001	0.212	<.001
Household Income (in quartiles)					1.498	<.001	1.033	<.001
BMI					−0.045	.172	−0.005	.868
Depression					0.020	.683	0.047	.283
Cognitive function					−0.147	.180	−0.125	.205
Diseases					−0.218	.245	−0.275	.124
Last dental visit (higher = longer time)			−1.441	<.001			−0.828	<.001
Brush			1.478	<.001			0.540	.018
Rinse			−1.637	<.001			−0.515	.124
Floss			1.701	<.001			0.976	<.001
For the slope
Intercept	−0.033	.066	−0.028	.664	−0.031	.924	−0.033	.921
Dropout for good (vs. complete data)					0.054	.379	0.052	.401
Returned after dropout (vs. complete data)					0.014	.796	0.016	.771
Death (vs. survivor)					0.090	.198	0.091	.196
Proxy interview (vs. non-proxy)					−0.047	.457	−0.051	.424
Age (in years)					0.001	.880	0.001	.874
Male (vs. female)					0.021	.592	0.022	.595
White (vs. Black)					−0.009	.824	−0.007	.871
Education (in years)					0.001	.866	<0.001	.920
Household income (in quartiles)					−0.010	.649	−0.010	.650
BMI					−0.002	.637	−0.001	.707
Depression					−0.003	.549	−0.003	.537
Cognitive function					<0.001	.969	<0.001	.995
Diseases					<0.001	.981	0.001	.973
Last dental visit (higher = longer time)			0.007	.583			0.004	.717
Brush			−0.017	.544			−0.013	.647
Rinse			−0.009	.811			−0.007	.858
Floss			0.006	.705			0.008	.669
Random effect	Variance	p value	Variance	p value	Variance	p value	Variance	p value
Intercept	45.556	<.001	26.104	<.001	22.532	<.001	19.324	<.001
Slope	0.042	<.001	0.043	<.001	0.045	<.001	0.046	<.001
Level 1	1.404		1.404		1.406		1.406

Note. BMI = body mass index.

Figure 3.

Trajectory of filled teeth by brushing and flossing.

Social stratification is associated with the number of filled teeth. For instance, higher education and income were associated with more filled teeth (Model 2, Table 5). With social stratification and baseline health status controlled, the effects of brushing and flossing on the level of the trajectory of missing teeth were attenuated but remained significant, while the effect of rinsing became insignificant (Models 1 and 3, Table 5). At the same time, the effects of social stratification remained robust even with oral hygiene controlled (Models 2 and 3, Table 5).

Discussion

This research contributes to our understanding of the linkages between oral hygiene and the trajectories of dental caries in later life. Even among older adults, oral hygiene is effective in controlling dental caries, although its effect varies across decayed teeth, missing teeth, and filled teeth. For instance, more brushing and flossing were associated with reduced numbers of decayed and missing teeth, whereas more rinsing did not appear to be useful in this respect. In addition, brushing and flossing were correlated with more filled teeth, but rinsing was associated with fewer filled teeth. Finally, more brushing and flossing were associated with lower rates of increase in missing teeth. However, oral hygiene was not correlated with the rates of change in decayed and filled teeth.

Prior research based on a sample of middle-aged and older White men in the VA Dental Longitudinal Study (DLS) has documented significant positive associations between most preventive behaviors (i.e., brushing, flossing, and dental prophylaxis) and measures of oral health status including numbers of functioning teeth and decayed, missing, and filled teeth (Boehmer et al., 1999). In addition, oral hygiene behaviors (except brushing) were associated with an increased baseline number of teeth and decreased subsequent tooth loss (Kressin et al., 2003). The present research extends prior observations in at least two respects. First, it yields new information regarding the association between oral hygiene and the rates of changes in dental caries. More brushing and flossing were associated with a lower rate of increase in missing teeth over time. However, oral hygiene was not correlated with the changes in decayed teeth and filled teeth. Second, in contrast to DLS, our sample included Black and White men and women. Hence, we are able to generalize our observations regarding the linkages between oral hygiene and the trajectories of dental caries across racial and gender categories.

Because social stratification is significantly associated with both oral hygiene and oral health, it is important to examine how oral hygiene is linked with the trajectories of dental caries in the context of social stratification. The effects of oral hygiene on dental caries attenuated somewhat but remained significant, when social stratification is controlled. At the same time, oral hygiene partially mediated the effects of social stratification on the trajectories of dental caries. Much of the current research on oral health disparities and oral hygiene is based on cross-sectional data (e.g., Sabbah et al., 2009). Our research has added an important dynamic dimension to our knowledge. In particular, social stratification was directly and indirectly associated with the trajectories of dental caries with oral hygiene playing a mediating role.

The PDS database does not include measures of wealth or net worth. We have chosen education and household income as the key measures of SES for the following reasons. First, there is strong evidence that education in conjunction with income explains most of the association between SES and health. Second, the evidence regarding the impact of wealth on oral health is mixed. Although some research revealed that wealth-related differences in dental service use were consistently higher than were income-related differences (Allin, Masseria, & Mossialos, 2009), other investigators observed similar differences in oral health with different indicators of SES including education, income, and wealth (Tsakos, Demakakos, Breeze, & Watts, 2011). Third, education and income may be regarded as proxies for wealth, as they are significantly correlated. Taking all these together, we are uncertain whether net worth would add significantly to the impact of SES on the trajectories of dental caries above and beyond those contributed by education and income. Further research is clearly warranted.

In addition to oral hygiene, social stratification may influence oral health through dental insurance, a measure of access to dental care. Among adults age 51 and over in the U.S., some 47% have dental insurance. Dental insurance is more common among those with higher education and income. At the same time, poor older adults were more likely to have dental coverage than low-income older adults, suggesting that the minimally available Medicaid dental coverage may be reaching some poor older adults (Manski et al., 2010). Although we did not include dental insurance in our model, we included education, income, and dental care use in our analysis, which are key determinants and consequence of dental insurance. Hence, we believe there is minimum bias resulting from this omission.

We have subjected the linkages among social stratification, oral hygiene, and dental caries to a more rigorous evaluation by taking general health conditions, mortality, attrition, and proxy interview into account as they are likely to be significantly correlated with poor oral health. The effects of social stratification and oral hygiene remained robust, when we controlled for baseline health conditions, mortality, attrition, and proxy interview. By and large they were uncorrelated with the trajectories of dental caries with one exception. That is, those who dropped out of the PDS without completing all the follow-ups had more missing teeth (b = 1.890, p = .006, Model 3, Table 4) and a greater rate of increase in missing teeth (b = 0.205, p = .026, Model 3, Table 4). Hence, in this particular context, our estimates are unlikely to be confounded by baseline health, mortality, attrition, and proxy interview.

Oral health among older adults is a high priority in public health because of the growing population of older Americans, the disproportionate burden of oral diseases, and disparities in access to dental care (Lamster, 2004; Petersen & Yamamoto, 2005). There is evidence that among older adults, failure to prevent or control oral disease (e.g., periodontal disease) may increase the risk of adverse health outcomes including diabetes and heart disease, while good oral hygiene is associated with positive health outcomes (Griffin et al., 2012). Understanding the trajectories of the linkages between oral hygiene and trajectories of dental caries would bring new insights into interventions in promoting and maintaining optimal oral health in old age. That is, it is not only important to reduce the levels of caries but also their rates of change over time. For instance, while it is important to reduce the number of decayed teeth, we may also bend the curve to accelerate its rate of decline. With reference to oral health policy, our findings offer support for a two-prong approach. While oral health education and the promotion of oral hygiene are important in controlling dental caries among older adults, reducing social disparities in conjunction with improved general access to dental care would contribute to oral health as well. Indeed, reducing disparities in education and income may lead to better oral hygiene, which in turn is associated with better oral health.

This research can be improved in several respects. First, our measures of oral hygiene were obtained at the baseline only; thus, we did not assess how changes in oral hygiene affected the trajectories of dental caries. This may lead to underestimation of the effects of intra-personal changes in oral hygiene on the trajectories of caries as reported by Kressin et al. (2003) that baseline and long-term hygiene behaviors were associated with baseline and subsequent tooth loss. Second, the dental care use measure did not differentiate preventive care from treatment. Consequently, the effects of preventive dental care as a type of oral hygiene practice on dental caries could not be assessed.

Third, untreated decayed teeth, missing teeth, and filled teeth are competing outcomes that are correlated. Consequently, baseline measures of caries may influence our estimates of the trajectories. To evaluate this possibility, we included baseline caries other than the outcome measure as covariates in our models. For instance, in predicting the trajectory of decayed teeth, numbers of missing teeth and filled teeth at the baseline were negatively associated with the level of decayed teeth but not its rate of change. Similar results were observed for the trajectories of missing teeth and filled teeth. More importantly, our estimates of the linkages among social stratification, oral hygiene, and the trajectory of a given measure of caries remain robust, even when baseline measures of other caries were controlled.

Fourth, one may be concerned that linear regression models were used to analyze dental caries that had skewed distributions. To address this concern, in addition to linear regression models, we undertook analyses by using ordered logit models as well as regression models with a negative binomial distribution. The results from these approaches were quite similar to those derived from linear regression models. The only exceptions were the significant but very small effects of brushing (b = .005, p = .011) and flossing (b = −.004, p < .001) on the rate of change in filled teeth revealed by the regression models with a negative binomial distribution but not by linear regression or ordered logit analyses. Because of the very small magnitude of these effects, they are likely of limited substantive significance.

Fifth, the present research is predicated on the assumption that the population is homogeneous with respect to how the predictors operate on the outcomes. It is possible that the population is heterogeneous with respect to how the predictors operate on the outcomes. For instance, using the group-based mixture models outlined by Nagin (2005), Broadbent, Thomson, and Poulton (2008) identified three distinct trajectories of dental caries experience measured by DMFS (decayed, missing, and filled surfaces) up to age 32. Further analysis of multiple trajectories of dental caries would yield valuable information, particularly concerning the heterogeneity in changes in oral health over time.

Finally, our findings need to be replicated with more recent data, as the last time observations were made in PDS was in 1994. In contrast with recent data derived from the National Health and Nutrition Examination (NHANES, 1999-2004), subjects in the PDS appeared to be less well-off and had poorer oral health. These differences could be in part due the fact the respondents of PDS were drawn from a limited region in North Carolina with an oversample of Blacks. Nevertheless, oral health disparities persist across racial/ethnic groups, despite that the differences between groups typically diminish when socioeconomic, health-related, and behavioral factors are considered (Wu, et al., 2011).

In summary, oral hygiene including brushing and flossing have a significant positive impact on the trajectories of dental caries, whereas rinsing appears less consequential. In addition, social stratification is important in influencing changes in dental caries over time directly as well as indirectly through oral hygiene. These findings suggest that both upstream and downstream determinants need to be addressed in improving the level of oral health and its rate of change among older adults.

Footnotes

Acknowledgements

We thank Celia Hybels and Lawrence Landerman for their comments on the analysis.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by Grants R21 DE19518 and R01 DE08060 (Bei Wu, PI) from the National Institute of Dental and Craniofacial Research.

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