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The Interplay between Socioeconomic Inequalities and Clinical Oral Health J. Steele, J. Shen, G. Tsakos, E. Fuller, S. Morris, R. Watt, C. Guarnizo-Herreño and J. Wildman J DENT RES published online 24 October 2014 DOI: 10.1177/0022034514553978 The online version of this article can be found at: http://jdr.sagepub.com/content/early/2014/10/20/0022034514553978

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research-article2014

JDRXXX10.1177/0022034514553978Journal of Dental ResearchSocioeconomic Inequalities and Clinical Oral Health

Research Reports: Clinical

The Interplay between Socioeconomic Inequalities and Clinical Oral Health

Journal of Dental Research 1­–8 © International & American Associations for Dental Research 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0022034514553978 jdr.sagepub.com

J. Steele1, J. Shen2, G. Tsakos3, E. Fuller4, S. Morris5, R. Watt3, C. Guarnizo-Herreño3, and J. Wildman6

Abstract Oral health inequalities associated with socioeconomic status are widely observed but may depend on the way that both oral health and socioeconomic status are measured. Our aim was to investigate inequalities using diverse indicators of oral health and 4 socioeconomic determinants, in the context of age and cohort. Multiple linear or logistic regressions were estimated for 7 oral health measures representing very different outcomes (2 caries prevalence measures, decayed/missing/ filled teeth, 6-mm pockets, number of teeth, anterior spaces, and excellent oral health) against 4 socioeconomic measures (income, education, Index of Multiple Deprivation, and occupational social class) for adults aged ≥21 y in the 2009 UK Adult Dental Health Survey data set. Confounders were adjusted and marginal effects calculated. The results showed highly variable relationships for the different combinations of variables and that age group was critical, with different relationships at different ages. There were significant income inequalities in caries prevalence in the youngest age group, marginal effects of 0.10 to 0.18, representing a 10- to 18-percentage point increase in the probability of caries between the wealthiest and every other quintile, but there was not a clear gradient across the quintiles. With number of teeth as an outcome, there were significant income gradients after adjustment in older groups, up to 4.5 teeth (95% confidence interval, 2.26.8) between richest and poorest but none for the younger groups. For periodontal disease, income inequalities were mediated by other socioeconomic variables and smoking, while for anterior spaces, the relationships were age dependent and complex. In conclusion, oral health inequalities manifest in different ways in different age groups, representing age and cohort effects. Income sometimes has an independent relationship, but education and area of residence are also contributory. Appropriate choices of measures in relation to age are fundamental if we are to understand and address inequalities. Keywords: healthcare disparity, income, tooth, periodontal diseases, dental caries, socioeconomic factors Received 10 June 2014; Last revision 5 September 2014; Accepted 11 September 2014

Introduction Good health is important to overcoming the other ethical and economic effects of disadvantage (Braveman and Gruskin 2003). Inequalities in health have been well documented, and reducing them is a priority for many governments (Marmot et al. 2008). Oral health is not immune from these; the phenomenon is widespread, with poorer people or those from a disadvantaged social position having poorer oral health (Stahlnacke et al. 2003; Sanders and Spencer 2005; Wamala et al. 2006; Turrell et al. 2007; Morita et al. 2007; Tsakos et al. 2011; Elani et al. 2012). In many cases, there is evidence of a social gradient with an incremental reduction in oral health from richest to poorest, but the nature and shape of the gradients are highly variable and may depend on the measures used (Morita et al. 2007; Sabbah et al. 2007; Holst 2008; Geyer et al. 2010; Tsakos et al. 2011; Elani et al. 2012). Nevertheless, the scale of the

inequalities in oral health, as estimated by measures such as the concentration index, is substantial and appears to be at

1

School of Dental Sciences and Centre for Oral Health Research, Newcastle University, UK 2 Institute of Health and Society, Newcastle University, UK 3 Department of Epidemiology and Public Health, University College London, UK 4 NatCen Social Research, Northampton Square, London, UK 5 Department of Applied Health Research, University College, London, UK 6 Economics, Newcastle Business School, Newcastle University, UK A supplemental appendix to this article is published electronically only at http://jdr.sagepub.com/supplemental. Corresponding Author: J. Steele, University of Newcastle-upon-Tyne, Framlington Place, Newcastle upon Tyne, NE2 4BW, United Kingdom. Email: [email protected]

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Journal of Dental Research

least similar to those for general health measures (Sabbah et al. 2007; Ravaghi et al. 2013; Shen et al. 2013). Damage accumulates in teeth in a unique way, providing visible evidence of their (and their owner’s) complex history. What we see represents different disease processes and behavioral responses, so we hypothesize that different clinical measures may tell very different stories at different points across the life course. If we want to understand and address inequalities in oral health, we need to understand how to approach and interpret oral health measurement in this context. Research to date has used a range of measures—including self-reported outcomes (Sanders and Spencer 2005; Wamala et al. 2006; Turrell et al. 2007; Tsakos et al. 2011), clinical measures (e.g., periodontal and caries experience measures and number of teeth; Lopez et al. 2006; Morita et al. 2007; Sabbah et al. 2007; Geyer et al. 2010; Elani et al. 2012), and various combinations of both (Stahlnacke et al. 2003; Sanders and Spencer 2004; Sanders, Slade, et al. 2006; Sanders, Spencer, et al. 2006; Celeste et al. 2009; Ravaghi et al. 2013)—but there has been little justification given for the selection. To date, no studies have sought to identify how we should approach this and whether it matters which we choose. Researchers into inequalities in general health have observed significant relationships with age, although these are far from clear and may depend on the measure used (Huisman et al. 2003; Chandola et al. 2007; Demakakos et al. 2008). In oral health, previous investigations suggest that the scale of inequalities vary with age and depend on the measure (Sanders and Spencer 2004; Shen et al. 2013; Guarnizo-Herreño et al. 2014). When trying to interpret the relationship between oral health and socioeconomic factors, we therefore need to pay careful attention to age and probably generation (cohort). This article aims to investigate oral health inequalities using diverse indicators of oral health outcome and 4 socioeconomic determinants, in the context of age and cohort. By investigating the relationships between income (alongside other socioeconomic predictors of oral health outcome) and age group, we can start to infer how such inequalities may emerge. Without this understanding, it is difficult fully to interpret the existing or future literature or to address the inequalities observed.

Methods We used data from the 2009 UK Adult Dental Health Survey (Steele and O’Sullivan 2011), which was based on a nationally representative sample of 11,380 individuals (among which 6,469 dentate adults had an oral examination), providing information on individual dental health and socioeconomic status. Full details of sampling are found in the survey technical report (O’Sullivan et al. 2011). Ethics approval was obtained through the National Research

Ethics Service in the United Kingdom, and written consent was obtained in all cases. We restricted our sample to the population aged ≥21 y to ensure accurate data on educational attainments. For all health outcomes, the final sample comprised those dentate participants who agreed to take part in an oral examination. The exception was for the analysis of number of teeth, where clearly it was important to include the edentate. The full sample size for dentate analysis was 5,084 but 5,404 for the analysis pertaining to the number of teeth. Weighted data were used. Missing data occurred at very low frequency with the exception of income (17.4%), a common finding in the literature (Turrell 2000), so we tested multiple imputation. We observed little difference with and without imputation, and the sample was still representative. Consequently, data are based on the largest sample with complete information on the outcomes and all independent variables. Seven diverse clinical oral health indicators were chosen to represent different ways in which oral disease or its sequelae may be measured. Four were directly related to disease: the presence of decay; the existence of teeth unrestorable due to decay; the number of decayed, missing, and filled teeth (DMFT); and the existence of any periodontal pocket of 6+ mm. Three further measures were added that were not disease specific but captured various possible compound effects of disease and treatment choices— namely, the number of natural teeth; having 3 or more unfilled upper spaces (to capture aesthetics); a dichotomous composite measure of excellent oral health (21+ teeth, 18+ sound and untreated teeth, no decay or pockets of ≥4 mm). The purpose was not to find a single perfect measure but to understand how apparent social inequalities in oral health vary according to diverse clinical indicators. We undertook multiple regressions for each clinical variable as the dependent variable with each of 4 socioeconomic variables: income quintiles, education in 3 categories, Index of Multiple Deprivation (IMD), and occupational social class. The 2007 English Index of Multiple Deprivation is a measure of deprivation calculated at local area level by combining 38 measures from 7 domains covering economic, social, health, and housing (Department for Communities and Local Government 2008). Income was “equivalized” on the basis of the number of adults and children in the household, weighted in accordance with their age and relationships (McClemens 1977). For each clinical measure, we estimated a regression against each socioeconomic variable. To capture the potential for inequalities to manifest differently in different cohorts in the population, we then undertook 4 sequential age-stratified regressions, starting with income in the model, then adding education, IMD, and finally occupational social class. Age, sex, marital status, region, long-standing illness, and self-assessed health were included as controls in all regressions.

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Socioeconomic Inequalities and Clinical Oral Health Table 1.  Summary Statistics for Independent and Dependent Variables. Independent Variable: Category Sex  Male  Female Age group, y  21-34  35-49  50-64  65+ Marital status  Single  Widowed  Divorced/separated  Married Long-standing limiting illness  Yes  No Self-assessed health  Excellent  Good  Fair  Poor Region   England north   England midlands   England south  Wales   Northern Ireland Education   Has degree level qualification   Has below-degree level qualification   No qualification Social class   Managerial and professional  Intermediate   Routine and manual Equivalized Incomea Full sample  Mean  SD   Interquartile range Dentate only  Mean  SD   Interquartile range

%

n

40.52 22.44 37.04

  2363 2721   1024 1699 1441 920   1128 270 631 3055   1629 3455   1941 2235 704 204   1301 1437 1669 339 338   1440 2922 722   2060 1141 1883

£

 

46.48 53.52 20.14 33.42 28.34 18.10 22.19 5.31 12.41 60.09 32.04 67.96 38.18 43.96 13.85 4.01 25.59 28.27 32.83 10.01 6.67 28.32 57.47 14.20

627.15 1391.65 489.17

               

%

n

608.14 1352.75 485.92

Dependent Variable: Category

Existence of decay or any unsound teeth  Yes 40.66  No 59.34 Existence of any unrestorable teeth 8.44  Yes  No 91.56 Existence of 3 or more upper spaces (n = 4,937)   Yes 5.08  No 94.92 Does not have “excellent” overall oral health  Yes 91.70   No (i.e., has excellent oral health) 8.30 Any pockets 6+ mm (n = 5067)  Yes 9.39  No 90.61

  2067 3017   429 4655 251 4686   4662 422   476 4591

Table 1. (continued) Natural Teeth Without edentate (n = 5084)  Mean  SD   Interquartile range With edentate (n = 5404)  Mean  SD   Interquartile range

    25.05 5.90 6   23.56 8.22 6

a

Quintiles, weekly in GBP: lowest–£210, £211–£341, £342–£507, £508–£795, £796–highest.

In models where the outcome variables were continuous (e.g., number of teeth, DMFT), ordinary least squares regression was used, and the marginal effects are reported, representing the change in the mean number of teeth affected in relation to the reference category. In models where the outcome variables were binary, the effects of covariates were calculated per logistic regression, and mean marginal effects were estimated. This represents the change in the probability of having the disease state or impairment, when moving from the reference category to each respective category for the socioeconomic variable. For example, a marginal effect of 0.1 for caries prevalence in people in the lowest income category represents an increase in the probability of having caries by 10 percentage points compared with the highest income group (the reference category).

Results Summary statistics for the final sample are shown in Table 1. The data in Tables 2 and 3 show significant relationships between clinical outcomes and socioeconomic measures for most combinations, invariably with people of lower income, lower occupational class, higher deprivation, or lower educational attainment demonstrating the poorest clinical outcomes. However, the size and significance of inequalities for individual socioeconomic variables depend on the clinical outcome used. The 2 simple caries variables—presence of caries lesions and ≥1 teeth that are unrestorable due to caries—are still strongly associated with income after adjustment. By contrast, the presence of any teeth with pockets of 6+ mm (severe periodontal disease), having unfilled upper spaces (untreated aesthetic impairment), and not having excellent overall oral health are weakly associated with income but show relationships and sometimes clear gradients with other socioeconomic measures that are constructed in different ways (e.g., IMD). The number of teeth tends to reduce with age; in this sample, the mean was 28.7 for 21 to 34 y but just 19.0 for those aged ≥65 y. However, there are also generational differences reflecting major changes in lifetime experiences over recent decades, so we hypothesized that age group (as

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Table 2.  Marginal Effects (95% Confidence Intervals) for All 4 Socioeconomic Variables in Adjusteda Regression Models with Each of 4 Disease Measures. Socioeconomic Indicators Income  Richest   Second richest  Intermediate   Second poorest  Poorest Education   Degree or above   Some qualification   No qualification Index of Multiple Deprivation   Least deprived   Second least deprived  Intermediate   Second most deprived   Most deprived Occupational social class   Managerial and professional  Intermediate   Routine and manual

Existence of Decay or Unsound Teeth

Existence of Unrestorable Teeth

Decayed, Missing, and Filled Teeth

Reference 0.10*** (0.05, 0.15) 0.11*** (0.06, 0.16) 0.08*** (0.02, 0.14) 0.11*** (0.05, 0.17)

Reference 0.04** (0.01, 0.08) 0.05** (0.01, 0.09) 0.05** (0.01, 0.10) 0.09*** (0.04, 0.14)

Reference 0.64** (0.13, 1.15) –0.05 (–0.61, 0.51) –0.08 (–0.71, 0.54) 0.53 (–0.14, 1.20)

Reference 0.05** (0.01, 0.09) 0.07** (0.00, 0.13)

Reference 0.02* (–0.00, 0.04) 0.04* (–0.00, 0.08)

Reference 0.87*** (0.43, 1.30) –0.27 (–1.01, 0.46)

Reference 0.01 (–0.04, 0.06) 0.03 (–0.01, 0.08) 0.06** (0.01, 0.11) 0.08*** (0.02, 0.14)

Reference 0.00 (–0.02, 0.02) 0.00 (–0.02, 0.03) 0.02 (–0.01, 0.04) 0.05*** (0.02, 0.09)

Reference 0.42 (–0.09, 0.93) 0.02 (–0.48, 0.52) 0.77** (0.17, 1.36) –0.03 (–0.68, 0.63)

Reference 0.00 (0.00, 0.00) 0.02** (0.00, 0.04)

Reference 0.12 (–0.35, 0.60) 0.32 (–0.16, 0.79)

Any Pockets 6+ mm  

Reference –0.01 (–0.05, 0.04) 0.00 (–0.04, 0.05)

Reference 0.01 (–0.01, 0.04) 0.01 (–0.02, 0.04) 0.02 (–0.02, 0.05) 0.03 (–0.01, 0.06)   Reference 0.01 (–0.01, 0.03) 0.04* (–0.00, 0.08)   Reference 0.03* (–0.00, 0.05) 0.03** (0.01, 0.06) 0.03* (–0.00, 0.06) 0.06*** (0.02, 0.10)   Reference 0.00 (–0.02, 0.02) 0.02* (–0.00, 0.04)

Each model controls for all other socioeconomic status variables. a All regressions adjusted for age, sex, marital status, region, long-standing illness, and self-assessed health. *P < 0.05. **P < 0.01. ***P < 0.001.

Table 3.  Marginal Effects with 95% Confidence Intervals for All 4 Socioeconomic Variables in Adjusteda Regression Models with Each of 3 Clinical Measures of Health Outcome. Socioeconomic Indicators Income  Richest   Second richest  Intermediate   Second poorest  Poorest Education   Degree or above   Some qualification   No qualification Index of Multiple Deprivation   Least deprived   Second least deprived  Intermediate   Second most deprived   Most deprived Occupational social class   Managerial and professional  Intermediate   Routine and manual

Existence of 3+ Unfilled Upper Spaces

Excellent Oral Health

Reference 0.01 (–0.00, 0.03) 0.01 (–0.01, 0.02) 0.01 (–0.01, 0.02) 0.02* (–0.00, 0.04)

Reference 0.00 (–0.02, 0.01) 0.01 (–0.01, 0.03) 0.00 (–0.02, 0.02) 0.00 (–0.02, 0.02)

Reference 0.01** (0.00, 0.02) 0.02** (0.00, 0.05)

Reference 0.02*** (0.00, 0.03) 0.02** (0.00, 0.04)

Reference 0.01 (–0.00, 0.02) 0.01** (0.00, 0.02) 0.01** (0.00, 0.03) 0.03*** (0.01, 0.05)

Reference 0.01 (–0.00, 0.02) 0.03*** (0.01, 0.04) 0.03*** (0.02, 0.04) 0.04*** (0.02, 0.05)

Reference 0.01* (–0.00, 0.02) 0.01*** (0.00, 0.02)

Reference 0.00 (–0.02, 0.01) 0.01* (–0.00, 0.03)

No. of Natural Teeth (with Edentate)   Reference –0.02 (–0.39, 0.36) –0.09 (–0.53, 0.35) –1.36*** (–1.91, –0.81) –0.69** (–1.22, –0.15)   Reference –0.60*** (–0.93, –0.28) –2.79*** (–3.48, –2.10)   Reference –0.32 (–0.77, 0.12) –0.61*** (–1.03, –0.18) –1.35*** (–1.86, –0.84) –1.13*** (–1.68, –0.58)   Reference 0.20 (–0.21, 0.62) –1.16*** (–1.59, –0.74)

Each individual model controls for all other socioeconomic status variables. a All regressions adjusted for age, sex, marital status, region, long-standing illness, and self-assessed health. *P < 0.05. **P < 0.01. ***P < 0.001. Downloaded from jdr.sagepub.com at Uni of Southern Queensland on October 26, 2014 For personal use only. No other uses without permission. © International & American Associations for Dental Research 2014

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Socioeconomic Inequalities and Clinical Oral Health Table 4.  Marginal Effects (95% Confidence Intervals) of Income Quintiles Compared with Richest for 4 Clinical Disease Variables, Adjusteda and Stratified by Age after Controlling for the Effects of Education, Index of Multiple Deprivation, and Occupational Social Class. Income Quintiles (Richest as Reference) All Existence of decay or unsound teeth (n = 5,084)   Second richest 0.10*** (0.05, 0.15)  Intermediate 0.11*** (0.06, 0.16)   Second poorest 0.08*** (0.02, 0.14)  Poorest 0.11*** (0.05, 0.17) Existence of unrestorable teeth (n = 5,084) 0.04** (0.01, 0.08)   Second richest  Intermediate 0.05** (0.01, 0.09)   Second poorest 0.05** (0.01, 0.10)  Poorest 0.09*** (0.04, 0.14) Decayed missing and filled teeth 0.64** (0.13, 1.15)   Second richest  Intermediate –0.05 (–0.61, 0.51)   Second poorest –0.08 (–0.71, 0.54) 0.53 (–0.14, 1.20)  Poorest Any pockets 6+ mm (n = 5,067)   Second richest 0.01 (–0.01, 0.04)  Intermediate 0.01 (–0.02, 0.04)   Second poorest 0.02 (–0.02, 0.05)  Poorest 0.03 (–0.01, 0.06)

21-34

35-49

50-64

0.16*** (0.05, 0.26) 0.18*** (0.07, 0.30) 0.1 (–0.04, 0.23) 0.17** (0.04, 0.31)

0.09** (0.01, 0.18) 0.10** (0.01, 0.20) 0.10* (–0.01, 0.21) 0.11** (0.01, 0.20)

0.05 (–0.03, 0.14) 0.06 (–0.03, 0.16) 0.09 (–0.02, 0.20) 0.05 (–0.05, 0.16)

0.05 (–0.02, 0.13) 0.05 (–0.03, 0.12) 0.02 (–0.05, 0.08) 0.11** (0.00, 0.21)

0.04 (–0.01, 0.10) 0.08** (0.00, 0.16) 0.07* (–0.01, 0.14) 0.10** (0.02, 0.18)

0.03 (–0.03, 0.09) 0.04 (–0.02, 0.10) 0.08* (–0.00, 0.16) 0.05 (–0.02, 0.12)

0.89* (–0.04, 1.82) 1.72*** (0.65, 2.78) 1.42** (0.15, 2.69) 1.42** (0.20, 2.65)

0.82* (–0.05, 1.69) –0.33 (–1.34, 0.68) –0.44 (–1.69, 0.81) 0.21 (–1.02, 1.44)

0.18 (–0.73, 1.09) –0.58 (–1.64, 0.47) 0.04 (–1.13, 1.20) 0.08 (–1.05, 1.21)

0.02 (–0.03, 0.07) 0.01 (–0.02, 0.05) 0.04 (–0.02, 0.11) 0.05 (–0.01, 0.10)

–0.00 (–0.03, 0.03) –0.02 (–0.05, 0.01) 0.00 (–0.03, 0.04) 0.01 (–0.03, 0.04)

0.00 (–0.06, 0.07) 0.05 (–0.03, 0.13) 0.06 (–0.04, 0.15) 0.05 (–0.04, 0.13)

65+ 0.02 (–0.13, 0.17) 0.07 (–0.08, 0.21) 0.03 (–0.11, 0.18) 0.04 (–0.13, 0.21)   –0.02 (–0.11, 0.07) –0.02 (–0.10, 0.07) –0.03 (–0.12, 0.06) 0.02 (–0.10, 0.13)   0.15 (–1.38, 1.68) –0.62 (–2.15, 0.91) –0.14 (–1.68, 1.39) 0.61 (–1.31, 2.52)   0.08 (–0.07, 0.24) 0.11 (–0.03, 0.25) 0.08 (–0.05, 0.20) 0.06 (–0.10, 0.22)

a All regressions adjusted for age, sex, marital status, region, long-standing illness, and self-assessed health. *P < 0.05. **P < 0.01. ***P < 0.001.

well as age) would affect the way that we observe inequalities, and the data support this. Consequently, Tables 4 and 5 stratify by age group after adjusting for the other 3 socioeconomic variables. After adjustment, significant income effects remained for the caries-related variables, for number of teeth, and for the presence of unfilled spaces in the oldest but not for periodontal disease and excellent oral health. The income effects are not always ordered in a gradient. In the youngest 2 groups, there is a significant step change in marginal probability for having caries (of about 0.1 in the youngest) between the top income quintile and the rest after adjustment but with no gradient. Being in the highest income group appears to be protective for caries, when compared with all the other categories, up until the age of 50 y but not beyond. By contrast, number of teeth shows little or no inequality in the young, but for the oldest adults, those in the poorest quintile have lost many more teeth than those in the wealthiest, with a clear gradient. Smoking is understood to be a direct causative factor for periodontal disease (Warnakulasuriya et al. 2010) but will also correlate with the socioeconomic variables. In view of this specific causative relationship, for the periodontal variable only, we repeated the analysis after first adjusting for the respondents’ current smoking status. Income differences were eliminated by adjusting for smoking, education, and IMD (see Appendix Table 4).

Discussion It is clear from these data that no single measure of oral health is adequate to interpret oral health inequalities. Different clinical measures behave very differently against the same socioeconomic variable, and the reverse is also true. Age group, reflecting both age and cohort, is also a critical factor. Analyses of population data will never give a perfect explanation of inequalities. The explanatory variables are grouped, and there is always unexplained variance. The correlation of the socioeconomic variables needs careful management, and missing data introduce an additional risk of bias. The data reported here are also cross sectional, so we can only imply the different effects of age and cohort. Crosssectional associations never provide strong evidence for causation, and the possibility that our results are partly driven by unobserved confounders cannot be ruled out. There have been informative longitudinal investigations of oral health inequalities (Thomson et al. 2004; Siukosaari et al. 2005; Holst and Schuller 2011), suggesting a dynamic relationship over time, but these cannot give the perspective of a whole adult population. Generally though, the quality of the clinical data was high. The clinicians were subject to intensive training and calibration (O’Sullivan et al. 2011), and while there were data missing for income, analysis with

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Table 5.  Marginal Effects (95% Confidence Intervals) of Income Quintiles Compared with Richest for 3 Clinical Variables Indicating Oral Health Outcomes, Adjusteda and Stratified by Age after Controlling for the Effects of Education, Index of Multiple Deprivation, and Occupational Social Class. Income Quintiles (Richest as Reference) All

21-34

35-49

Existence of unfilled upper spaces (n = 4,937)   Second richest 0.01 (–0.00, 0.03) NA  Intermediate 0.01 (–0.01, 0.02) NA   Second poorest 0.01 (–0.01, 0.02) NA  Poorest 0.02* (–0.00, 0.04) NA Excellent oral health (n = 5,084)   Second richest 0.00 (–0.02, 0.01) 0.00 (–0.07, 0.07)  Intermediate 0.01 (–0.01, 0.03) 0.04 (–0.03, 0.11)   Second poorest 0.00 (–0.02, 0.02) 0.04 (–0.04, 0.12)  Poorest 0.00 (–0.02, 0.02) 0.06 (–0.02, 0.13) No. of natural teeth (n = 5,404) with edentate   Second richest –0.02 (–0.39, 0.36) 0.05 (–0.37, 0.48)  Intermediate –0.09 (–0.53, 0.35) –0.21 (–0.72, 0.31)   Second poorest –1.36*** (–1.91, –0.81) –0.60* (–1.23, 0.03) –0.69** (–1.22, –0.15) –0.2 (–0.78, 0.39)  Poorest

50-64

0.00 (–0.01, 0.02) 0.00 (–0.01, 0.02) 0.01 (–0.02, 0.04) 0.02 (–0.02, 0.05)

–0.00 (–0.03, 0.03) –0.01 (–0.03, 0.02) –0.01 (–0.04, 0.02) 0.01 (–0.03, 0.04)

–0.01 (–0.05, 0.03) 0.00 (–0.04, 0.05) –0.02 (–0.08, 0.04) –0.03 (–0.09, 0.02)

–0.00 (–0.02, 0.01) 0.00 (–0.01, 0.02) 0.00 (–0.01, 0.01) –0.00 (–0.01, 0.01)

–0.44** (–0.87, –0.02) 0.59 (–0.29, 1.46) –0.17 (–0.67, 0.34) 0.84 (–0.17, 1.85) –0.57 (–1.36, 0.23) –0.08 (–1.29, 1.14) –1.05*** (–1.73, –0.37) –0.2 (–1.39, 0.99)

65+ 0.2 (–0.05, 0.45) 0.16 (–0.04, 0.36) 0.13 (–0.04, 0.31) 0.25* (–0.04, 0.54)   NA NA NA NA   –2.43** (–4.34, –0.52) –1.91** (–3.72, –0.10) –3.70*** (–5.59, –1.81) –4.51*** (–6.82, –2.21)

NA indicates where the proportion affected by the condition was too small to estimate marginal effects. All regressions adjusted for age, sex, marital status, region, longstanding illness and self-assessed health. *P < 0.05. **P < 0.01. ***P < 0.001. a

and without imputation showed little difference, the survey weights were used, and we can be confident that the data are representative of the population investigated. The correlation of the 4 socioeconomic variables was managed by testing each independently while controlling for others or, for age-stratified analysis, starting with income and then adding the other variables to establish any residual income effect (data for each stage are reported in the appendices). Intermediate behavioral effects (e.g., dental behaviors) were not included in the models, as the aim was to understand the relationships between socioeconomic measures and oral health outcomes and not unpick the intermediate steps. The relationship of the number of teeth with age and cohort is critical to understanding how inequalities operate. There is no income-related relationship in the young, because this population of young people, rich or poor, now have dentitions that are more or less complete. In the oldest group, a huge difference between richest and poorest (based on current income) has opened up, and the unadjusted marginal difference was nearly 8 teeth. The youngest and oldest groups are up to 2 generations apart, having very different lifetime exposures, but for the current generation of older people, the effects of a lifetime of inequality are laid bare. Subsequent generations require other measures to understand inequalities. For caries, the marginal effect was significant up to age 50 y but disappeared in older groups for whom tooth loss (the end stage of disease) was much more sensitive. Longitudinal data from Finland have similarly observed caries-based (DMFT) inequalities diminishing with age (Siukosaari 2005). In the younger groups, there was no evidence of a gradient per se but rather a step change

such that being wealthy is protective compared with the rest, as opposed to being poor representing a particular additional risk. By contrast, being in the poorest quintile seemed to be important when using the presence of teeth “unrestorable” due to caries among younger people as an outcome, a finding consistent with low service utilization directly related to income. DMFT has been widely used as a measure of oral health, including investigations of inequalities (Siukosaari et al. 2005; Geyer et al. 2010; Holst and Schuller 2011), and it is included here for completeness. Theoretically, it captures caries experience, but by adulthood, it becomes impossible to distinguish damage caused by caries from damage and tooth loss related to treatment, periodontal disease, and a range of other factors. The relationship with socioeconomic measures was unclear, reflecting the multiple components, although in the youngest age group, a generation with low disease experience, it demonstrates an income gradient after adjustment. Caution is urged when investigating inequality in adults, as DMFT may not be particularly sensitive. The measure of periodontal disease (any pockets 6+ mm) demonstrated significant inequalities with income before adjustment but not after. Given the specific and direct causative link between smoking and periodontitis, we reran the models with smoking entered. With just education added, the marginal effects for income almost disappear (Appendix Table 4). With IMD included, income and social class make no significant contribution. Sabbah et al. (2007) demonstrated income gradients in periodontal disease but without simultaneous adjustment. Morita et al. (2007) simultaneously adjusted in a similar way to our analysis and

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Socioeconomic Inequalities and Clinical Oral Health saw no income effects. Income inequalities in periodontal disease appear largely to be mediated by education (perhaps a proxy for health behaviors) and smoking. We tested several other oral health variables representing very different possible manifestations of inequalities but which were not simple disease measures. Unfilled upper spaces toward the front of the mouth represent an impairment that is visible. Acceptance of a visible space might be influenced by myriad factors, including the ability to afford a replacement (income), access to services, and social context, including age. Cases were infrequent (5% for the sample), but we found a large and significant income effect after adjustment but just in the oldest poor (0.25 for adults aged 65+ y is a huge marginal effect). Across the whole sample, IMD showed the most significant relationship (Table 3), and although the marginal effect on probability looks small (0.03), so is the starting probability, representing a substantial increase in risk. These findings are consistent with a hypothesis of income being relevant but also social context being critical. The excellent oral health variable looks at oral condition from a salutogenic perspective and splits the population into those with very healthy teeth and periodontal tissues (a minority, concentrated among the youngest) and the rest. In the youngest, there is a very clean gradient according to income when unadjusted, which becomes nonsignificant with adjustment, consistent again with contributions from education and social environment. In populations with good oral health, where disease prevalence and experience are low, the loss of excellent health rather than the presence of advanced disease may be a sensitive early life clinical marker of developing inequality. Large, significant, and important social inequalities in oral health and disease were apparent whatever measure we investigated, but the selection is critical. Tooth loss is a good way to see inequality that has emerged toward the end of life but is not useful early in life. The contribution of income was very different for caries than for periodontal disease, presumably reflecting the different ways in which risks may be socially determined. Income, education, social position, and the sort of area you live in might each have a unique impact on oral health. There is evidence that all these may generate oral health inequalities, but the evidence to date has been contradictory (Lopez et al. 2006; Turrell et al. 2007; Celeste et al. 2009; Borrell and Baquero 2011; Shen et al. 2013). Using different socioeconomic measures allows these complexities to be explored (Galobardes et al. 2006). Income may affect the ability to afford services at any given point in life and therefore affect clinical decisions with lifetime consequences on oral health outcome. Education may affect the way that you choose to interact with services and use information, while occupational class and IMD may reflect the values of your peers and perhaps the environment in which you live and work. These measures will correlate but also represent independent mechanisms by which inequalities may develop.

Income may go up and down through life, but the presence of income as a predictor suggests that even historical effects may be detectable; however, the evidence for multiple and complex contributory factors in this study lies in the alteration of income effects, as the 3 other socioeconomic measures are dropped into the models. Our data suggest that the way that inequalities develop is not simple, nor is there a single “best” oral health measure; the approach may depend on the population in which you are interested and the question that you are asking. There is overlap among social measures, but when examined in detail and in the context of age group, the combinations were informative and clearly suggested a breadth of likely mechanisms leading to inequality. It is not simply a matter of being at higher risk of disease when you are poor, because the income effects on caries risk identify the wealthy as protected, while for periodontal disease, education, IMD, and smoking account for the income effects. Nevertheless, in some contexts, being poor is related to outcome (severe untreated caries and having a visible space), with both of these representing conditions where an income effect may be current or recent rather than historical. Nor is it simply that education is the issue or the context of the area in which you live, although there is evidence from these data (anterior spaces and excellent oral health) that these are important. There are many possible paths between socioeconomic position and oral health inequality that require further unpicking. However, while increasing resources for treatment services may provide benefits, the analysis here suggests that it will not resolve inequalities. Upstream action addressing risks, beliefs, behaviors, and the living environment are probably as important as affordable access to professional treatment. Author Contributions J. Steele, contributed to conception, design, data acquisition, analysis, and interpretation, drafted the manuscript; J. Shen, contributed to conception, design, data analysis and interpretation, drafted the manuscript; G. Tsakos, contributed to conception, design, data acquisition, analysis, and interpretation, critically reviewed the manuscript; R. Watt, contributed to conception, design, data acquisition, analysis, and interpretation, critically reviewed the manuscript; C. Guarnizo-Herreño, contributed to design and data interpretation, critically reviewed the manuscript; S. Morris, contributed to design and data interpretation, critically reviewed the manuscript; E. Fulller, contributed to design and data analysis, critically reviewed the manuscript; J. Wildman, contributed to conception, design, data analysis, and interpretation, critically reviewed the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

Acknowledgments This work was supported by the UK Economic and Social Research Council (grant ES/K004689/1) as part of the Secondary Data Analysis

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Journal of Dental Research

Initiative. The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.

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