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 Table of Contents  
Year : 2019  |  Volume : 11  |  Issue : 4  |  Page : 197-203

Effects of area-based deprivation on dental caries, perceived dental treatment need and oral health related quality of life of UK adults

Centre of Population Oral Health and Clinical Prevention Studies, Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh, Malaysia

Date of Web Publication14-Aug-2019

Correspondence Address:
Dr. Nawwal A Mohd Radzi
Centre of Population Oral Health and Clinical Prevention Studies, Faculty of Dentistry, University Teknologi MARA, Sungai Buloh Campus, 47000.
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jioh.jioh_47_19

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Aims and Objectives: To measure the influence of Index of Multiple Deprivation England (IMDE) on dental caries prevalence, perceived dental need, and oral health related quality of life (OHRQoL). Materials and Methods: Secondary analysis of the 2009 Adult Dental Health Survey (ADHS) was carried out to report the influence of IMDE on the number of carious teeth, perceived need toward dental treatment OHRQoL of UK adults using Oral Health Impact Profile (OHIP-14). Results: As the range of IMDE from the least to the most deprived area is 10 to 1, IMDE score was negatively correlated with the number of carious teeth (r = −0.147, P = 0.01). Similarly, IMDE score was negatively correlated with total OHIP-14 score (higher score indicating higher impact on OHRQoL) with r = −0.135 (P = 0.01). Logistic regression showed statistically significant results of IMDE score in increasing the likelihood of respondent in having higher number of caries (odds ratio [OR] = 0.93, P = 0.000), perception of needing dental treatment (OR = 1.111, P = 0.000), and higher OHIP-14 score (OR = 0.94, P = 0.000). Conclusion: Measures of relative deprivation are important in research related to oral health inequality. Area-based deprivation index proved to be one of the tools that can visibly disclose the inequalities of burden of disease in oral health. Policymakers should consider concentrating resources toward those with low household income in highly deprived areas rather than those with similar income but in less-deprived area.

Keywords: Area-Based Deprivation, Oral Health Inequality, Oral Health Related Quality of Life

How to cite this article:
Mohd Radzi NA. Effects of area-based deprivation on dental caries, perceived dental treatment need and oral health related quality of life of UK adults. J Int Oral Health 2019;11:197-203

How to cite this URL:
Mohd Radzi NA. Effects of area-based deprivation on dental caries, perceived dental treatment need and oral health related quality of life of UK adults. J Int Oral Health [serial online] 2019 [cited 2022 Aug 17];11:197-203. Available from:

  Introduction Top

In any population, an individual’s health varies for variety of reasons. The differences are considered acceptable if it is a consequence of age or gender. Unequal pattern of health caused by social, economic, or political factors that affect certain member of a society in terms of their opportunity and access to appropriate resources, conclusively describe health inequality.[1] Such inequality is avoidable, unnecessary, unjust, and unfair.[2]

Oral health inequality linked better health with more affluent socioeconomic attributes, which persist even in developed countries.[3] Researches linking oral health inequalities and socioeconomic attributes typically use single indicator measures such as occupation, education, or household income. These measures, as reviewed by Locker[4] in 2000, despite can predict general and oral health outcome of the population, are limited in providing full picture of health inequalities of a population.[5] Historically, occupational classification segregates population and shows variants in morbidity and mortality rates.[6] The limitation of single indicator measure in epidemiological research led to the growth of studies using alternative measures of socioeconomic status such as area-based deprivation measure. Deprivation is defined by Townsend[7] as a state of observable and demonstrable disadvantage relative to a community (local or nationwide) to which an individual, family, or group belongs. It is applied to conditions rather than resources that distinguished it from the concept of poverty.[7]

Area-based classification that combined socioeconomic characteristics and environmental conditions proved useful in identifying particularly groups that have disadvantage in terms of health.[6] At present, studies that linked oral health and measures of deprivation are relatively at an early stage, usually involving children and dental caries as the outcome of interest.[4]

This article aimed to investigate the effects of deprivation using Index of Multiple Deprivation England (IMDE) on caries prevalence, self-reported perceived treatment needs, and oral health related quality of life (OHRQoL). This is a secondary analysis of the national representative data from the 2009 Adult Dental Health Survey (ADHS) exploring the association between area-based deprivation and oral health.

  Materials and Methods Top

The survey involved 13,400 households across England, Wales, and Northern Ireland, using two-stage cluster sampling technique of 268 primary sampling units (PSUs) across the UK. The research used dataset obtained from 2009 ADHS, without direct contact with participants or any members of the survey. The legal consent had been obtained from local authorities.

Each PSU contained two postcode sectors with 25 addresses sampled from each sector. The IMDE 2010 was matched to each respondent’s data. A total of 6469 dental examinations were completed on the consented participants (n = 7973). The finalized number of participants extracted for the data analysis was 5553 and simplified in the study profile [Figure 1]. Inclusion criteria are those of 16 years and above who completed both questionnaire and clinical interview. Those who were fully edentulous were excluded from the survey. Questionnaire-based interviews and clinical examinations were employed to measure the dental health of the participants.
Figure 1: Study profile

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The following variables were extracted from the ADHS (2009):

  • Age: Seven categories (16–24, 25–34, 35–44, 45–54, 55–64, 65–74, and 75 years and over). The 65–74 and 75 years and over groups were categorized under 55 years and over group for analysis purpose

  • Gender

  • Deprivation status: IMDE score (IMDE 2010) was assigned to participant’s postcode. It is an overall measure of multiple deprivations calculated for every lower super output area (LSOA) in England. LSOAs were homogenous small areas of relatively even sizes (around 1500 people) of which there were 32,482 in England. IMDE was used to rank the 32,482 LSOAs from the most to the least deprived. This variable was split into 10 groups of equal sizes, which created a decile representative of one-tenth of the whole.[8] Decile 1 indicates most deprived and decile 10 means most affluent

  • IMD measure wide concept of multiple deprivation consisted of seven domains: Income deprivation, employment deprivation, health deprivation and disability, education skills and training deprivation, and crime. Through the use of appropriate weighting measure, they were combined into a single overall IMDE

  • Education attainment: Whether or not participants hold any professional, vocational, or work-related qualifications in the questionnaire

  • Dental perceived need (Yes or no): Inquired by asking “Would you need any treatment if you visit the dentist tomorrow?”

  • The number of decayed teeth measure quantified the number of dental caries obtained from the clinical examination in the survey. It included teeth with restoration, recurrent caries, and restorations that were lost or broken.[9] The number of decayed teeth in each individual in this survey ranged from 0 to 21 teeth. In the regression analysis section of this variable, those who did not have any caries were coded as 1, whereas those who have one or more number of dental caries were coded as 2.

    Oral Health Impact Profile (OHIP-14) was used to assess the oral health-related quality of life. Respondents were asked to rate for the last 12 months on each item ranked in a Likert-type scale from 1 (never) to 5 (very often). The score ranged from 0 to 70 with high OHIP-14 score signifying poor OHRQoL. In the regression analysis section of this variable, due to the skewness of the data, the total OHIP score was recorded into a new category. The new category split the samples into two categories: those with OHIP score below the median (median = 1) were coded as 1 and those who were in the median group and above were coded as 2.

    Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) software, version 20 (IBM, Armonk, New York). Descriptive statistic and bivariate analysis using chi-squared tests investigate the association between dental perceived need and each demographic feature. Correlation analysis was carried out using Spearman’s correlation test for categorical variables and Pearson’s correlation test for continuous variable. Correlation was calculated between total OHIP-14 score, number of dental caries, perceived dental treatment need, and IMDE score. Three logistic regression models were performed to examine whether IMDE predicted (1) presence of dental caries, (2) perceived dental treatment need, and (3) total OHIP-14.

      Results Top

    Data of 5553 participants were analyzed. [Table 1] shows the distribution of participants’ sociodemographic features alongside their perceived dental treatment needs.
    Table 1: Sociodemographic features of participants by perceived dental treatment need

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    The total number of decayed or unsound teeth of the participants ranged from 0 to 21 and the mean number of decayed teeth was 0.96 (standard deviation [SD] = 1.88, range = 0–21). A total of 60.8% of the sample did not have any caries. With regard to the participants’ perception toward treatment need, 46.6% reported requiring some form of dental treatment whereas 59.4% did not perceive the need for dental treatment. As for total OHIP-14 score, the value ranged from 0 to 56 and 45% of the sample scored 0. The mean number of the total OHIP-14 score was 3.74 (SD = 6.46, range = 0–56). When asked about their self-reported oral health, 24.3% reported having very good oral health, whereas the majority, 46.7%, rated their oral health as “good,” 6% reported as having “bad” oral health, and less, that is, one in fifty (1.3%) admitted having “very bad” oral health.

    [Table 2] shows the relationships between IMDE score, number of dental caries, perceived dental treatment need, and total OHIP-14 scores. The correlation between the number of decayed teeth and IMDE score was −0.147 and IMDE score with total OHIP-14 scores was −0.135 (both correlations were significant at the 0.01 level [two tailed]). As IMDE ranked those who lived at the least deprived area with higher score (i.e., IMDE 10) and as the rank reduced in number toward the most deprived area (IMDE 1), both number of decayed or unsound teeth and total OHIP-14 scores increased. Similarly, total OHIP-14 score is negatively correlated with perceived treatment need. All subsections of OHIP-14 were negatively correlated with IMDE. As the rank reduced, the OHIP-14 score in each subsection increased, indicative of poorer OHRQoL of the population.
    Table 2: Correlation of Index of Multiple Deprivation England with number of dental caries, perceived dental treatment need, and total Oral Health Impact Profile (OHIP-14) score

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    [Table 3] shows logistic regression model that assessed the impact of similar IMDE score on the likelihood of increase in the number of decayed or unsound teeth of the respondents. The full model containing both predictors was statistically significant, χ2 = (2, n = 5553) = 56.01, P < 0.001. IMDE ranks were recorded to be a statistically significant contributor to the model with an odds ratio of 0.93. Education attainment however was not statistically significant in predicting the number of decayed or unsound teeth.
    Table 3: Logistic regression predicting the likelihood of increased number of dental caries

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    The second model assessed the impact of area-based deprivation measure (IMDE) and education attainment level with the likelihood of having a perception of either requiring dental treatment or not from the respondents. The model was statistically significant, χ2 = (2, n = 5553) =114.3, P < 0.001. This model posed similar statistically significant findings from the independent variable IMDE score, having a predictive value of respondents’ perceived need with an odds ratio of 1.11 (perceived treatment need: Yes, 1; No, 2). This indicated that as the IMDE rank of the British adults increased, they do 1.1 times likely not need any dental treatment [Table 4].
    Table 4: Logistic regression predicting likelihood of having a perception of needing dental treatment

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    Logistic regression was performed to assess the relationship between socioeconomic statuses on the likelihood that the respondent would have higher total OHIP-14 score, indicative of a greater impact on their OHRQoL. The model contained two independent variables, namely IMDE rank and level of education. The full model containing both predictors was statistically significant, χ2 = (2, n = 5553) = 31.06, P < 0.001, indicating that the model was able to distinguish those who have the OHIP-14 score above or below the value of 1. The model, as a whole, explained 0.6% (Cox and Snell R-squared) and 0.7% (Nagelkerke R-squared) of the variance in total OHIP-14 score and correctly classified 55% cases. Only IMDE rank made a statistically significant contribution to the model. IMDE recorded an odds ratio of 0.95, indicating that increasing IMDE score will reduce the total OHIP-14 score by 0.05 [Table 5].
    Table 5: Logistic regression predicting likelihood of increasing total Oral Health Impact Profile (OHIP-14) score

    Click here to view

      Discussion Top

    This study showed that social inequalities were present with respect to number of caries, perceived dental needs, and OHRQoL when area-based measures were used. This study also intended to assess whether there is any variance in the relationship of IMDE as a measure of area-based deprivation and the OHRQoL. The correlations of the seven subsections of scoring high in the OHIP-14 questionnaire were all significantly inversed with the IMDE score. Similar findings were also recorded with the participant’s perceived treatment need. These results showed that higher score in each of the OHIP-14 subsection is related to the need for dental treatment. Total OHIP-14 score was also significantly and inversely correlated with IMDE. However, subsections of functional limitation, physical disability, and psychological disability were significantly correlated with having no professional, vocational, or work-related qualification. Nuttal et al.[10] reported that in the 1998 ADHS, the most frequently reported problems were pain and psychological discomfort whereas handicap was the least frequently experienced problem. The finding of this study is similar to the finding by Masood et al.,[11] that is, having a better area-based measure of deprivation (IMDE) significantly associated with people having higher impact of oral health affecting their quality of life.

    In the three regression models, while predicting whether IMDE score has any influence on the number of decayed teeth, perceived need to treatment, and total OHIP-14 score, the IMDE score was found to be statistically significant in all three models. Although no comparison could be made from the previous survey with regard to IMDE score, Treasure et al.[12] did find from the 1998 ADHS that having no education qualifications had a significant odds ratio of 1.35 of having decayed and unsound teeth. No direct comparison between variables used in this study could be made because the variable IMDE score was only introduced as an area-based deprivation index to the most recent ADHS. Bower et al.[13] explored the association between area-based deprivation and adult oral health using multilevel regression analysis of 1998 ADHS on 346 households in Scotland, the findings showed that the population in the most deprived area had an average of 4.6 less sound teeth than their least deprived counterpart. Treasure et al.[12] conducted multivariate analysis from the 1998 ADHS sample and revealed that adults originated from the north of Great Britain, who were categorized in the study to be most deprived, had an effect on the odds of having increased tooth loss as the distance from the south of England increased. Both studies provided support that the population in the least affluent area was experiencing the worst oral health.

    Because of the nature of the ADHS as a cross-sectional study, it could not test any causal relationship as it was not longitudinal in nature. Thus, it is worth considering whether conducting similar survey in the future will reduce the burden of oral health inequality or more cost-effective ways of tackling the problem should be taken. Unique to 2009 ADHS, Scotland decided to opt out, hence the results were not representative of all four-home nations, thus any nation-wide comparison is impossible.[14] Matters concerned with data analysis, among others, were the missing values of individual participants. A total of 1096 cases were excluded through listwise method in SPSS that could limit the sample size and unnecessarily affect the results.[15] Other shortfall of this study was the reduction of statistical power during the stage of analyzing the data. This is due to the dichotomization of the variables as a result of the huge proportion of subjects with no decayed or unsound teeth and those who scored 0 in the OHIP-14 questionnaire.[16] The resultant dichotomization of variables was a potential source of bias through the loss of information and statistical power.[13] Although bivariate correlation models provided ways to quantify variables in a mathematical relationship, it might be oversimplified and could not be taken at face value because other clinical variables were not included in the correlation model.[15] Correlation analysis was suitable as a start-off point in conducting a research as the relationship could be indirect and require further analyses. For example, it was found that losing anterior teeth has a higher impact on OHRQoL compared with posterior teeth.[17] White et al.[14] showed that dentate participants with 21 or more teeth had considerably better quality of life compared with those having fewer natural teeth or wearing denture. Baker[18] suggested that a more powerful statistical method of structural equation modeling was the best technique for assessing the range of social, behavioral, and attitudinal factors and their interrelationships with oral health outcome. This technique would fill the gap of the majority of the studies that explore individual contextual factors and their relationship with oral health.

    It was clear in this study that oral health inequalities persisted despite the National Health Service was established in 1948. These unequal distributions of health were starkly visible with the use of area-based measure of deprivation such as IMDE. Locker and Ford[19] proved that the socioeconomic status of one’s area influenced their health and health-related behavior, independent of their household economic status. This implicates that future research could benefit from using an accurate measure of area-based deprivation to address the research questions related to oral health inequalities. Study by Landes and Jardin[20] indicated that the more deprived the area, the higher the proportion of that population accessing care in dental clinics in Northeast England, which visualized the appropriate way in tackling inequality of oral health.

    Marmot and Bell[21] suggested three recommendations to tackle health inequality, which include improving daily living conditions, tackling unequal distribution of resources and measures in the present situation while assessing the results of the actions implemented. They also recommended appropriated magnitude of effort to be directed suitably with the need of each area.[21] Marshman et al.[22] identified that perceived need and perceived difficulty accessing dental services were key predictors of oral health outcomes that widened the inequality gap. Crocombe et al.[23] later recognized that the positive effects of dental attendance on patient’s OHRQoL were influenced by a more affluent residential area of the population. This finding indicates that it is vital to increase accessibility and availability of dental service to the area with the worst health experiences. As proposed by Penchansky and Thomas,[24] “availability” meant that the volume and type of services meet the number of population. This implies that oral health promotion team needs to consider using skill-mix in a dental team to increase mobility of dental care toward the needy population. Coupled with the knowledge of oral health prevention, the dental care professionals could tackle multiple factors of common diseases using common risk factor approach.[25],[26],[27] Variety of health promoting measures are available, which could be applied based on the principles of health promotion advocated in Ottawa Charter to be applied in clinical practice.[28],[29] These include reorienting the conventional curative methods toward preventing future diseases.[30],[31],[32]

    This study found pervasive social inequalities in UK adults. Discrete area-based deprivation measures of socioeconomic status were found capable of showing these inequalities. Health inequality remedies require involvement from the government through transformation of policies. It is hoped that future research on area-based deprivation measure will assist in focusing the attention to marginalized population where they experienced restricted lifestyle choices that is reflected in their oral health condition.

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    Conflicts of interest

    There are no conflicts of interest.

      References Top

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      [Figure 1]

      [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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