Abstract
Objectives: Although inequalities in dental implant use based on educational level have been reported, no study has used income as a proxy for the socioeconomic status. We examined: (i) income inequalities in implant use; and (ii) whether income or education has a stronger association with implant use in elder Japanese. Methods: In 2016, a self-reported questionnaire was mailed to participants aged 65 years or older living across Japan as part of the ongoing Japan Gerontological Evaluation Study. We used data from 84,718 respondents having 19 or fewer teeth. After multiple imputation, multi-level logistic regression estimated the association of dental implant use with equivalised income level and years of formal education. Confounders were age, sex, and density of dental clinics in the residential area. Results: 3.1% of respondents had dental implants. Percentages of dental implant use among the lowest (≤ 9 years) and highest (≥ 13 years) educational groups were 1.8 and 5.1, respectively, and among the lowest (0 < 12.2 ‘1,000 USD/year’) and highest (≥ 59.4 ‘1,000 USD/year’) income groups were 1.7 and 10.4, respectively. A fully adjusted model revealed that both income and education were independently associated with dental implant use. Odds ratios for implant use in the highest education and income groups were 2.13 [95% CI = 1.94–2.35] and 4.85 [95% CI = 3.78–6.22] compared with the lowest education and income groups, respectively. From a model with standardised variables, income showed slightly stronger association than education. Conclusion: This study reveals a public health problem that even those with the highest education but low income might have limited accessibility to dental implant services.
Key words: Dental implant(s), access to care, dental services research, dental public health, epidemiology
INTRODUCTION
Although a rise in the demand and use of dental implant treatment services has been reported in Sweden and the USA1., 2., the higher treatment cost associated with these services is likely to cause strong income inequalities. The treatment costs of dental implants are higher than those of conventional therapy such as endodontic treatment and complete and partial denture treatments3., 4.. The average cost of a single tooth implant in Japan ranges from ‘3,000 to 6,000 USD’, with annual maintenance fees ranging from ‘30 to 100 USD’, depending on where treatment takes place5. Social inequalities in dental implant use have been previously reported in a few studies from the USA2., 6., 7.. However, these studies could not measure income inequality in dental implant use. Two studies used geographical location (zip code) as a proxy for socioeconomic status (SES)6., 7., and one study used educational attainment as an indicator of SES2. Because income is considered to be directly related to the payment for implant treatment, inequalities in income should be directly measured.
Educational attainment has also been used as an index of SES. Education affects health through not only employment position and income, but also knowledge, health-promoting decisions, literacy and obtaining a well-educated social network8. Because dental implants have several merits related to function, quality of life and patient satisfaction compared with conventional treatments9., 10., highly educated patients may prefer dental implants. However, it is still possible that even patients with high education but with low income levels could be restricted from accessing dental implant treatment. Studies examining the effects of both individual education and income variables on dental implant use are required.
Japan is the country with the lowest out-of-pocket dental expenditures because the public insurance system covers a wide range of dental services11, and it is also ranked as the country with the highest access to dental care, with 3.2 dental visits per person per year in 201512. Additionally, Japan has adopted a universal health care insurance system for the general population since 196113, through which beneficiaries can access medical and dental treatment when needed and only pay 10%–30% of the total costs of treatment depending on income, age and health condition14. Nevertheless, dental implant therapy is not covered by insurance except in rare cases of absolute necessity, such as those involving congenital, pathological and accidental jaw bone deficiencies15. Conventional care is less expensive and is covered by universal health care insurance, while implant care is not. Hence, implant care may be selected only by affluent people who can afford to pay for it personally, even if implants are the best treatment option. In Japan, any licensed dentist is allowed to provide dental implant treatment, and it is the dentist’s decision whether to refer patients to a dental implant specialist14.
The aim of this study was to examine the association between dental implant use and two predictors of SES (equivalised income level and years of formal education) among older Japanese populations, and to examine whether income or education has a stronger association with dental implant use. We hypothesised that: (i) social inequalities in dental implant use exist in Japan; and (ii) social inequalities in dental implant use are more strongly associated with income level than with education level.
METHODS
In our cross-sectional study, we used data from the Japan Gerontological Evaluation Study (JAGES), an ongoing prospective cohort study concerned with the cognitive function, social and health status of the older Japanese population16. In 2016, the JAGES survey was conducted in 38 municipalities in 18 different prefectures (out of 47 prefectures) across Japan for a community-dwelling population aged 65 years or older. The JAGES survey is a collaborative survey of researchers and municipality governments. Depending on the municipalities’ policies, population size and allocated budget, a simple random sampling was conducted in 22 municipalities, and a survey for all 65 years or older residents was conducted in 16 municipalities. A self-reported questionnaire containing a question related to dental implants was sent by mail to 279,661 functionally independent target participants. In the questionnaire, we asked about the number of remaining teeth as follows: ‘How many natural teeth do you presently have?’, with five potential answers, ‘I have no natural teeth’, ‘I have 1–4 natural teeth’, ‘I have 5–9 natural teeth’, ‘I have 10–19 natural teeth’, or ‘I have 20 or more natural teeth’. We followed STROBE guidelines for cross-sectional studies.
Outcome variable
Our outcome variable was having dental implants or not. In our questionnaire, we asked ‘Do you have any dental implants in your mouth?’, and the participants chose ‘Yes’ or ‘No’.
Predictors
The two predictors for SES were equivalised income level and years of formal education. Questions related to annual household income, number of people in the household, and years of formal education were included in the JAGES project questionnaire. We calculated equivalised income level as the annual pre-tax household income divided by the squared root of the number of people in the household. According to the Japanese comprehensive survey of living conditions, the relative poverty level, which refers to household incomes less than half of the median annual household income, was 12,200 USD in 201317. We used this threshold to categorise our lowest income level group. Following a previous study18, we applied other income categories as shown in Tables 1–3. We used the currency exchange rate of 100 JPY = 1 USD to convert our equivalised income level results to USD. For years of formal education, we asked ‘How many years of formal education do you have?’, with four categorical answers: ‘< 6, 6–9, 10–12, ≥ 13 years’. We grouped the first two categories together because the number of respondents with < 6 years of formal education who used dental implants was small (n = 5).
Confounders
Age and sex were included as demographic characteristics. The number of dental clinics in residential areas was considered a proxy for geographical accessibility to dental care, including dental implant treatment. Because geographical accessibility to care and SES were correlated, it was considered a confounder. For each of the included 38 municipalities, we gathered the data on the number of dental clinics in the residential area and the corresponding population size from the 2014 survey of Physicians, Dentists and Pharmacists19 to calculate the density of dental clinics per 10,000 residents.
Statistical methods and data analysis
First, Spearman’s correlation between SES measurements, income and education was used to examine whether or not there was a possibility of multicollinearity when they were included into the same regression model. Then, we built seven multi-level logistic regression models to calculate the odds ratio of dental implant use with both of our SES predictors. We initially ran six models for equivalised income level and years of formal education separately (three models for each). The first and second models were unadjusted. The third and fourth models were adjusted for age and sex. The fifth and sixth models were adjusted for age, sex and number of dental clinics in residential areas. In a seventh and final fully adjusted model, we included both equivalised income level and years of formal education simultaneously. In addition, to compare the degree of the associations of income and education on implant use, both SES variables and Z-scores were included in the final model as continuous variables. A multiple imputation (MI) analysis was applied to address missing data. We used 20 chained equation analyses. For sensitivity analysis, we applied complete case analysis (n = 58,232) after dropping all participants with any missing value on the outcome, predictors or confounders. All analyses were conducted using Stata/SE 14 software from StataCorp LP (College Station, TX, USA).
RESULTS
Of the 279,661 functionally independent target population to whom the questionnaire was mailed, 179,991 responded (response rate = 64.3%). Only those who chose the remaining teeth categories ‘having 19 teeth or less’ (n = 84,718) were included in the analysis, because the rest of respondents selected having 20 or more teeth (n = 95,273) and might have a complete dentition without any dental implant needs. In our study population, 47.9% of the participants were males (n = 38,990), and 52.1% were females (n = 42,318). The mean age was 75.70 (SD ± 6.65 years). Descriptive statistics are shown in Table 1. Overall, 2,552 (3.1%) participants had dental implants. Higher dental implant use was observed in females and younger age groups. Spearman’s correlation test showed lower correlation between income and education variables (Spearman’s rho = 0.22, P < 0.0001). In our analysed data, 24,721 participants had missing data on equivalised income level, 1,884 had missing data on years of formal education, and 3,410 had missing data on having dental implants or not. All these data were imputed and included in the analyses, while we had complete data for age, sex and density of dental clinics in residential areas.
Table 1.
Descriptive distribution of dental implant use
| Variable name | Dental implant use N (%) |
Total N (%) | |
|---|---|---|---|
| No | Yes | ||
| Sex | |||
| Male | 37,978 (97.4) | 1,012 (2.6) | 38,990 (47.9) |
| Female | 40,778 (96.3) | 1,540 (3.6) | 42,318 (52) |
| Age group (years) | |||
| 65–69 | 17,302 (95.4) | 830 (4.5) | 18,132 (22.3) |
| 70–74 | 19,030 (96.3) | 732 (3.7) | 19,762 (24.3) |
| 75–79 | 19,144 (97) | 587 (2.9) | 19,731 (24.2) |
| 80–84 | 14,483 (98) | 296 (2) | 14,779 (18.1) |
| 85-max | 8,797 (98.8) | 107 (1.2) | 8,904 (10.9) |
| Formal education (years) | |||
| ≤ 9 | 32,443 (98.1) | 597 (1.8) | 33,040 (41.5) |
| 10–12 | 29,240 (96.4) | 1,078 (3.5) | 30,318 (38) |
| ≥ 13 | 15,423 (94.8) | 839 (5.1) | 16,262 (20.4) |
| Equivalised income level groups (1,000 USD/year) | |||
| 0 < 12.2 | 12,426 (98.2) | 222 (1.7) | 12,648 (20.7) |
| 12.2 ≤ 29.7 | 34,108 (97) | 1,046 (2.9) | 35,154 (57.7) |
| 29.7 ≤ 59.4 | 10,647 (94.6) | 605 (5.3) | 11,252 (18.4) |
| ≥ 59.4 | 1,614 (89.5) | 188 (10.4) | 1,802 (2.9) |
| Density of dental clinics/10,000 individuals | |||
| < 3 | 4,258 (97) | 130 (2.9) | 4,388 (5.4) |
| 3 < 4 | 11,389 (97.5) | 287 (2.4) | 11,676 (14.3) |
| 4 < 5 | 27,215 (97.3) | 734 (2.6) | 27,949 (34.3) |
| 5 < 6 | 15,155 (96.1) | 613 (3.8) | 15,768 (19.3) |
| ≥6 | 20,739 (96.3) | 788 (3.6) | 21,527 (26.4) |
N, number of participants.
A clear step-wise social gradient in dental implant use by both equivalised income level and years of formal education variables was observed (Figure 1), and the association between the equivalised income level and dental implant use appeared to be stronger than that between years of formal education and dental implant use. Table 2 shows the results of the multi-level logistic regression analyses with MI. A clear step-wise social gradient was also observed in dental implant use in all our regression models consistently across all higher income or education groups. A steep increase in the odds ratios for dental implant use was observed among the respondents with the highest income (≥ 59.4 ‘1,000 USD/year’) in all regression models. In our fully adjusted model (model 7), when we included both SES variables, the odds ratio of income appeared to be higher than that of education. To compare the degrees of odd ratios of income and education, we standardised these variables and included them into the fully adjusted model. As a result, the odds ratios of one standard deviation change of income and education were 1.46 [95% CI = 1.38–1.55] and 1.34 [95% CI = 1.28–1.41], respectively. Our sensitivity analysis performed using complete case analysis, after dropping all participants with missing values, revealed very similar results (Table 3).
Figure 1.
Dental implant use (%) by equivalised income level and years of formal education (n = 58,232).
Table 2.
Odds ratios of equivalised income level, years of formal education, and confounders for dental implant use by multi-level logistic regression models with MI (N = 84,718)
| Model 1 |
Model 2 |
Model 3 |
Model 4 |
Model 5 |
Model 6 |
Model 7 |
|
|---|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Years of formal education | |||||||
| ≤ 9 | 1 | 1 | 1 | 1 | |||
| 10–12 | 1.92 (1.75–2.10) | 1.76 (1.60–1.93) | 1.75 (1.59–1.93) | 1.54 (1.41–1.70) | |||
| ≥ 13 | 2.73 (2.48–3.00) | 2.64 (2.37–2.94) | 2.63 (2.36–2.92) | 2.13 (1.94–2.35) | |||
| Equivalised income level (1,000 USD/year) | |||||||
| 0 < 12.2 | 1 | 1 | 1 | 1 | |||
| 12.2 ≤ 29.7 | 1.71 (1.52–1.92) | 1.72 (1.53–1.94) | 1.72 (1.54–1.94) | 1.56 (1.39–1.76) | |||
| 29.7 ≤ 59.4 | 3.17 (2.76–3.63) | 3.15 (2.71–3.66) | 3.15 (2.71–3.67) | 2.66 (2.30–3.08) | |||
| ≥ 59.4 | 6.00 (4.76–7.57) | 6.00 (4.64–7.75) | 5.99 (4.64–7.75) | 4.85 (3.78–6.22) | |||
| Sex | |||||||
| Male | 1 | 1 | 1 | 1 | 1 | ||
| Female | 1.60 (1.46–1.74) | 1.64 (1.50–1.78) | 1.64 (1.50–1.78) | 1.60 (1.46–1.74) | 1.70 (1.55–1.86) | ||
| Age group (years) | |||||||
| 65–69 | 1 | 1 | 1 | 1 | 1 | ||
| 70–74 | 0.82 (0.72–0.92) | 0.84 (0.74–0.95) | 0.84 (0.74–0.95) | 0.82 (0.72–0.92) | 0.86 (0.76–0.97) | ||
| 75–79 | 0.67 (0.58–0.77) | 0.69 (0.60–0.79) | 0.69 (0.60–0.79) | 0.66 (0.58–0.77) | 0.72 (0.63–0.83) | ||
| 80–84 | 0.45 (0.39–0.53) | 0.48 (0.41–0.56) | 0.48 (0.41–0.56) | 0.45 (0.39–0.53) | 0.50 (0.43–0.58) | ||
| 85-max | 0.27 (0.21–0.35) | 0.31 (0.24–0.40) | 0.30 (0.23–0.40) | 0.27 (0.21–0.35) | 0.31 (0.24–0.40) | ||
| Density of dental clinics/10,000 individuals | |||||||
| < 3 | 0.72 (0.39–1.34) | 0.69 (0.37–1.28) | 0.74 (0.40–1.36) | ||||
| 3 < 4 | 0.74 (0.54–1.02) | 0.68 (0.47–0.97) | 0.73 (0.53–1.00) | ||||
| 4 < 5 | 0.85 (0.62–1.15) | 0.80 (0.57–1.12) | 0.84 (0.61–1.14) | ||||
| 5 < 6 | 1.08 (0.80–1.44) | 1.06 (0.75–1.49) | 1.05 (0.78–1.41) | ||||
| ≥ 6 | 1 | 1 | 1 | ||||
| Variance at municipality level | 0.11 (0.06–0.18) | 0.12 (0.77–0.20) | 0.11 (0.07–0.19) | 0.11 (0.06–0.18) | 0.09 (0.05–0.16) | 0.09 (0.05–0.15) | 0.08 (0.04–0.14) |
CI, confidence interval; OR, odds ratio.
All P-values were < 0.001, except for the ‘density of dental clinics’ variable.
Model 1, 2: Unadjusted.
Model 3, 4: Age and sex adjusted.
Model 5, 6: Age, sex and density of dental clinics adjusted.
Model 7: All confounders, years of formal education and equivalised income level adjusted.
Table 3.
Results of the sensitivity analysis (complete case analysis): odds ratios of equivalised income level, years of formal education, and confounders for dental implant use by multi-level logistic regression models (N = 58,232)
| Model 1 |
Model 2 |
Model 3 |
Model 4 |
Model 5 |
Model 6 |
Model 7 |
|
|---|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Years of formal education | |||||||
| ≤ 9 | 1 | 1 | 1 | 1 | |||
| 10–12 | 1.92 (1.71–2.15) | 1.75 (1.56–1.96) | 1.73 (1.54–1.94) | 1.54 (1.38–1.72) | |||
| ≥ 13 | 2.82 (2.50–3.17) | 2.76 (2.43–3.14) | 2.71 (2.37–3.09) | 2.22 (1.96–2.50) | |||
| Equivalised income level (1,000 USD/year) | |||||||
| 0 < 12.2 | 1 | 1 | 1 | 1 | |||
| 12.2 ≤ 29.7 | 1.68 (1.50–1.89) | 1.71 (1.53–1.92) | 1.71 (1.52–1.92) | 1.55 (1.38–1.72) | |||
| 29.7 ≤ 59.4 | 3.14 (2.77–3.56) | 3.18 (2.76–3.66) | 3.17 (2.73–3.68) | 2.69 (2.34–3.10) | |||
| ≥ 59.4 | 6.03 (4.87–7.46) | 6.17 (4.82–7.89) | 6.11 (4.74–7.88) | 4.92 (3.87–6.26) | |||
| Sex | |||||||
| Male | 1 | 1 | 1 | 1 | 1 | ||
| Female | 1.64 (1.46–1.83) | 1.71 (1.53–1.90) | 1.71 (1.53–1.90) | 1.64 (1.47–1.83) | 1.76 (1.57–1.97) | ||
| Age group (years) | |||||||
| 65–69 | 1 | 1 | 1 | 1 | 1 | ||
| 70–74 | 0.78 (0.69–0.89) | 0.81 (0.70–0.92) | 0.80 (0.70–0.92) | 0.78 (0.68–0.89) | 0.82 (0.72–0.94) | ||
| 75–79 | 0.70 (0.60–0.81) | 0.72 (0.62–0.84) | 0.71 (0.61–0.84) | 0.69 (0.59–0.81) | 0.75 (0.64–0.88) | ||
| 80–84 | 0.44 (0.36–0.53) | 0.47 (0.38–0.58) | 0.47 (0.38–0.58) | 0.43 (0.35–0.53) | 0.48 (0.39–0.59) | ||
| 85-max | 0.25 (0.18–0.34) | 0.29 (0.20–0.40) | 0.28 (0.20–0.41) | 0.25 (0.17–0.35) | 0.28 (0.20–0.40) | ||
| Density of dental clinics/10,000 individuals | |||||||
| < 3 | 0.66 (0.35–1.24) | 0.62 (0.32–1.20) | 0.69 (0.36–1.31) | ||||
| 3 < 4 | 0.65 (0.48–0.87) | 0.59 (0.41–0.84) | 0.65 (0.47–0.90) | ||||
| 4 < 5 | 0.75 (0.55–1.01) | 0.70 (0.49–1.00) | 0.75 (0.54–1.05) | ||||
| 5 < 6 | 1.03 (0.81–1.31) | 1.01 (0.74–1.37) | 0.98 (0.74–1.30) | ||||
| ≥ 6 | 1 | 1 | 1 | ||||
| Variance at municipality level | 0.13 (0.05–0.36) | 0.17 (0.04–0.64) | 0.17 (0.05–0.56) | 0.14 (0.57–0.35) | 0.09 (0.05–0.17) | 0.09 (0.04–0.17) | 0.08 (0.04–0.15) |
CI, confidence interval; OR, odds ratio.
All P-values were < 0.001 except for the ‘density of dental clinics’ variable.
Model 1, 2: Unadjusted.
Model 3, 4: Age and sex adjusted.
Model 5, 6: Age, sex and density of dental clinics adjusted.
Model 7: All confounders, years of formal education and equivalised income level adjusted.
DISCUSSION
To the best of our knowledge, this is the first study to examine the association between income level and dental implant use. In addition, we compared the effect of the association of both income and education levels on implant use. Additionally, this is the first study to describe the prevalence and demography of dental implant use in Japan. Income level showed a stronger association with dental implant use than did education level. In addition, both education and income levels were independently associated with dental implant use. Our study suggests that even participants with the highest education level but without high income might have limited accessibility to dental implant treatment. This study also shows that there is a clear, step-wise social gradient in dental implant use favouring each successively higher SES group.
Study limitations and strengths
There are several limitations in this study. Self-reporting of whether or not patients had implants may have been flawed because the respondents may have mis-classified the treatment they received. However, the prevalence of dental implant use (3.1%) found in our study is similar to the prevalence (3.6%) of dental implant use in Japanese individuals aged 65 years and older, as reported by a clinical dental examination-based survey conducted by the Japanese Ministry of Health, Labour and Welfare in 201620. In addition, there were two other separate questions related to bridge and denture treatments in our questionnaire. Therefore, the possibility of mis-classification is likely to be small. In addition, the number of dental implants per capita was not included in our questionnaire. Such information would have provided deeper insight concerning the quantity of dental implants for each participant and their association with SES. Additionally, data on income and dental status are self-reported; therefore, the association between income and dental implant use might have been over- or under-estimated. However, self-reported data are reliable for oral epidemiological studies in Japan21. Due to the limitations of our questionnaire, the last category of participants who reported having 20 or more teeth was dropped from the analysis because some participants in that group might have a complete dentition. However, we analysed the full data set including this category and found almost the same results (Table S1). Finally, because our target population was not a nationally representative sample, generalisability was limited.
There are several strengths in this study. This is the first study to examine the association between income level and dental implant use. Additionally, it is the first study to include two socioeconomic predictors simultaneously in an analytical model to assess the stronger predictor of social inequalities in dental implant use. Additionally, our study is a community dwelling-based study for elder population with a large sample size. Most importantly, dental implant provision in Japan is an ideal service for assessing social inequalities in the ‘out of insurance’ dental health services utilisation, as the universal health insurance system provides wider coverage for basic dental services but not dental implants. Finally, this is the first study to describe the precise pattern (step-wise social gradient) of social inequalities in dental implant use because previous studies have used only binary categories of SES (high and low) and, therefore, they could not show the social gradient of dental implant use.
Our results are consistent with the results of three previous studies from the USA. Those studies reported an increase in the odds ratio for dental implant use among the higher SES groups2., 6., 7.. However, none of these studies examined the association of income level as a predictor of dental implant use. Additionally, our study’s prevalence is similar to the prevalence reported in previous studies in other countries. For example, in a recent study from the USA, the prevalence of dental implant use rose from 0.7% in 1999 to 2000 to 5.7% in 2015 to 20162. In Switzerland, the prevalence of dental implant use in 2002 was 4.4%22. Additionally, in a comparative study, the prevalence of dental implants among Swedes was 4.8%, while it was 2.5% among Danes23. In a longitudinal study from Sweden, a rise in patient demand for dental implant therapy was significantly associated with higher income levels24. Most of these previous studies are relatively old, were focused on the prevalence of dental implant use in a given population, and investigated the patient’s need or the decision-making of dental implant therapy rather than examining the association between dental implant use and SES.
With respect to public health implications, our study provides highly relevant information for policy and health care decision-makers to better understand the determinants and extent of social inequalities in the utilisation of health services that are not covered by insurance, especially in a system that provides universal health care for everyone, such as the Japanese system. To address these clear and extensive social inequalities in implant use and in alignment with the National Health Service (NHS) guidelines for implant use in UK25, one option would be to include the dental implant service in the health insurance scheme but only for those who are most in need, such as the edentate who belong to low socioeconomic groups and have severe denture intolerance. In essence, the first line of treatment for the edentate would be the provision of conventional full dentures. In cases in which these dentures do not result in sufficient levels of oral function, implant-supported overdentures, which are associated with increased levels of satisfaction and quality of life26., 27., can be subsidised for low socioeconomic groups, while higher socioeconomic groups would be expected to cover the cost by themselves. In contrast to Japan, two European countries are adopting reimbursement of dental implant costs. In the Netherlands, the health insurance system reimburses most costs of an implant overdenture for edentulous patients with atrophic alveolar ridges. In Sweden, there is equal reimbursement for removable and fixed implant prostheses exists, with a predominance of fixed implant supported prostheses over removable implant supported prostheses28. Additionally, in Korea, the health insurance system reimburses the costs of two implants throughout the patient’s lifetime for those aged 65 years and older29.
Future studies are needed to assess the trends of dental implant use in Japan over time, preferably including data from younger age groups and from a nationally representative sample including the number of dental implants per capita, and site of implant placement. As an important proxy for the SES among older people, wealth inequalities in dental implant use should also be examined in future studies. Furthermore, studies are needed to assess the causality between income, education and dental implant use. Additionally, surveys are needed to assess the actual workforce of dental practitioners who perform dental implant treatments to better estimate the association between dental implant use and the number of dental implant treatment providers. Finally, comparative analysis studies from different countries with different health care systems can help explain the global trends in dental implant use.
CONCLUSION
Both equivalised income level and years of formal education were independently associated with dental implant use. Slightly stronger association was observed between equivalised income level and dental implant use. Our study suggests that even participants with the highest education level but without high income might have limited accessibility to dental implant services. Relevant dental health policies are needed to reduce inequalities in dental implant use.
Acknowledgements
The authors are grateful to the people who participated in this study. This study used data from the Japan Gerontological Evaluation Study (JAGES), which was supported by the JSPS (Japan Society for the Promotion of Science), KAKENHI (Grant Numbers JP15H01972, 15H04781 and 16H05556), Health Labour Sciences Research Grants (H28-Choju-Ippan-002), the Research and Development Grants for Longevity Science from Japan Agency for Medical Research and Development (AMED), Personal Health Record (PHR) Utilization Project from AMED, the Research Funding for Longevity Sciences from National Center for Geriatrics and Gerontology(29-42), and the World Health Organization Centre for Health Development (WHO Kobe Centre) (WHO APW 2017/713981).
Conflicts of interest
The authors do not have any conflicts of interest to declare.
Funding
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Author contributions
Hazem Abbas contributed to the conception of the study, study design, data acquisition, analysis, interpretation and drafted the manuscript; Jun Aida contributed to the conception, design, data acquisition, analysis and interpretation; Masashige Saito contributed to the data analysis; Georgios Tsakos, Richard G. Watt and Shigeto Koyama contributed to the data interpretation; and Katsunori Kondo and Ken Osaka contributed to the data acquisition. All authors critically revised the manuscript, gave final approval, and agree to be accountable for all aspects of the work ensuring integrity and accuracy.
SUPPORTING INFORMATION
Additional supporting information may be found in the online version of this article:
Table S1 Results of full data set including participants with ≥ 20 teeth (complete case analysis): odds ratios of equivalised income level, years of formal education, and confounders for dental implant use by multi-level logistic regression models (N = 132,320).
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