Abstract
Backgrounds
Lower relative income is recognized as an important social determinant of health. Poor people are also less likely to invest in oral health due to limited resources. The aims of this study was to investigate the association of preventive dental scaling with income status and change in a large population.
Methods
This study was done on adults aged 30 years or older from January 2014 to December 2017 in the National Health Insurance System (NHIS) database. During this period, 558,067 individuals underwent health examinations, of whom 510,864 were adults aged 30 or older. The final sample for this study sample consisted of 497,812 adults aged 30 years and older, excluding individuals with missing data. Monthly health insurance premium data were offered in 20 quintiles, which were divided into five levels, from quintile 1 (lowest income status) to quintile 5 (highest income status). Participants’ income quintiles were aggregated annually from 2014 to 2017. The cumulative number of years with lower or higher income was used to examine the association between income changes and scaling. Multivariate analysis was used to evaluate the association of income status and health insurance type with scaling. Multiple logistic regression analysis was used to assess the association of income changes with scaling between the final observation year and the baseline year and the association between cumulative income status and the scaling. Odds ratio (ORs) and 95% confidence intervals (CIs) were estimated after adjusting for potential confounders.
Results
The ORs for the income quintile 2 and 3 groups were lower than the Medical Aid group, but overall, the lower income group was receiving less scaling (p < 0.0001). As income increased, people were more likely to perform scaling (p < 0.0001). Those receiving Medical Aid were less likely to receive scaling than those with health insurance (p < 0.0001). People with more cumulative low income status were less likely to receive scaling (p < 0.0001). Those whose base year income level improved over previous four years received more scaling and the Medical Aid and quintile 1 group required three income levels to increase ORs, while the other income groups increased ORs with a single level increase in income level (p < 0.0001).
Conclusions
Despite insurance coverage, lower incomes were associated with less scaling and participation in scaling increased as income increased. The Medical Aid and quintile 1 groups show that, unlike other income groups, participation in scaling increases only when their income rises above a certain level. High income variability, income decline, and persistently low income were associated with lower participation in scaling.
Keywords: National health information database, National health insurance system, Income, Periodontitis, Preventive dental scaling
Introduction
Lower relative income is recognized as an important social determinant of health. People living in lower relative income have fewer resources to meet their health needs, making it difficult for them to make affordable health investments [1, 2]. Poor people are also less likely to invest in oral health due to limited resources.
Oral disease is a major public health problem worldwide, with adverse effects on the physical and mental health of individuals and a significant societal burden. Periodontal disease has been considered the most important oral health burden and the leading cause of teeth loss worldwide [3]. Periodontal disease can increase the risk of developing chronic diseases such as diabetes and cardiovascular disease, which have long-term effects on overall health [4, 5]. Proper prevention and treatment of periodontal disease is very important for oral and systemic health. Scaling is the gold standard of periodontal treatment and prevention. However, access to most dental care, including scaling, is limited by a number of factors, with many studies consistently reporting income-related inequalities in access to dental care. In most European countries, access to care was significantly higher in the highest income deciles than in the lowest, reflecting inequalities in preventive dental care [6].
Although people with lower levels of education and income have greater dental needs, those with higher levels of education and income are more likely to use dental services [7]. People living in areas with the highest income inequality were 3.0 times more likely to have severe periodontal disease, and pocket depth was reported to correlate with income inequality [8]. A cohort study reported that family income at birth and at the ages of 15, 19, 23 and 30 was associated with the development of moderate to severe periodontitis at the age of 31, regardless of behavioral factors such as smoking [9]. There was also a more than a threefold increase in adult periodontal disease (31.1% vs. 11.9%) and caries (32.2% vs. 9.9%) in individuals with a low childhood socioeconomic status compared to those with a high one [10]. However, these studies did not examine the impact of changes in income status during adulthood on access to preventive dental care.
This study aims to investigate the association between preventive dental scaling and income status in a large population and to examine the impact of income changes on preventive dental scaling participation rates.
Materials and methods
Data source
This study used data from the National Health Information Database (NHID), produced by the National Health Insurance System (NHIS). The NHIS is the only compulsory health insurance program in operation, covering around 97% of the Korean population. Since its establishment in 2000, the NHIS has provided longitudinal data on this population in conjunction with the National Death Registry and the National Health Examination Program [11, 12]. The remaining 3% of the population, who are primarily low earners, are covered by health assistance programs, and their data has been incorporated into the NHIS database. The NHIS database is predicated on ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes. Data relating to health behaviors and anthropometry, laboratory tests, and physical and mental health assessment was collected through a standardized national general health examination program. These programs are conducted biennially for local subscribers aged 20 years or older. This study was approved by the Institutional Review Board of The Catholic University of Korea, Yeouido St. Mary’s Hospital and adhered to the ethical guidelines of the Declaration of Helsinki. A clinical trial number was not applicable.
The data supporting the findings of this study are available from the Korea National Health Insurance Service (NHIS) but restrictions apply to their availability as they were used under license for the current study, and therefore not publicly available. However, data are available from the authors upon reasonable request and with the permission of the Korea National Health Insurance Service. Due to legal restrictions and concerns for patient privacy, and given that the Korea National Health Insurance Service (NHIS) owns the data, the raw data and declaration of consent cannot be shared publicly. However, you can access details of the dataset by visiting the Korea National Health Insurance Service homepage at https://nhiss.nhis.or.kr/. Alternatively, the date can be requested via nhiss@nhis.or.kr or by calling + 82-1577-1000. Requests can also be sent in writing to 32 Gungang-ro, Wonju-si, Gangwon-do 26,464.
Study population
Since July 2013, the NHIS has covered adults aged 20 years and over for dental scaling once a year. To distinguish it from scaling for therapeutic purposes, scaling for preventive purposes is managed using a separate prescription code. From January 2014 to December 2017, 558,067 individuals underwent health examinations, of whom 510,864 were adults aged 30 or older. The final sample for this study consisted of 497,812 adults aged 30 years and older, excluding individuals with missing data (Fig. 1).
Fig. 1.
Flow diagram
Variables
A definition of sociodemographic variables is the same as that stated in the previous paper [13]. Household income was adjusted for the number of family members and categorized into quintiles. Alcohol consumption was categorized as never, mild a heavy (≥ 30 g alcohol per day for men and ≥ 20 g alcohol per day for women). Smoking was categorized as non-smokers, former smokers and current smokers. Regular exercise was defined as undertaking at least two and a half hours of moderate-intensity physical activity, one and a half hours of high-intensity physical activity, or a combination of both, per week. Body mass index (BMI) was calculated using the following formula: weight/height2 (kg/m2). Serum glucose, total cholesterol, triglyceride, high density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol were measured after fasting over eight hours. Hypertension was defined as ICD-10 codes I10 to I13 or I15 and treatment with antihypertensive medications, systolic blood pressure (BP) ≥ 140mmHg, or diastolic BP ≥ 90mmHg). Type 2 diabetes was defined as ICD-10 codes E11 to E14, treatment with anti-diabetic drugs, or a fasting glucose level of at least 126 mg/dL). Hyperlipidemia was defined as ICD-10 code E78, treatment with lipid-lowering agents, or serum total cholesterol of at least 240 mg/dL.
Scaling for preventive purposes is managed with a different prescription code to scaling for treatment purposes. As the outcome variable in this study was preventive dental scaling, the prescription code for therapeutic scaling (regardless of referral) was excluded. Preventive dental scaling was defined as undergoing a scaling procedure in the same year as a health check-up.
Definition of income status
Since the National Health Insurance Service (NHIS) does not provide household income data, we used health insurance premiums as an indicator of household income, as we did in our previous paper [14, 15]. Monthly health insurance premiums usually remain the same throughout the year, unless there is a significant change in income. Employee premiums are based on wages, whereas self-employed premiums are based on income and assets. The monthly health insurance premium data were presented in twenty quintiles, which were then categorized into one of five groups ranging from quintile 1 (lowest income) to quintile 5 (highest income). Those with a very low income (about 3% of South Korea’s total population) were categorized as Medical Aid recipients. If a person’s income dropped and they met the Medical Aid eligibility criteria, they could receive medical assistance the following year. If a Medical Aid recipient’s income increased and they met the eligibility criteria, they could switch from medical assistance to health insurance. Among those transitioning to health insurance, low-income individuals just above Medical Aid eligibility are classified as the second tier. They have lower out-of-pocket medical expenses than higher-income groups. Participants’ income quintiles were aggregated annually from 2014 to 2017. The cumulative number of years with a lower or higher income was examined to study the association between income changes and preventive dental scaling. Cumulative income status was counted for the base year and the four preceding years. Changes in income status were then compared between the baseline year and the preceding four years.
Statistical analyses
Descriptive statistics were used to analyze the baseline characteristics. An independent t-test was used for continuous variables and a chi-squared test was used for categorical variables to ascertain whether there were statistically significant differences between the scaling and non-scaling groups for each baseline characteristic variable. Multivariate analysis was employed to assess the association between income status, health insurance type and scaling. Odds ratios (OR) and 95% confidence intervals (CI) were estimated after adjusting for potential confounders. Multiple logistic regression analysis was used to assess the association between cumulative income status and scaling. Odds ratios (OR) and 95% confidence intervals (CI) were estimated after adjusting for potential confounders. Multiple logistic regression analysis was used to assess the association between income changes and scaling from the baseline year to final observation year. Odds ratios (OR) and 95% confidence intervals (CI) were estimated following adjustment for potential confounders. All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Two-sided p-values of less than 0.05 were considered to be statistically significant.
Results
The result of this study represents an administratively coded preventive scale (i.e., preventive). Table 1 shows the general characteristics of the population categorized as having or not having undergoing preventive scaling. Of the 497,812 individuals, 95,423 people were in the group that underwent scaling for preventive purposes. The mean age of the scaling group was lower than that of the non-scaling group (52.37 ± 13.53 vs. 48.63 ± 11.63, p < 0.0001). People aged 65 and over were less likely to have had a preventive dental scaling than those in other age groups. (p < 0.0001). Those with employee insurance group had higher use of preventive dental scaling (p < 0.0001). Those in the Medical Aid to quintile 3 income group were less likely to have had preventive dental scaling than the those in above quintile 4 income group (p < 0.0001). The prevalence of diabetes, hypertension, dyslipidemia and chronic kidney diseases was lower in the scaling group than in the non-scaling group (p < 0.0001).
Table 1.
The characteristics of study participants
| Total | Preventive dental scaling | p-value | ||
|---|---|---|---|---|
| No | Yes | |||
| N | 497,812 | 402,389 | 95,423 | |
| Age, years | 52.37(13.53) | 48.63(11.63) | < 0.0001 | |
| Age groups, year | ||||
| < 40 | 93,555(18.79) | 71,737(17.83) | 21,818(22.86) | < 0.0001 |
| 40–64 | 31,6613(63.6) | 25,2107(62.65) | 64,506(67.6) | |
| ≥ 65 | 87,644(17.61) | 78,545(19.52) | 9,099(9.54) | |
| Sex | ||||
| Male | 249,239(50.07) | 202,552(50.34) | 46,687(48.93) | < 0.0001 |
| Female | 248,573(49.93) | 199,837(49.66) | 48,736(51.07) | |
| Health Insurance Type | ||||
| Self-employed insured | 120,338(24.17) | 99,215(24.66) | 21,123(22.14) | < 0.0001 |
| Employee insured | 372,708(74.87) | 299,173(74.35) | 73,535(77.06) | |
| Medical Aid | 4,766(0.96) | 4,001(0.99) | 765(0.8) | |
| Place, Urban | 225,930(45.38) | 178,787(44.43) | 47,143(49.4) | < 0.0001 |
| Income | ||||
| Quintile 1, Medical Aid | 79,223(15.91) | 65,789(16.35) | 13,434(14.08) | < 0.0001 |
| Quintile 2 | 73,942(14.85) | 61,049(15.17) | 12,893(13.51) | |
| Quintile 3 | 92,806(18.64) | 76,039(18.9) | 16,767(17.57) | |
| Quintile 4 | 115,949(23.29) | 93,340(23.2) | 22,609(23.69) | |
| Quintile 5 | 135,892(27.3) | 106,172(26.39) | 29,720(31.15) | |
| BMI (body mass index) | ||||
| < 18.5 | 16,057(3.23) | 12,918(3.21) | 3,139(3.29) | < 0.0001 |
| < 23 | 182,534(36.67) | 145,271(36.1) | 37,263(39.05) | |
| < 25 | 120,432(24.19) | 97,303(24.18) | 23,129(24.24) | |
| < 30 | 154,269(30.99) | 126,295(31.39) | 27,974(29.32) | |
| ≥ 30 | 24,520(4.93) | 20,602(5.12) | 3,918(4.11) | |
| Smoking | ||||
| Never | 308,266(61.92) | 247,908(61.61) | 60,358(63.25) | < 0.0001 |
| Former | 81,951(16.46) | 64,886(16.13) | 17,065(17.88) | |
| Current | 107,595(21.61) | 89,595(22.27) | 18,000(18.86) | |
| Drinking | ||||
| Never | 268,083(53.85) | 218,780(54.37) | 49,303(51.67) | < 0.0001 |
| Mild (< 30 for man, < 20 for woman) | 189,412(38.05) | 150,285(37.35) | 39,127(41) | |
| Heavy (≥ 30 for man, ≥ 20 for woman) | 40,317(8.1) | 33,324(8.28) | 6,993(7.33) | |
| Regular exercise | 98,386(19.76) | 77,409(19.24) | 20,977(21.98) | < 0.0001 |
| Diabetes mellitus | 59,564(11.97) | 51,364(12.76) | 8,200(8.59) | < 0.0001 |
| Hypertension | 153,163(30.77) | 130,613(32.46) | 22,550(23.63) | < 0.0001 |
| Dyslipidemia | 136,271(27.37) | 112,277(27.9) | 23,994(25.14) | < 0.0001 |
| Chronic kidney diseases | 21,794(4.38) | 18,988(4.72) | 2,806(2.94) | < 0.0001 |
| Age, years | 51.65 ± 13.27 | 52.37 ± 13.53 | 48.63 ± 11.63 | < 0.0001 |
| Height, cm | 163.43 ± 9.32 | 163.22 ± 9.4 | 164.33 ± 8.91 | < 0.0001 |
| Weight, kg | 64.43 ± 12.29 | 64.39 ± 12.32 | 64.6 ± 12.19 | < 0.0001 |
| BMI, kg/m2 | 24.01 ± 3.4 | 24.06 ± 3.42 | 23.8 ± 3.29 | < 0.0001 |
| Waist Circumference, cm | 81.14 ± 11.12 | 81.35 ± 10.72 | 80.28 ± 12.66 | < 0.0001 |
| Systolic BP, mmHg | 122.71 ± 14.92 | 123.24 ± 15.04 | 120.46 ± 14.16 | < 0.0001 |
| Diastolic BP, mmHg | 76.25 ± 10.01 | 76.48 ± 10.05 | 75.27 ± 9.8 | < 0.0001 |
| Fasting glucose, mg/dL | 100.69 ± 25.57 | 101.28 ± 26.44 | 98.2 ± 21.38 | < 0.0001 |
| Total Cholesterol, mg/dL | 196.38 ± 39.68 | 196.29 ± 39.98 | 196.75 ± 38.39 | 0.0016 |
| HDL -C, mg/dL | 55.57 ± 15.5 | 55.36 ± 15.34 | 56.47 ± 16.12 | < 0.0001 |
| LDL -C, mg/dL | 115.28 ± 40.28 | 115.14 ± 40.47 | 115.86 ± 39.47 | < 0.0001 |
Data are presented as frequencies (%) in categorical variables and means ± standard deviation (SD) in continuous variables
BP Blood pressure, HDL High density lipoprotein cholesterol, LDL Low-density lipoprotein cholesterol
P-values were obtained by an independent t-test for continuous variables or a chi-square test of categorical variables. P < 0.05 is statistically significant
Table 2 shows the OR (95% CI) results of the multivariate analysis of the association between income status and health insurance type and preventive dental scaling. The ORs for the income quintile 2 and 3 groups were lower than the Medical Aid group, but overall, the lower income group was receiving less scaling (p < 0.0001). Those with employee insurance group were more likely to have dental scaling, in contrast to those with self-employed insurance and Medical Aid (p < 0.0001).
Table 2.
Association of income states and health insurance types with preventive dental scaling
| Number of participants who received preventive dental scaling | OR (95% C.I) | ||||||
|---|---|---|---|---|---|---|---|
| Model 1 | p-value | Model 2 | p-value | Model 3 | p-value | ||
| Income status | |||||||
| Q1,MA | 13,434 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 |
| Q2 | 12,893 |
1.034 (1.007, 1.062) |
0.978 (0.952, 0.005) |
0.981 (0.954, 1.008) |
|||
| Q3 | 16,767 |
1.080 (1.053, 1.107) |
0.993 (0.968,1.018) |
0.998 (0.972, 1.024) |
|||
| Q4 | 22,609 |
1.186 (1.159, 1.214) |
1.117 (1.091, 1.145) |
1.124 (1.097, 1.152) |
|||
| Q5 | 29,720 |
1.371 (1.340, 1.402) |
1.412 (1.379, 1.444) |
1.420 (1.386, 1.453) |
|||
| Health Insurance Type | |||||||
| self-employed | 21,123 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | 0.0005 | 1 (Ref.) | < 0.0001 |
| employee | 73,535 |
1.154 (1.135, 1.174) |
1.023 (1.005, 1.041) |
1.045 (1.027, 1.064) |
|||
| MA | 765 |
0.898 (0.830, 0.972) |
0.903 (0.834, 0.978) |
1.038 (0.957, 1.126) |
|||
Model 1 was not adjusted. Model 2 was adjusted for age, sex, smoking, alcohol consumption, regular exercise, body mass index, diabetes mellitus, hypertension, and dyslipidemia. Model 3 was adjusted for the variables in model 2 plus income level or type of health insurance
Q Quintile, MA Medical Aid
CI Confidence interval, OR Odds ratio
P < 0.05 is statistically significant
Table 3 shows the associations between cumulative income status and preventive dental scaling. ORs decreased as the cumulative number of low income and Medical Aid status increased, and ORs increased as the cumulative number of high income status increased (p < 0.0001).
Table 3.
Association between cumulative income status and the preventive dental scaling
| Cumulative No. of years |
Number of participants who received preventive dental scaling | OR (95% C.I) | |||||
|---|---|---|---|---|---|---|---|
| Model 1 | p-value | Model 2 | p-value | Model 3 | p-value | ||
| MA | |||||||
| 0 | 94,147 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 |
| 1–4 | 682 |
0.717 (0.661, 0.778) |
0.801 (0.738, 0.869) |
0.780 (0.710, 0.857) |
|||
| 5 | 594 |
0.802 (0.735, 0.876) |
0.888 (0.813, 0.970) |
0.786 (0.638, 0.969) |
|||
| Q1 | |||||||
| 0 | 68,231 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 |
| 1–4 | 23,374 |
0.876 (0.861, 0.890) |
0.862 (0.848, 0.877) |
0.860 (0.846, 0.875) |
|||
| 5 | 3,818 |
0.739 (0.714, 0.766) |
0.823 (0.794, 0.853) |
0.818 (0.787, 0.850) |
|||
| Q5 | |||||||
| 0 | 52,681 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 |
| 1–4 | 24,586 |
1.248 (1.227, 1.269) |
1.275 (1.253, 1.297) |
1.277 (1.255, 1.299) |
|||
| 5 | 18,156 |
1.352 (1.326, 1.378) |
1.528 (1.498, 1.559) |
1.529 (1.499, 1.560) |
|||
Model 1 was not adjusted. Model 2 was adjusted for age, sex, smoking, alcohol consumption, regular exercise, body mass index, diabetes mellitus, hypertension, and dyslipidemia. Model 3 was adjusted for the variables in model 2 plus type of health insurance
Q Quintile, MA Medical Aid
CI Confidence interval, OR Odds ratio
P < 0.05 is statistically significant
Table 4 shows the OR (95% CI) results of the multivariate analysis of the association between change in base year income and preventive dental scaling. Those whose base year income level improved over previous four years received more scaling (p < 0.0001). The Medical Aid and quintile 1 group required three income levels to increase ORs, while the other income groups increased ORs with a single level increase in income level.
Table 4.
Changes in income status between the two-time points and the participation of preventive dental scaling
| Income status | Number of participants who received preventive dental scaling | OR (95% C.I) | ||||||
|---|---|---|---|---|---|---|---|---|
| First time point (preceding 4 years) |
Last time point (baseline) |
Model 1 | p-value | Model 2 | p-value | Model 3 | p-value | |
| MA, Q1 | MA, Q1 | 5,554 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 | 1 (Ref.) | < 0.0001 |
| Q2 | 2,960 |
1.095 (1.043, 1.150) |
0.982 (0.935, 1.033) |
0.988 (0.939, 1.039) |
||||
| Q3 | 1,958 |
1.109 (1.049, 1.174) |
0.966 (0.911, 1.024) |
0.979 (0.922, 1.039) |
||||
| Q4 | 1,390 |
1.217 (1.140, 1.299) |
1.129 (1.057, 1.207) |
1.156 (1.081, 1.237) |
||||
| Q5 | 859 |
1.269 (1.172, 1.374) |
1.283 (1.183, 1.390) |
1.328 (1.223, 1.441) |
||||
| Q2 | MA, Q1 | 2,306 |
1.028 (0.973, 1.087) |
< 0.0001 |
1.05 (0.993, 1.110) |
< 0.0001 |
1.047 (0.989, 1.107) |
< 0.0001 |
| Q2 | 4,406 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | ||||
| Q3 | 4,193 |
1.119 (1.069, 1.173) |
1.051 (1.002, 1.102) |
1.049 (1.000, 1.100) |
||||
| Q4 | 1,912 |
1.175 (1.108, 1.247) |
1.145 (1.078, 1.216) |
1.148 (1.080, 1.219) |
||||
| Q5 | 763 |
1.200 (1.102, 1.307) |
1.284 (1.178, 1.399) |
1.296 (1.189, 1.413) |
||||
| Q3 | MA, Q1 | 1,803 |
0.952 (0.898, 1.008) |
< 0.0001 |
0.965 (0.910, 1.023) |
< 0.0001 |
0.965 (0.910, 1.024) |
< 0.0001 |
| Q2 | 2,312 |
0.997 (0.945, 1.050) |
1.006 (0.954, 1.061) |
1.005 (0.953, 1.060) |
||||
| Q3 | 6,159 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | ||||
| Q4 | 5,727 |
1.160 (1.114, 1.207) |
1.106 (1.062, 1.151) |
1.104 (1.060, 1.149) |
||||
| Q5 | 1,327 |
1.273 (1.191, 1.360) |
1.326 (1.239, 1.418) |
1.330 (1.243, 1.423) |
||||
| Q4 | MA, Q1 | 1,883 |
0.927 (0.878, 0.978) |
< 0.0001 |
0.915 (0.866, 0.967) |
< 0.0001 |
0.908 (0.859, 0.960) |
< 0.0001 |
| Q2 | 1,695 |
0.951 (0.898, 1.006) |
0.933 (0.881, 0.988) |
0.929 (0.877, 0.984) |
||||
| Q3 | 2,524 |
0.917 (0.874, 0.962) |
0.919 (0.876, 0.965) |
0.922 (0.878, 0.968) |
||||
| Q4 | 10,485 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | ||||
| Q5 | 6,159 |
1.135 (1.095, 1.175) |
1.120 (1.081, 1.161) |
1.119 (1.080, 1.160) |
||||
| Q5 | MA, Q1 | 1,888 |
0.897 (0.851, 0.946) |
< 0.0001 |
0.836 (0.792, 0.883) |
< 0.0001 |
0.832 (0.788, 0.878) |
< 0.0001 |
| Q2 | 1,520 |
0.913 (0.861, 0.968) |
0.814 (0.767, 0.864) |
0.815 (0.768, 0.865) |
||||
| Q3 | 1,933 |
0.995 (0.943, 1.049) |
0.868 (0.822, 0.916) |
0.868 (0.823, 0.917) |
||||
| Q4 | 3,095 |
0.938 (0.899, 0.979) |
0.883 (0.846, 0.922) |
0.883 (0.845, 0.922) |
||||
| Q5 | 20,612 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | ||||
Model 1 was not adjusted. Model 2 was adjusted for age, sex, smoking, alcohol consumption, regular exercise, body mass index, diabetes mellitus, hypertension, and dyslipidemia. Model 3 was adjusted for the variables in model 2 plus type of health insurance
Q Quintile, MA Medical Aid
CI Confidence interval, OR Odds ratio
P < 0.05 is statistically significant
Discussion
The study found that despite having insurance coverage for preventive dental scaling, lower income was associated with lower participation in scaling, and higher income was associated with higher participation in scaling. Use of preventive dental scaling decreased as the cumulative number of low-income status increased, while receipt of preventive dental scaling increased as the cumulative number of high-income status increased. Use of preventive dental scaling increased when baseline income increased from the previous four years. The Medical Aid and quintile 1 groups only increased receipt of preventive dental scaling if they were in quintile 4 and 5, where baseline income increased by three levels from the previous four years.
As lower relative income is an important determinant of oral health, many countries have welfare programs that provide financial assistance to the poor, including full or partial coverage of dental treatment [16]. In Korea, since July 2013, adults aged 20 years and older have been covered by health insurance for preventive dental scaling once a year. However, use of preventive dental scaling among the poor is still reported to be low. There is a positive relationship between monthly income and use of preventive dental scaling, and the difference in odds ratios by income increases over time [17]. Non-participation in dental scaling decreased from 58.0 to 48.7 per cent in the highest income group and from 86.3 to 78.8 per cent in the lowest income group, but after adjusting for variables, the non-participation rate in the lowest income group increased from 1.19 to 1.29 [18]. In Finland, income-related inequalities in oral health perceptions were also reported to have not improved after dental subsidy reforms [19]. In Sweden, even after the dental care reform, self-perceived poor oral health was highest among the unemployed, those on disability pensions and those with limited financial resources, because they avoided dental care for financial reasons [20]. Although many countries have welfare policies in place, dental care still seems to be unaffordable for low-income people. This is likely due to the fact that out-of-pocket expenses are relatively high for low-income people compared to their income.
Low-income people are known to have a high prevalence of systemic diseases such as diabetes and cardiovascular disease [21, 22]. They will spend more money on their systemic disease care. It is assumed that preventive care for less severe periodontal disease is reduced in the lower income groups because they have to spend within their limited economic means. Unless income increases beyond a certain level, it is unlikely that low-income people will be able to afford to visit a dentist for preventive dental scaling.
The study showed that the second and third quartile income groups received less use of preventive dental scaling than the medical assistance and first quartile income groups. The Medical Aid and quintile 1 income groups received less scaling even as their income increased and they moved into next income groups. The Medical Aid and quintile 1 groups, unlike other income groups, showed at least three-step increase in income level to increase use of preventive dental scaling. In South Korea, all citizens are required to have health insurance, and the typical health insurance enrollee (about 97% of the population) pays about 30% of out-of-pocket expenses. Those with health insurance who fall below the 50 per cent median income threshold are classified as the second tier and pay about 10 to 15 per cent of their out-of-pocket expenses. There is a system to provide additional medical expense reductions based on income level, but to be eligible for these benefits, people in second tier must meet the income, property, and dependency criteria [23]. Low-income people (about 3 per cent of the population) who are eligible for Medical Aid have a median income of 40 per cent or less of the median income, and co-payments for outpatient visits to primary health care facilities are either free or set at less than 1 dollar. As a result, when Medical Aid recipients move to the next level of income, their actual out-of-pocket costs for medical services may increase. This means that Medical Aid recipients have very few out-of-pocket medical expenses, so the actual the burden of out-of-pocket medical expenses likely to increase when Medical Aid recipients move up the second income level tier. When Medical Aid recipient transition to health insurance, the increased medical costs can be a significant financial burden if their income does not increase enough to pay for the increased out-of-pocket costs. In 2016, nearly 1.4 million households had limited health insurance coverage due to six or more delinquent health insurance payments [18]. Previous studies have shown that those who leave public assistance programs use significantly less healthcare than continuing public assistance recipients [24]. This pattern suggests that burden of out-of-pocket expenses is likely the reason why Medical Aid and next tier groups had increased use of preventive dental scaling when incomes above a certain level increased. In fact, unlike other income groups, the Medical Aid and quintile 1 groups only show an increase in use of preventive dental scaling when they are in the quintile 4 and 5, where the baseline income has increased by three levels over the previous four years.
The results of this study showed that self-employed insured people were less likely to receive preventive dental scaling than employee insured and Medical Aid recipients. The premiums for those with workplace health insurance are paid for by the employee and the company, while those with self-employed insurance pay a set premium based on their income, assets, etc. If an employee loses his or her job, he or she will have to pay more premiums, which can add to the financial burden. Previous studies have shown that self-employed insurance members were 1.31 times more likely to experience unmet dental needs due to financial burden than employed health plan members, and 5.47 times more likely than higher-income employed health plan members [25].
Low participation rates in preventive dental scaling for those over 65 years of age in this study. Other study of dental scaling utilization has reported similar findings [26]. Even after dental insurance coverage expanded to drive changes in utilization, absolute utilization declined with age. This may be related to the fact that 8.7% of adults aged 70 and over in the country are edentulous. However, they still require scaling as they have an average of 16.9 natural teeth. The higher use of dental care by younger beneficiaries may be due to the following factors. First, older adults who have lost teeth may not recognize the need for dental services, except for dentures. Second, younger beneficiaries may have higher oral health literacy and access to dental care than older adults. Third, South Korea’s high elderly poverty rate may discourage older adults from visiting the dentist. South Korea’s elderly poverty rate is higher than all other OECD countries: the OECD average was 13.1 per cent in 2018, while South Korea’s was the highest at 43.4 per cent [27]. Low-income seniors seem to be more exposed to unmet dental care needs due to economic factors and lack of opportunities to gain knowledge about oral health.
This study has several limitations. Firstly, the findings of this study are difficult to generalize as it is based on data from a single Asian country. Secondly, the study was based on individuals with a registered health check in the NHIS database, meaning those without a registered health check were excluded. Thirdly, the analysis period was short at around 4 years. Fourthly, no data was provided on the number of household members contributing to or receiving support from household income. Fifthly, the study is unlikely to reflect actual individual income levels because income status was derived from insurance premiums.
Nevertheless, this study has several strengths. Firstly, the study was conducted in a relatively ethnically homogeneous population, so it may reduce confounding by ethnicity. Secondly, as it used findings from a large population, it can provide epidemiological evidence that people on lower incomes are less likely to receive preventive dental scaling, and that individuals are more likely to receive scaling as their income increases.
Conclusions
After adjusting for potential confounding variables, the analysis showed that lower income was associated with less use of preventive dental scaling. People were also more likely to receive scaling as their income increased. However, unlike other income groups, the Medical Aid and quintile 1 groups show that participation in scaling increases only when their income rises above a certain level.
Acknowledgements
Not applicable.
Authors’ contributions
TaeYeon Lee contributed to data interpretation, and manuscript drafting. Kyungdo Han contributed to study conception, design, data analysis and interpretation. Kyoung-In Yun contributed to the study conception, design, data interpretation, and manuscript drafting. All the authors gave their final approval of the version to be published and agreed to be accountable for all aspects of the work.
Funding
This research was supported by grant of the Institute of Clinical Medicine Research in the Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea (Grant Number: YSI24H006). The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data availability
The data that support the findings of this study are available from the Korea National Health Insurance Service (NHIS) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Korea National Health Insurance Service.
Declarations
Ethics approval and consent to participate
All participants provided informed consent prior to data collection. Ethical clearance was obtained from the Institutional Review Board of The Catholic University of Korea, Yeouido St. Mary’s Hospital (study number: SC25ZISE0006) and conducted in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the Korea National Health Insurance Service (NHIS) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Korea National Health Insurance Service.