Socioeconomic and geographic inequalities in dental care utilization among Thai older adults: trends and determinants, 2009–2021

Abstract

Background

Despite Thailand’s universal healthcare coverage, socioeconomic and geographic disparities in dental care utilization persist, particularly among older adults. This study examined the patterns and determinants of inequality in dental care use among older adults in Thailand (aged 60 and above) during the country’s transition to an ageing society (2009–2021).

Methods

Nationally representative data from the Thailand Health and Welfare Survey (2009, 2015, 2021) were analyzed (N = 53,825 older adults). Dental care utilization was assessed via self-reported use in the past 12 months. Weighted analyses and multivariable logistic regression were performed to examine associations between dental care utilization and socioeconomic, geographic, and health-related factors, classified according to the Andersen Behavioral Model of Health Services Use.

Results

Overall dental care utilization among older adults declined from 9.1% in 2009 to 7.2% in 2021, despite near-universal health insurance coverage. Substantial socioeconomic and geographic inequalities in dental care utilization were observed across all survey years. In 2021, after adjustment for covariates, respondents in the highest household asset quartile had significantly greater odds of utilizing dental care than those in the lowest quartile (adjusted odds ratio [aOR] = 2.4; 95% confidence interval [95% CI]: 2.0–2.9). Older adults with tertiary education demonstrated markedly higher odds of utilization compared with those with no formal education (aOR = 6.0; 95% CI: 4.3–8.3). Geographic disparities were also evident, with older adults residing in the Northeast showing substantially lower odds of utilization than Bangkok residents (aOR = 0.3; 95% CI: 0.2–0.3). Beneficiaries of the Civil Servant Medical Benefit Scheme (CSMBS) had higher odds of dental care utilization than those enrolled in the Universal Coverage Scheme (UCS) (aOR = 2.0; 95% CI: 1.8–2.3).

Conclusions

Despite Thailand’s long-standing near-universal health coverage, substantial inequalities in dental care utilization persist among Thai older adults, particularly among socioeconomically disadvantaged and rural populations. These findings suggest that equitable oral healthcare in ageing societies requires policy approaches that extend beyond financial coverage alone and address structural, geographic, and ageing-related barriers to care. Strengthening community-based and geriatric-sensitive oral healthcare delivery may help reduce persistent inequalities in access.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12903-026-09024-9.

Introduction

Population ageing has become a major demographic transition worldwide, particularly in low- and middle-income countries (LMICs), where health systems are increasingly challenged to meet the complex healthcare needs of older adults [1, 2]. Oral health is an important component of healthy ageing, as poor oral conditions and tooth loss are associated with impaired nutrition, reduced quality of life, social isolation, and functional decline [3–5]. Despite the importance of oral healthcare, substantial socioeconomic and geographic inequalities in dental care utilization persist globally, especially among older populations. These disparities remain a major public health concern because unequal access to oral healthcare may exacerbate broader health and social inequalities in later life [6–9]. In response, the World Health Organization (WHO) Global Oral Health Action Plan 2023–2030 has emphasized the need for equitable, integrated, and person-centered oral healthcare systems [10, 11].

Thailand represents an important context for examining oral healthcare inequalities in ageing populations. The country entered an ageing society in 2005 and transitioned toward a complete-aged society in 2022, making it one of the most rapidly ageing countries in Southeast Asia [12]. During this demographic transition, Thailand implemented several major health and ageing policies aimed at improving healthcare access and quality of life among older adults. Since 2001, the implementation of Universal Health Coverage (UHC) policy has provided near-universal financial protection for healthcare services, including dental care. In addition, the revised Second National Plan for Older Persons (2002–2021; revised in 2009) emphasized healthy ageing, healthcare access, and social welfare for older populations, while the National Oral Health Strategic Plan of Thai Older Persons (2015–2022) aimed to strengthen oral healthcare for older adults. Together, these policy developments provide an important context for evaluating the pattern and determinants of dental care utilization among Thai older adults [13–15].

Previous studies have reported persistent socioeconomic inequalities in dental care utilization despite the implementation of universal health coverage, with higher utilization consistently observed among wealthier and more socially advantaged older adults [16–18]. In addition, studies applying the Andersen Behavioral Model have identified important predisposing and enabling determinants of dental and denture service utilization, including education, household assets, dependency status, and health-promoting behaviors [19, 20]. However, several important evidence gaps remain. First, previous Thai studies have focused on specific types of dental services, particularly denture utilization, rather than broader patterns of dental care utilization among older adults. Second, previous research has primarily relied on single cross-sectional datasets, with limited evaluation of whether socioeconomic and geographic inequalities have persisted across successive survey waves during Thailand’s demographic and policy transitions [18, 19]. Third, although international evidence suggests that factors such as socioeconomic status, education, rural residence, functional limitation, physical dependency, and access barriers substantially influence dental care utilization among older adults [9], these broader ageing-related determinants remain insufficiently examined within the Thai context.

This study aimed to address these gaps by examining pattern and determinants of inequality in dental care utilization in the past 12 months among Thai older adults from 2009 to 2021, focusing on socioeconomic and geographic disparities. In particular, it investigates modifiable factors such as health insurance coverage and rural accessibility that may drive these disparities. The study also seeks to generate policy-relevant insights to support equitable ageing-in-place within Thailand’s UHC framework. As one of the few LMICs with near-universal health coverage, Thailand’s experience may offer transferable lessons for other countries balancing population ageing with the goal of oral health equity.

Methodology

Data source and sampling design

The data for this study were obtained from the nationally representative Health and Welfare Survey (HWS), conducted in 2009, 2015, and 2021 by the National Statistical Office (NSO) of Thailand. The HWS employed a two-stage stratified sampling design with survey weights to ensure representativeness of the national population. The sampling frame included non-institutional households residing in both municipal (urban) and non-municipal (rural) areas across all provinces.

In the first stage, the primary sampling units consisted of blocks in municipal areas and villages in non-municipal areas, selected using probability proportional to the number of households in each block or village. In the second stage, households were randomly selected using systematic random sampling.

Individuals aged 60 years and older were eligible for inclusion. Across all survey years, approximately 75% of respondents provided self-reported information; the remainder required assistance from a household proxy due to hearing impairment, cognitive limitation, illness, or physical disability. Proxy-assisted responses were retained in the main analysis. The final analytic sample comprised 11,402, 26,566, and 15,857 respondents from the HWS 2009, 2015, and 2021, respectively (Fig. 1). Data were collected through structured face-to-face interviews using standardized household and individual questionnaires.

Fig. 1.

Sample selection flowchart for older adults included from the Health and Welfare Survey (HWS) Thailand 2009, 2015, and 2021

Dependent variable

Dental care utilization was assessed using the survey question: “Have you utilized dental care at any facility during the past 12 months preceding the survey?” Responses were coded as a binary variable (1 = utilized; 0 = did not utilize). The facility encompassed dental care received at public primary care units (sub-district health-promoting hospitals with dental health personnel or community hospitals), general and tertiary care hospitals, and private facilities. This broad definition ensures capture of both public and private sector utilization [17, 18].

Independent Variables were classified according to the Andersen Behavioral Model described as follows [18–21].

Geographic characteristics: regional divisions (North, Northeast, Central, South, and Bangkok) and area of residence (urban or rural).

Demographic characteristics: age group (60–69, 70–79, ≥ 80 years), sex (male, female), and marital status (married, single, widowed/divorced/separated). Household characteristics included headship status and living arrangement (i.e., living alone or not).

Socioeconomic characteristics: educational attainment (no formal education, up to primary, secondary/vocational, tertiary), and household economic status based on an asset index constructed via Principal Component Analysis of household durable goods. This includes television, mobile phone, computer, tablet, refrigerator, microwave, washing machine, oven, air conditioner, car, motorbike, and truck [14]. The first principal component score was used as the composite wealth indicator, and households were ranked into quartiles within each survey wave, representing relative socioeconomic position (Quartile 1 = poorest; Quartile 4 = wealthiest) [19].

• i.Health-related characteristics: These included predisposing and enabling factors:
• ii.Health insurance entitlement, was classified as: [1] the Universal Coverage Scheme (UCS), covering the majority of Thai citizens not eligible for other public schemes; [2] the Civil Servant Medical Benefit Scheme (CSMBS), covering current and retired government employees and their immediate dependents; and [3] those not entitled to either public-subsidized scheme. Older adults with supplementary private insurance were additionally flagged [18, 19].
• iii.Chronic illness, classified as none, one disease or two or more diseases (comorbidity), based on self-reported physician diagnosis.
• iv.Disability status, defined as either functional disability (difficulty performing activities of daily living) or mental disability (presence of moderate to severe anxiety or depression), based on self-report using standardized screening items.
• v.Healthcare utilization, including participation in health promotion and disease prevention activities, and hospitalizations in the past 12 months.

Measurement of key variables across survey waves

Mental disability was derived from self-reported depression or anxiety using Likert-scale measures that differed across survey waves (3-point in HWS 2009; 5-point in HWS 2015 and 2021). Responses were harmonized into a binary indicator, with any reported symptoms classified as mental disability. For respondents without self-reported symptoms, physician-diagnosed conditions (depression, schizophrenia, or psychotic disorders) were used as proxy indicators. Differences in variable categorizations where mental disorders were grouped in HWS 2009 but disaggregated in later waves may introduce misclassification and affect comparability across surveys.

Functional disability was assessed across three domains: mobility, self-care, and daily activities. Although conceptually consistent, response scales differed across survey waves (3-point in 2009; 5-point in 2015 and 2021). Responses were dichotomized to indicate moderate-to-severe limitations, and a composite binary variable was defined for individuals reporting limitations across all domains.

For those without self-reported limitations, physician-diagnosed conditions (e.g., cerebrovascular disease, stroke-related paralysis or paresis, dementia, musculoskeletal disorders) were used as proxy indicators. Broader disease groupings in HWS 2009 may reduce specificity and introduce measurement error. In addition, for the 5-point scale (HWS 2015 and 2021), responses indicating moderate or severe symptoms (scores ≥ 3) were classified as functional or mental disability, while mild or no symptoms were classified as absent. For the 3-point scale (HWS 2009), any reported symptom (score ≥ 1) was classified as functional or mental disability.

Health promotion and disease prevention service utilization was measured using comparable questions across survey waves but with different reference periods (past month in HWS 2009 versus past 12 months in HWS 2015 and 2021). To enhance comparability, analyses were restricted to consistently defined services: non-communicable disease screening, routine annual health examinations, immunization uptake, and participation in health behavior modification programs. Differences in recall periods and service categorization should be considered when interpreting temporal comparisons across waves.

Data analysis

The HWS employed a stratified two-stage sampling design with enumeration areas selected as primary sampling units using probability proportional to size sampling and households selected systematically at the second stage. Sampling weights supplied by the NSO accounted for unequal probabilities of selection arising from the multistage sampling design and were calibrated (post-stratified) using official Thai population estimates for each survey year (2009, 2015, and 2021) according to age–sex groups, region, and municipal/non-municipal residence [14]. Nonresponse adjustment was incorporated within the NSO’s standard ratio estimation procedure, as documented in the HWS technical report; the specific nonresponse weighting approach was implemented by the NSO prior to public data release, and the authors applied the supplied weights without modification [14].

All analyses were conducted using Stata version 14.0 (StataCorp LLC, College Station, TX, USA). The complex survey design was specified using the svyset command, and the svy: prefix was applied to all descriptive and regression analyses to account for weighting, clustering, and stratification.

Weighted prevalence estimates with 95% confidence intervals (CIs) were calculated for dental care utilization according to determinants. Multivariable logistic regression was used to estimate adjusted odds ratios (aORs) and 95% CIs for factors associated with dental care utilization. Multivariable models were fitted separately for each survey year to examine consistency in associated determinants across survey waves. All models adjusted for age group, sex, marital status, household headship, living arrangement, educational attainment, household asset quartile, region and area of residence, health insurance entitlement, supplementary private insurance coverage. chronic illness, disability status, health promotion and disease prevention service utilization, and hospitalization. A sensitivity analysis excluding proxy-assisted respondents was conducted to assess the robustness of findings. Statistical significance was defined as a two-sided p value < 0.05.

Ethical considerations

This study involved secondary analysis of de-identified microdata from the Health and Welfare Survey (HWS), an official national survey conducted by the National Statistical Office (NSO) of Thailand under the Statistics Act B.E. 2550. The HWS is conducted under established statutory data governance, confidentiality, and personal data protection frameworks, including the NSO. De-identified microdata were officially obtained through formal written authorization from the NSO for research purposes (NSO Authorization Letter, Ref. No. DE 0504/2332; Data Use Agreement Contract No. 159/2567). As the present study used de-identified secondary data and involved no direct contact with human participants, informed consent was not required. In addition, the study was approved by the Research Ethics Committee of the Faculty of Dentistry, Chulalongkorn University (Approval No. HREC-DCU 2025124). All procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki.

Results

Sample characteristics

The analytic sample included 53,825 individual older adults across three waves of the Health and Welfare Survey (HWS 2009, 2015, and 2021) (Table 1). The majority of participants were “early older adults” (aged 60–69), comprising 57.9% in 2021. Most respondents were female (56.6%) and married (61.6%). Regarding socioeconomic status, 74.9% had attained only primary education in 2021, although there was a modest increase in the proportion with tertiary education, from 4.5% in 2009 to 7.3% in 2021. Wealth distribution also improved over time; the proportion of individuals in the poorest quartile declined from 35.4% in 2009 to 25.3% in 2021. In terms of health status, the prevalence of comorbidity, defined as having two or more chronic conditions, increased markedly from 16.2% to 31.0% over the study period.

Table 1.

Characteristics of the Thai older adults in HWS 2009, 2015, and 2021

Determinants (= 1 if yes, = 0 if otherwise)	Proportionate distribution of samples (%)
	2009 (N = 11,402)	2015 (N = 26,566)	2021 (N = 15,857)
1) Socioeconomic characteristics
Asset index of household possession:
1st Quartile	35.4	26.9	25.3
2nd Quartile	15.6	26.6	33.4
3rd Quartile	22.6	22.6	15.8
4th Quartile	26.4	23.9	25.5
Educational level attainment:
Not attended formal education	10.4	9.7	7.8
Up to Primary	73.6	77.5	74.9
Secondary/ vocational	11.6	7.9	9.9
Tertiary or higher	4.4	4.9	7.4
2)Geographic characteristics
Region and area of residence:
Bangkok	4.1	3.6	4.0
Central/ Urban	17.3	14.4	14.3
Central /Rural	14.1	14.1	13.4
North/ Urban	14.7	13.9	14.3
North/ Rural	10.1	11.5	11.7
Northeast/ Urban	13.9	14.8	15.2
Northeast/ Rural	10.9	13.5	13.1
South/ Urban	7.8	7.6	7.5
South/ Rural	7.1	6.6	6.5
3)Demographic characteristics
Age group: (years)
Early older adults(60–69)	52.4	56.3	57.9
Middle older adults(70–79)	33.5	29.8	28.0
Late older adults(80 and over)	14.1	13.9	14.1
Sex:
Male	42.6	43.7	43.5
Female	57.4	56.3	56.5
Marital status:
Single	3.9	3.9	5.2
Married	60.4	62.6	61.6
Widowed/ divorced/ separated	35.7	33.9	33.2
being as head of household	58.5	61.8	61.9
living alone (at present)	8.6	11.1	13.7
4)Health and welfare status
Health insurance entitlement
a) UCS	74.5	79.3	81.6
b) CSMBS	21.6	18.3	17.8
c) Not entitled to public subsidized insurance	3.8	2.5	1.9
Having supplementary private insurance *	1.9	4.5	3.8
Whether having chronic illness
a) None	45.1	43.9	41.1
b)1 disease	38.8	33.7	27.9
c)2 or more diseases	16.2	22.4	31.0
disability
functional	25.6	6.4	9.4
mental	13.9	2.8	3.9
Health care utilization
a) Received health promotion and diseases prevention	5.0	17.7	25.8
b) Was hospitalized (from either illness or accident)	10.1	8.2	5.4

Not entitled to public subsidized insurance’ refers to individuals not covered by either UCS or CSMBS, in addition to

*Older adults covered by a public health insurance scheme who additionally held private insurance were classified as having supplementary private health insurance

Dental care utilization across survey waves

Overall, dental care utilization among the older adults declined from 9.1% in 2009 to 7.2% in 2021, despite near-universal health insurance coverage (Table 2). Utilization patterns revealed stark socioeconomic inequalities. In 2021, 11.3% of individuals in Quartile 4 (wealthiest) reported dental care use in the past 12 months, whereas only 5.0% of those in Quartile 1 (poorest) did so. Education was also a strong determinant: 19.4% of tertiary-educated older adults reported utilization, compared to just 3.8% of those with no formal education. Geographic disparities persisted, with Bangkok residents reporting the highest utilization (17.3%) and individuals in the rural Northeast the lowest (4.9%). Insurance scheme was also associated with differential utilization, with CSMBS beneficiaries reporting higher rates (11.8%) than UCS beneficiaries (5.9%).

Table 2.

Distribution of dental care utilization during the past 12 months preceding the survey among Thai older adults in HWS 2009, 2015, and 2021

Determinants (= 1 if yes, = 0 if otherwise)	Dental care utilization (% with 95% confidence interval)
	2009 (N = 11,402)	2015 (N = 26,566)	2021 (N = 15,857)
1) Socioeconomic characteristics
Asset index of household possession:
Quartile1	6.5 (5.7, 7.2)	4.7 (4.2, 5.2)	5.0 (4.3, 5.7)
Quartile2	6.7 (5.5, 7.8)	4.9 (4.4, 5.4)	5.3 (4.7, 5.9)
Quartile3	8.4 (7.3, 9.4)	5.9 (5.3, 6.5)	7.5 (6.4, 8.5)
Quartile4	14.0 (12.8, 15.3)	10.1 (9.4, 10.9)	11.3 (10.3, 12.3)
Educational level attainment
Not attended formal education	5.1 (4.7, 6.2)	3.2 (2.5, 3.8)	3.8 (2.7, 4.9)
Up to Primary	8.4 (7.8, 8.9)	5.5 (5.2, 5.8)	5.6 (5.2, 6.0)
Secondary/ vocational	17.1 (15.1, 19.2)	11.9 (10.5, 13.2)	11.4 (10.3, 13.5)
Tertiary or higher	11.4 (9.7, 18.6)	17.2 (15.1, 19.2)	19.4 (17.1, 21.6)
2)Geographic characteristics
Region and area of residence
Bangkok	16.1 (12.8, 19.5)	14.7 (12.5, 16.9)	17.3 (14.3, 20.2)
Central/ Urban	10.1 (8.8, 11.5)	6.4 (5.7, 7.2)	7.3 (6.1, 8.3)
Central /Rural	7.3 (6.0, 8.6)	5.4 (4.7, 6.1)	6.0 (4.9, 6.9)
North/ Urban	8.1 (6.8, 9.4)	7.2 (6.3, 8.0)	8.7 (7.5, 9.9)
North/ Rural	6.9 (5.5, 8.4)	6.5 (5.6, 7.4)	6.4 (5.3, 7.6)
Northeast/ Urban	9.9 (8.5, 11.4)	5.6 (4.9, 6.4)	5.5 (4.5, 6.4)
Northeast/ Rural	7.1 (5.7, 8.6)	5.0 (4.3, 5.7)	4.9 (4.6, 5.8)
South/ Urban	10.8 (8.7, 12.8)	6.8 (5.8, 7.9)	8.2 (6.7, 9.8)
South/ Rural	8.5 (6.6, 10.4)	5.2 (4.2, 6.3)	7.3 (5.7, 8.9)
3)Demographic characteristics
Age group (years)
Early older adults(60–69)	10.7 (9.9, 11.5)	7.5 (7.0, 7.9)	8.4 (7.8, 8.9)
Middle older adults(70–79)	8.1 (7.2, 8.9)	5.7 (5.2, 6.2)	6.1 (5.4, 6.8)
Late older adults(80 and over)	4.3 (3.3, 5.3)	3.2 (2.6, 3.8)	3.8 (3.0, 4.6)
Sex
Male	8.6 (7.4, 9.4)	6.0 (5.6, 6.5)	6.9 (6.3, 7.5)
Female	9.2 (8.5, 9.9)	6.6 (6.2, 6.9)	7.3 (6.7, 7.8)
Marital status
Single	9.5 (6.7, 12.2)	7.2 (5.6, 8.7)	9.9 (7.8, 11.9)
Married	9.8 (9.1, 10.5)	6.9 (6.6, 7.4)	7.7 (7.2, 8.3)
Widowed/ divorced/ separated	7.5 (6.7, 8.3)	5.0 (4.6, 5.5)	5.5 (4.8, 6.1)
being as head of household	9.5 (8.8, 10.2)	6.6 (6.2, 6.9)	7.3 (6.7, 7.8)
living alone (at present)	9.8 (7.9, 11.7)	7.1 (6.2, 8.0)	8.2 (7.0, 9.3)
4)Health and welfare status
Health insurance entitlement
a) UCS	7.4 (6.8, 8.0)	5.2 (4.9, 5.5)	5.9 (5.5, 6.3)
b) CSMBS	13.9 (12.6, 15.3)	10.7 (9.8, 11.5)	11.8 (10.6, 12.9)
c) Not entitled to public subsidized insurance	10.5 (7.6, 13.4)	10.8 (8.5, 13.2)	12.5 (8.8, 16.9)
Having supplementary private insurance	17.2 (12.1, 22.3)	9.6 (8.0, 11.3)	17.0 (14.0, 20.0)
Having chronic illness
a) None	7.9 (7.1, 8.6)	5.6 (5.1, 6.0)	6.2 (5.6, 6.7)
b)1 disease	9.2 (8.4, 10.1)	6.1 (5.8, 6.8)	7.1 (6.4, 7.9)
c)2 or more diseases	11.2 (9.8, 12.7)	7.9 (7.2, 8.6)	8.3 (7.5, 9.0)
Disability
functional	10.1 (9.0, 11.2)	5.1 (4.0, 6.3)	7.4 (6.1, 8.7)
mental	10.5 (9.0, 12.0)	9.6 (5.3, 13.8)	8.2 (1.1, 15.3)
Health care utilization
a) Received health promotion and diseases prevention	18.3 (18.1, 21.5)	12.6 (11.6, 13.5)	11.2 (10.3, 12.2)
b) Was hospitalized (from either illness or accident)	10.7 (8.9, 12.5)	6.9 (3.5, 10.3)	8.1 (6.3, 9.9)

Determinants of dental care utilization

Multivariable logistic regression analyses showed that dental care utilization was significantly associated with socioeconomic status, educational attainment, geographic residence, and health insurance scheme (Table 3). After adjustment for all covariates, respondents in the highest household asset quartile had significantly greater odds of utilizing dental services compared with those in the lowest quartile (aOR = 2.4; 95% CI : 2.0–2.9). Educational attainment demonstrated a strong gradient, with respondents with tertiary education showing substantially higher odds of utilization relative to those with no formal education (aOR = 6.0; 95% CI: 4.3–8.3).

Table 3.

Adjusted odds ratio (aOR) with 95% confidence interval of dental care utilization during the past 12 months preceding the survey among Thai older adults in HWS 2009, 2015, and 2021

Determinants(=1 if yes, =0 if otherwise)	adjusted OR (95% confidence interval)
	2009	2015	2021
1) Socioeconomic characteristics
Asset index of household possession:
Quartile1	reference	reference	reference
Quartile2	1.0 (0.8, 1.3)	1.0 (0.9, 1.2)	1.1 (0.9, 1.3)
Quartile3	1.2 (1.0, 1.5)	1.1 (1.0, 1.3)	1.5 (1.2, 1.9)
Quartile4	1.9 (1.6, 2.3)	1.6 (1.3, 1.8)	2.4 (2.0, 2.9)
Educational level attainment
Not attended formal education	reference	reference	reference
Up to Primary	1.6 (1.3, 2.0)	1.6 (1.3, 2.0)	1.5 (1.1, 2.0)
Secondary/ vocational	2.7 (2.1, 3.6)	3.2 (2.4, 4.1)	3.4 (2.4, 4.7)
Tertiary or higher	1.7 (0.7, 4.1)	4.5 (3.4, 5.9)	6.0 (4.3, 8.3)
2)Geographic characteristics
Region and area of residence
Bangkok	reference	reference	reference
Central/ Urban	0.6 (0.4, 0.8)	0.4 (0.3, 0.5)	0.4 (0.3, 0.5)
Central /Rural	0.4 (0.3, 0.6)	0.3 (0.3, 0.4)	0.3 (0.2, 0.4)
North/ Urban	0.5 (0.4, 0.6)	0.5 (0.4, 0.6)	0.5 (0.4, 0.6)
North/ Rural	0.4 (0.3, 0.5)	0.4 (0.3, 0.5)	0.3 (0.3, 0.4)
Northeast/ Urban	0.6 (0.4, 0.8)	0.4 (0.3, 0.4)	0.3 (0.2, 0.4)
Northeast/ Rural	0.4 (0.3, 0.6)	0.3 (0.2, 0.4)	0.3 (0.2, 0.4)
South/ Urban	0.6 (0.5, 0.9)	0.4 (0.3, 0.4)	0.4 (0.3, 0.6)
South/ Rural	0.5 (0.3, 0.7)	0.3 (0.2, 0.4)	0.4 (0.3, 0.5)
3)Demographic characteristics
Age group(years)
Early older adults(60-69)	2.4 (1.8, 3.1)	2.1 (1.8, 2.6)	2.1 (1.6, 2.6)
Middle older adults(70-79)	1.8 (1.4, 2.4)	1.7 (1.4, 2.1)	1.5 (1.2, 2.0)
Late older adults(80 and over)	reference	reference	reference
Sex
Male	reference	reference	reference
Female	1.4 (1.2, 1.6)	1.4 (1.2, 1.6)	1.1 (1.0, 1.3)
Marital status
Single	1.1 (0.8, 1.6)	1.4 (1.1, 1.8)	1.7 (1.3, 2.2)
Married	1.4 (1.2, 1.7)	1.5 (1.3, 1.7)	1.3 (1.1, 1.5)
Widowed/ divorced/ separated	reference	reference	reference
being as head of household	1.4 (1.2, 1.6)	1.4 (1.2, 1.6)	1.0 (0.9, 1.2)
living alone (at present)	1.1 (0.9, 1.5)	1.2 (1.0, 1.3)	1.2 (1.0, 1.4)
4)Health and welfare status
Health insurance entitlement
a) UCS	Reference	reference	reference
b) CSMBS	2.0 (1.7, 2.3)	2.1 (1.9, 2.4)	2.0 (1.8, 2.3)
c) Not entitled to public subsidized insurance	1.6 (1.1, 2.2)	2.3 (1.7, 2.9)	2.4 (1.7, 3.3)
Having supplementary private insurance	2.1 (1.5, 3.1)	1.4 (1.2, 1.7)	2.6 (2.0, 3.1)
Whether having chronic illness
a) None	reference	reference	reference
b)1 disease	1.1 (1.0, 1.3)	1.1 (0.9, 1.2)	1.2 (1.0, 1.4)
c)2 or more diseases	1.3 (1.1, 1.5)	1.3 (1.2, 1.5)	1.4 (1.2, 1.6)
Disability
functional	1.1 (0.9, 1.3)	0.7 (0.5, 0.9)	0.7 (0.3, 1.6)
mental	1.2 (1.0, 1.5)	1.6 (1.0, 2.7)	1.1 (0.5, 2.9)
Health care utilization
a) Received health promotion and diseases prevention	2.3 (1.8, 2.9)	2.7 (2.4, 3.0)	2.1 (1.9, 2.4)
b) Was hospitalized (from either illness or accident)	1.1 (0.9, 1.3)	1.0 (0.6, 1.7)	1.1 (0.9, 1.4)

Geographic disparities were also observed. Compared with Bangkok residents, older adults living in the rural Northeast had significantly lower odds of dental care utilization (aOR = 0.3; 95% CI: 0.2–0.3). In addition, respondents covered by the Civil Servant Medical Benefit Scheme (CSMBS) had higher odds of utilizing dental services than those enrolled in the Universal Coverage Scheme (UCS) (aOR = 2.0; 95% CI: 1.8–2.3). The direction and statistical significance of the associations for education, household wealth, geographic region, and insurance scheme were broadly consistent across the 2009, 2015, and 2021 survey waves (Table 3). A sensitivity analysis excluding proxy-assisted older adults yielded consistent results with those of the main analysis; the direction and statistical significance of all primary associations were unchanged.

Discussion

This study demonstrated persistent socioeconomic and geographic inequalities in dental care utilization among Thai older adults despite Thailand’s UHC system. Across the study period, older adults with lower socioeconomic status, lower educational attainment, and rural residence consistently showed lower utilization of dental services. These findings are consistent with previous evidence from other countries indicating that financial coverage alone may be insufficient to ensure equitable access to oral healthcare [18, 19, 22–28].

A central finding is that socioeconomic status remains the most powerful predictor of dental care utilization. Consistent with prior studies from LMICs and high-income countries, both wealth and education independently predicted access to care. The six-fold disparity in utilization between the tertiary-educated and those with no formal education suggests that health literacy, prior exposure to preventive care, and confidence in navigating health systems may play a key role. Educated individuals may also place a higher perceived value on oral health and be more proactive in seeking dental services. Behavioral and cultural barriers may further exacerbate these inequalities; for example, dental fear, low perceived need, or a traditional reliance on self-care may deter less-educated or rural older adults from utilizing services, even when insured. This may reflect differences in oral health literacy, perceived need, healthcare navigation, and health-seeking behaviors [8, 17, 18, 29–35].

Significant geographic disparities were also observed, with older adults living outside Bangkok, particularly in rural regions, showing lower utilization of dental services. Although Thailand’s UHC system reduces financial barriers, geographic accessibility remains an important determinant of oral healthcare utilization. Previous evidence suggests that travel distance, transportation barriers, and uneven distribution of dental services may substantially affect healthcare access among older adults, especially those with reduced mobility or functional dependency [36]. These findings indicate that physical accessibility may remain a major barrier despite insurance coverage [37, 38].

Insurance scheme differences were also associated with dental care utilization. Older adults covered by the Civil Servant Medical Benefit Scheme (CSMBS) demonstrated higher utilization than those enrolled in the Universal Coverage Scheme (UCS), suggesting persistent inequities in healthcare access across insurance arrangements. While Thailand’s UHC system has substantially improved financial protection, variations in benefit packages, service availability, and care pathways may continue to influence dental service utilization [39].

The findings should also be interpreted within the broader context of population ageing. Older adults may experience additional barriers to oral healthcare utilization, including functional decline, cognitive impairment, frailty, and increasing dependence on caregivers. Evidence from geriatric oral health research suggests that mobility limitations, transportation difficulties, and lack of caregiver support may substantially reduce access to dental services among dependent older adults [5, 30, 40]. Consequently, oral healthcare inequalities among older adults should be interpreted beyond financial access alone and within a broader ageing and functional-health context [5, 41–43]. Interestingly, a previous study showed that unmet need for outpatient, inpatient, and dental care remained relatively low among Thai older adults [44]. However, inequalities in dental care utilization appeared substantially more pronounced than those observed for general healthcare services. As shown in Supplementary Table A, the insurance scheme differential was markedly larger for dental care (CSMBS 11.8% vs. UCS 5.9%) than for outpatient and inpatient care, suggesting that barriers to dental care extend beyond insurance entitlement alone. In addition, whereas outpatient and inpatient utilization increased with age, dental care utilization declined among the oldest age groups, indicating that older adults with greater functional dependency may face compounded barriers to accessing oral healthcare.

From a policy perspective, the findings in this study support the need for oral healthcare strategies that extend beyond financial coverage alone. In line with the WHO Global Oral Health Action Plan 2023–2030 and other policy recommendations, policies should incorporate ageing-sensitive and community-based approaches to improve access among vulnerable older adults. The persistent education gradient, whereby older adults with tertiary education had sixfold greater odds of dental care utilization than those with no formal education, underscores the need for targeted oral health literacy interventions for low-educated and socioeconomically disadvantaged older adults. Likewise, the substantially lower utilization among older adults residing in the rural Northeast compared with Bangkok residents highlights the importance of expanding mobile dental services, outreach programs and community-based oral health care delivery to reduce geographic barriers. Because socioeconomically disadvantaged older adults are more likely to delay dental care until symptoms worsen, integrating annual oral health screening and preventive dental assessments into existing primary care and chronic disease management services for older adults may improve early access to care. Finally, the persistent difference in dental care utilization between beneficiaries of the CSMBS and the UCS suggests that harmonizing dental benefit packages and improving service accessibility across insurance schemes may be necessary to achieve equitable oral healthcare access in ageing societies [11, 36, 37, 45, 46].

Several limitations should be acknowledged. First, dental care utilization was self-reported and may be subject to recall bias. Second, the repeated cross-sectional design limits causal inference and prevents assessment of individual-level changes over time. Third, some variables differed slightly across survey waves, particularly regarding mental and functional disability assessment, which may have introduced measurement inconsistencies. Finally, the study could not distinguish between preventive and treatment-oriented dental visits, which may be differently influenced by socioeconomic factors. Future longitudinal and mixed-methods studies are needed to better understand ageing-related barriers, healthcare navigation, and the long-term effects of inequalities in dental care utilization among older adults.

Conclusion

Despite Thailand’s near-universal health coverage system, substantial socioeconomic and geographic inequalities in dental care utilization persist among older adults. Lower socioeconomic status, rural residence, and insurance-related differences remained important determinants of reduced utilization. These findings suggest that achieving equitable oral healthcare in ageing societies requires policy approaches that address not only financial barriers but also geographic accessibility, functional limitations, and ageing-related dependency. Strengthening geriatric-sensitive and community-based oral healthcare delivery may help reduce persistent inequalities among older adults.

Abbreviations

WHO

World Health Organization

UHC

Universal Health Coverage

LMIC

Low-and middle-income countries

UCS

Universal Coverage Scheme

CSMBS

Civil Servant Medical Benefit Scheme

NSO

National Statistical Office

HWS

Health and Welfare Survey

PCA

Principal Component Analysis

aOR

adjusted Odds Ratio

95% CI

95% confidence interval

Authors’ contributions

T.S. and N.T.L.T. contributed to the study conceptualization, data preparation, statistical analysis, interpretation of findings, and preparation of the initial manuscript draft. K.F.S. and P.P.P. contributed to the development of the manuscript and provided critical revisions for intellectual content. All authors reviewed and approved the final manuscript.

Funding

None declared.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The study utilized secondary data from the Thailand Health and Welfare Survey (HWS), conducted by National Statistical Office (NSO) of Thailand, Ministry of Digital Economy and Society (MDES). Ethical clearance for the original data collection was obtained from the NSO. As this study involved only the analysis of anonymized, aggregated secondary data, the requirement for individual informed consent was waived by the Research Ethics Committee of the Faculty of Dentistry, Chulalongkorn University (HREC-DCU 2025124). All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable. The manuscript does not contain any individual person’s data in any form (including images, videos, or individual details).

Competing interests

The authors declare no competing interests.

Clinical trial Number

Not applicable.