Abstract
Background
Oral health is integral to overall well-being and Universal Health Coverage (UHC), yet disparities in dental service utilization (DSU) persist in low- and middle-income countries (LMICs). Socioeconomic status (SES) and educational inequalities exacerbate barriers to accessing essential dental services, limiting UHC progress. Therefore, this study assessed absolute and relative socioeconomic and education-based inequalities in DSU in Bangladesh, Bhutan, and Nepal and identified demographic and socioeconomic determinants.
Methods
Data from the most recent WHO STEPwise approach to surveillance (STEPS) surveys in Bangladesh (2017–18), Bhutan (2019), and Nepal (2019) were analyzed. Inequalities in DSU were assessed using the slope index of inequality (SII), relative index of inequality (RII), and relative concentration index (RCI). The determinants of DSU were assessed using mixed-effects binary logistic regression models.
Results
Ever DSU was highest in Bhutan (48.8%), followed by Bangladesh (29.1%) and Nepal (5.6%). Socioeconomic inequalities in DSU were significant in Bhutan, with higher utilization concentrated among the wealthiest groups (SII: 33.9, 95% confidence interval [CI]: 27.8–40.1 for ever DSU; RII: 2.11, 95% CI: 1.86–2.39), while education-based disparities favored individuals with higher education (RII: 1.85, 95% CI: 1.62–2.13). In Bangladesh, education-based inequalities in DSU were evident only in urban areas (SII: 17.3, 95% CI: 8.2–26.5; RII: 1.69, 95% CI: 1.38–2.06). In Nepal, DSU was uniformly low across all groups, with minimal or even reversed socioeconomic and educational disparities. Independent determinants of DSU, identified through adjusted mixed-effects logistic regression, included older age, higher education, and urban residence in Bangladesh; higher education and SES in Bhutan; and female sex in Nepal.
Conclusions
DSU was limited across Bangladesh, Bhutan, and Nepal, with the lowest rates observed in Nepal. Substantial socioeconomic and educational inequalities in DSU were evident in Bhutan and Bangladesh. These findings underscore the need for UHC-aligned, equity-focused reforms, including national oral health policies, targeted outreach to disadvantaged groups, and expanded access to preventive services.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-24098-w.
Keywords: Dental service utilization, Education-based inequality, Low- and Middle-Income countries, Socioeconomic inequality, Universal health coverage
Background
Oral health is integral to overall well-being and a critical component of Universal Health Coverage (UHC), yet it remains largely neglected in public health agendas, particularly in low- and middle-income countries (LMICs), contributing to substantial disparities in access and outcomes [1–5]. Oral diseases are among the most prevalent and preventable non-communicable conditions globally, affecting approximately 3.5 billion people with issues such as untreated dental caries, periodontal disease, tooth loss, and oral cancers, disproportionately impacting LMICs [6]. In South Asia, these challenges are further intensified by limited healthcare infrastructure, financial barriers, and workforce shortages, collectively resulting in suboptimal oral health and hindering UHC progress [6].
In South Asian countries, including Bangladesh, Bhutan, and Nepal, socioeconomic disparities in access to healthcare are prevalent [7], affecting both dental service utilization (DSU) and broader dental healthcare services [8]. In countries like Bangladesh, Bhutan, and Nepal, over 50% of the population resides in rural areas with limited access to dental facilities [9, 10]. Although dental public health programs exist in all three countries, access to publicly funded dental services is limited, particularly in rural and underserved areas [11–13]. Expenditures for oral healthcare in these settings are largely out-of-pocket, creating major barriers for disadvantaged populations and often leading to unmet dental care needs [11–13]. Bangladesh and Nepal rely heavily on private-sector dental services [11], whereas Bhutan offers publicly funded care through its government-run system [14]. Their shared regional and economic contexts as South Asian LMICs, coupled with distinct healthcare structures and oral health policies, make them well-suited for a comparative analysis.
A systematic review and meta-analysis revealed that DSU is significantly influenced by social, economic, ethnic, and educational factors, underscoring widespread inequalities in access to oral healthcare [15]. While socio-demographic determinants of DSU have been explored, most studies focus on high-income, non-Asian countries [15–18], leaving notable gaps in evidence from LMICs. Despite the substantial oral disease burden in South Asia, where 28.7% of adults have untreated dental caries and 20.8% suffer from severe periodontitis [19], comparative research on DSU inequalities across socioeconomic and educational groups in these countries remains limited.
Recent studies have investigated DSU in these countries; however, key gaps persist. For example, Gupta et al. [20] examined prevalence and socio-demographic factors, while Pengpid and Peltzer [21] identified DSU predictors in Bangladesh. Yet, neither quantified relative nor absolute socioeconomic and educational inequalities in DSU. Addressing these gaps is essential for informing equity-driven policies and tracking progress toward UHC. Furthermore, measuring DSU disparities can guide targeted interventions, such as redistributing resources, expanding preventive outreach, or reducing financial barriers to promote equitable access to dental healthcare. Thus, the primary objective of this study was to assess the extent of socioeconomic and education-based inequalities in DSU in Bangladesh, Bhutan, and Nepal by measuring both absolute and relative disparities. In addition, we aimed to identify determinants of DSU among adults in these countries.
Methods
Data source and study participants
Data from the most recent WHO STEPwise approach to surveillance (STEPS) conducted in Bangladesh (2017–18), Bhutan (2019), and Nepal (2019) were used. These nationally representative surveys aimed to provide updated data on the health indicators for non-communicable diseases in these countries. Detailed methodologies, including sampling strategy, sample size calculation, and data collection procedures, have been reported previously [22–24]. Briefly, these surveys used a multistage stratified cluster sampling method, stratifying populations into urban and rural areas with primary sampling units (PSUs) selected based on probability proportional to size. The sample sizes were proportionate to the population distribution within each stratum. From each selected PSU, a fixed number of households were systematically selected. Then, one adult member (aged 18–69 years) was randomly selected from each selected household. This resulted in 8,185, 5,575, and 5,593 participants in Bangladesh, Bhutan, and Nepal, respectively. After the initial exclusions already accounted for in the original STEPs datasets (e.g., ineligible or non-responding individuals), we further excluded participants with missing data on key covariates. Final analytical sample sizes were 8,143 (Bangladesh), 5,446 (Bhutan), and 5,240 (Nepal), as illustrated in supplementary figures S1–S3.
Outcome
DSU, the primary focus of this study, was defined using two outcome variables: “Never visited a dentist” and “Visited a dentist within the last 12 months.” Respondents were asked, “How long has it been since you last visited a dentist?” with six response options: “Never,” “Less than 6 months,” “6–12 months,” “More than 1 year but less than 2 years,” “2 or more years but less than 5 years,” and “5 or more years ago.” These responses were used to construct two dichotomous outcomes: (1) Ever DSU — participants who selected any option other than “Never” were classified as having ever utilized dental services; and (2) DSU within the last 12 months — those who responded “Less than 6 months” or “6–12 months” were considered to have utilized services within the past year. Additionally, we assessed the prevalence of ever DSU for preventive reasons, based on participants who reported a routine check-up as the main reason for their last dental visit.
Exposures and covariates
The main exposure of this study was the participants’ educational status and their household socioeconomic status (SES). Educational status was categorized into four harmonized levels: ‘no formal schooling’, ‘less than primary’, ‘primary completed’, and ‘secondary or higher’. To account for differences in educational systems across the three countries, we derived these standardized categories from country-specific education reporting systems (details are provided in Table S1). Household wealth quintile, as a household SES measure, was estimated based on household assets and housing characteristics using a principal component analysis and divided into quintiles (poorest, poorer, middle class, richer, and richest) [22–24]. Covariates were selected based on the Andersen behavioral model of healthcare utilization [25, 26], providing a comprehensive framework for understanding the factors affecting healthcare access at an individual level. This model facilitates systematic categorization of factors into predisposing, enabling, and need-based components, offering insights into the contribution of socio-demographic characteristics and healthcare access towards DSU disparities [17]. The predisposing factors included in the analysis were age, sex, marital status, and educational status. Enabling factors included in the analysis were household SES, and place of residence (urban/rural). Health behaviors, including tobacco use, brushing frequency, and toothpaste use, were also assessed. The need factors included in the analysis were self-reported chewing problems and speech difficulties.
Statistical analysis
Descriptive statistics were used to summarize the basic characteristics of study participants. Differences in proportions across groups were assessed using the chi-square test. To evaluate socioeconomic and education-based inequalities in DSU, we used three well-established measures: the slope index of inequality (SII), relative index of inequality (RII), and relative concentration index (RCI). These measures were chosen to capture both absolute (SII) and relative (RII, RCI) dimensions of inequality, allowing for a comprehensive understanding of disparities in access [27].
The SII is a regression-based absolute measure that quantifies the difference in DSU between the most and least advantaged subgroups, while accounting for subgroups. Values typically range from − 1 to + 1 and are multiplied by 100 for interpretation. A positive SII indicates greater DSU among the advantaged group, whereas a negative value reflects greater DSU among the disadvantaged.
The RCI is a relative measure that reflects the extent to which DSU is concentrated across the socioeconomic or educational spectrum. The RCI values range from − 1 to + 1 and are also multiplied by 100. A positive RCI indicates the concentration of DSU among more advantaged groups. The RII, another regression-based relative measure, represents the ratio of DSU between the most and least advantaged subgroups. An RII of 1 suggests no inequality; values above 1 indicate higher DSU among the advantages, while values below 1 indicate the opposite. The use of two relative measures (RII and RCI) alongside one absolute measure (SII) allows for robust and complementary insights, as each measure captures different aspects of inequality, both in magnitude and distribution [27]. All three measures were calculated for each country except SES-based inequality in Bangladesh, due to unavailable data. For all inequality measures, the most disadvantaged subgroup (i.e., individuals with no formal education or from the poorest wealth quintile) was used as the reference category.
Mixed-effects binary logistic regression models were used to identify independent determinants of ever DSU. Given the hierarchical structure of the STEPs surveys, where individuals are nested within clusters, a multilevel binary logistic regression model with a random intercept at the community level was employed. The Likelihood-ratio test was used to compare and select between fixed-effects and multilevel models. All models were adjusted for predisposing, enabling, and behavioral variables (tobacco use), and included random effects to account for the clustered survey design. Additionally, a sensitivity analysis was conducted to test model robustness by including behavioral and need factors including brushing frequency, toothpaste use, and oral health impacts (on speech and chewing). This sensitivity analysis was limited to Nepal and Bangladesh, as the Bhutan dataset lacked these variables, restricting comparability of behavioral and need-based determinants across all three countries. Sampling weights provided by the STEPs surveys were applied in all analyses to account for the complex survey design and to ensure nationally representative estimates. All analyses were conducted using STATA SE version 18.0; statistical significance was set at p < 0.05, and 95% confidence intervals (CI) were reported for all estimates.
Results
Participant characteristics
The participants’ characteristics are presented in Table 1. Across all three countries, the majority of participants were within the 18–49 years age group, with a higher proportion of female participants, particularly in Bhutan (62.3%) and Nepal (64.3%). Education levels varied, with a large share of participants reporting no formal education in Bhutan (52.5%) and Nepal (41.0%), while Bangladesh had the highest proportion with secondary education or above (24.0%). In Bhutan and Nepal, SES skewed toward the lower wealth quintiles. Rural residence was most common in Bhutan (62.9%), followed by Bangladesh (51.1%) and Nepal (37.9%). Tobacco use was reported by around 20% of participants in Bhutan and 30% in Nepal. While most individuals reported using toothpaste, brushing less than twice per day was common across all countries.
Table 1.
Prevalence of dental service utilization among adults aged 18–69 in Bangladesh, Bhutan, and Nepal
| Characteristics | Sample size (%)a | Dental service utilization (Weighted percentage with 95% CI) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Bangladesh | Bhutan | Nepal | |||||||
| Bangladesh | Bhutan | Nepal | Ever | Within one year | Ever | Within one year | Ever | Within one year | |
| Total | 8,143 | 5,373 | 5240 | 29.1 (27.5–30.8) | 13.2 (12.1–14.4) | 48.8 (45.9,51.8) | 16 (14.3,17.9) | 5.6 (4.5–6.9) | 0.1 (0.0-0.4) |
| Predisposing factors | |||||||||
| Age group | p < 0.001 | p = 0.016 | p = 0.188 | p = 0.251 | p < 0.001 | p = 0.005 | |||
| 18–34 years | 3,200 (39.3) | 2104 (39.2) | 1,960 (37.4) | 23.6 (21.5–25.8) | 11.6 (9.9–13.4) | 48.7 (45.0,52.4) | 16.9 (14.5,19.5) | 3.5 (2.6–4.8) | 0.2 (0.0-0.9) |
| 35–49 years | 3,153 (38.7) | 1896 (35.3) | 1,817 (34.7) | 34.2 (31.7–36.9) | 14.8 (13.1–16.7) | 47.1 (42.8,51.4) | 15.5 (13.3,18.1) | 7 (5.3–9.1) | 0.1 (0.0-0.5) |
| 50–69 years | 1,790 (22.0) | 1373 (25.6) | 1,463 (27.9) | 36.6 (32.7–40.6) | 15.4 (12.8–18.5) | 52.2 (48.1,56.2) | 14.2 (11.9,16.9) | 8.8 (6.6–11.7) | 0.2 (0.0-0.9) |
| Sex | p = 0.993 | p = 0.549 | p = 0.112 | p = 0.071 | p < 0.001 | p = 0.001 | |||
| Male | 3,786 (46.5) | 2028 (37.7) | 1,872 (35.7) | 29.1 (26.8–31.5) | 12.9 (11.4–14.5) | 47.4 (43.9,51.1) | 14.8 (12.7,17.2) | 3.7 (2.6–5.1) | 0.2 (0.1-1.0) |
| Female | 4,357 (53.5) | 3345 (62.3) | 3,368 (64.3) | 29.1 (27.0-31.3) | 13.6 (12.0-15.3) | 50.5 (47.1,53.8) | 17.4 (15.2,19.8) | 7.2 (5.8-9.0) | 0.1 (0.0-0.3) |
| Marital status | p = 0.009 | p = 0.783 | p = 0.021 | p = 0.062 | p = 0.004 | p = 0.096 | |||
| Single | 534 (6.6) | 595 (11.1) | 332 (6.3) | 22.4 (17.9–27.6) | 12 (8.9–16.0) | 55 (48.6,61.2) | 19.3 (15.1,24.3) | 2.2 (1.1–4.3) | 0.5 (0.1–3.6) |
| Married | 7,217 (88.6) | 4260 (79.3) | 4,629 (88.3) | 29.7 (28.0-31.4) | 13.4 (12.3–14.7) | 47.1 (44.0,50.2) | 15.3 (13.6,17.2) | 6 (4.8–7.5) | 0.1 (0.0-0.2) |
| Divorced/widowed | 392 (4.8) | 518 (9.6) | 279 (5.3) | 36.4 (28.0-45.8) | 13.1 (8.0-20.6) | 49.9 (43.7,56.1) | 13.2 (9.8,17.4) | 9 (4.7–16.7) | 0.1 (0.0–1.0) |
| Education level | p = 0.172 | p = 0.339 | p = 0.000 | p = 0.000 | p = 0.007 | p = 0.360 | |||
| No schooling | 2,464 (30.3) | 2822 (52.5) | 2,147 (41.0) | 27.1 (24.5–29.9) | 12 (10.2–14.1) | 41.1 (38.1,44.2) | 12.5 (10.9,14.3) | 7.6 (5.8–9.9) | 0.1 (0.0-0.2) |
| < Primary | 1,198 (14.7) | 504 (9.4) | 516 (9.8) | 27.2 (23.4–31.5) | 15 (11.9–18.9) | 41.2 (35.5,47.0) | 9.7 (6.7,13.9) | 5.7 (3.9–8.2) | 0.0 |
| Primary | 2,530 (31.1) | 294 (5.5) | 975 (18.6) | 31.3 (28.4–34.2) | 14.1 (12.0-16.5) | 47.6 (39.4,56.0) | 13.2 (9.4,18.2) | 4.6 (3.0–7.0) | 0.0 |
| ≥ Secondary | 1,951 (24.0) | 1753 (32.6) | 1,602 (30.6) | 29.8 (26.5–33.3) | 12.6 (10.4–15.2) | 58 (53.9,61.9) | 21.1 (18.2,24.3) | 4.3 (3.1-6.0) | 0.3 (0.1–1.1) |
| Enabling factors | |||||||||
| Household SES | p = 0.000 | p = 0.000 | p = 0.357 | p = 0.231 | |||||
| Poorest | - | 1090 (20.3) | 1,496 (28.5) | - | - | 35.5 (30.6,40.8) | 8.7 (6.8,11.0) | 7.3 (5.2–10.1) | 0.0 (0.0-0.2) |
| Poorer | - | 1077 (20) | 989 (18.9) | - | - | 44.1 (38.9,49.3) | 12.6 (9.9,15.9) | 5.2 (3.6–7.5) | 0.0 |
| Middle class | - | 1109 (20.6) | 900 (17.2) | - | - | 41.4 (36.6,46.4) | 13 (10.8,15.6) | 5.7 (3.9–8.3) | 0.0 (0.0-0.2) |
| Richer | - | 1070 (19.9) | 832 (15.9) | - | - | 54.5 (50.0,58.9) | 18.5 (15.2,22.3) | 4.9 (3.3–7.2) | 0.4 (0.0-2.5) |
| Richest | - | 1090 (20.3) | 1,023 (19.5) | - | - | 65.2 (61.6,68.6) | 25.2 (21.6,29.1) | 4.8 (3.3–7.1) | 0.3 (0.1-1.0) |
| Place of residence | p < 0.001 | p = 0.000 | p = 0.000 | p = 0.002 | p = 0.771 | p = 0.827 | |||
| Rural area | 4,158 (51.1) | 3382 (62.9) | 1,987 (37.9) | 27.4 (25.5–29.4) | 12.1 (10.8–13.5) | 43.4 (39.6,47.3) | 13.6 (11.6,15.9) | 5.4 (3.8–7.5) | 0.0 |
| Urban area | 3,985 (48.9) | 1991 (37.1) | 3,253 (62.1) | 35.0 (31.7–38.3) | 17.2 (15.0-19.7) | 56.6 (51.7,61.4) | 19.4 (16.5,22.7) | 5.7 (4.4–7.4) | 0.2 (0.1–0.7) |
| Health behaviors/practices | |||||||||
| Current tobacco use | p = 0.070 | p = 0.075 | p = 0.000 | p = 0.000 | p = 0.915 | p = 0.624 | |||
| No | 4,280 (52.6) | 4285 (79.8) | 3,678 (70.2) | 30.4 (28.1–32.7) | 14.2 (12.6–15.9) | 52.2 (49.1,55.3) | 17.6 (15.7,19.7) | 5.5 (4.4-7.0) | 0.1 (0.0-0.6) |
| Yes | 3,863 (47.4) | 1088 (20.2) | 1,562 (29.8) | 27.5 (25.3–29.8) | 12.0 (10.4–13.8) | 38.4 (34.1,42.9) | 11 (8.6,14.1) | 5.6 (4.2–7.6) | 0.1 (0.0-0.6) |
|
Brushing frequency (≥ twice/day) |
p = 0.470 | p = 0.636 | p = 0.112 | p = 0.018 | p = 0.652 | p = 0.353 | |||
| No | 4,045 (49.7) | 2135 (77.6) | 4,881 (93.1) | 28.6 (26.1–31.2) | 13.5 (11.9–15.3) | 0.6 (0.2,2.1) | 0.3 (0.1,1.0) | 5.5 (4.4–6.9) | 0.1 (0.0-0.5) |
| Yes | 4,098 (50.3) | 617 (22.4) | 359 (6.9) | 29.7 (27.8–31.8) | 12.9 (11.5–14.6) | 1.8 (0.4,8.1) | 1.3 (0.3,5.6) | 6.3 (3.7–10.7) | 0.3 (0.0-2.4) |
| Toothpaste uses | p = 0.001 | p = 0.002 | p < 0.001 | p = 0.002 | |||||
| No | 3,583 (44.0) | - | 583 (11.1) | 25.8 (23.5–28.2) | 11.2 (9.8–12.9) | - | - | 1.6 (0.7–3.5) | 0.0 |
| Yes | 4,560 (56.0) | - | 4,657 (88.9) | 31.6 (29.4–34.0) | 14.8 (13.2–16.5) | - | - | 6.2 (5.1–7.7) | 0.2 (0.1–0.5) |
| Need factors | |||||||||
| OHI speech problem | p < 0.001 | p = 0.000 | p < 0.001 | p < 0.001 | |||||
| No | 7,042 (86.5) | - | 5,024 (95.9) | 25.3 (23.7–27.0) | 9.5 (8.4–10.6) | - | - | 4.8 (3.8-6.0) | 0.1 (0.0-0.4) |
| Yes | 1,101 (13.5) | - | 216 (4.1) | 54.1 (49.1–59.1) | 38.2 (33.4–43.2) | - | - | 35.1 (25.6–45.9) | 0.6 (0.2-2.0) |
| OHI chewing problem | p < 0.001 | p = 0.000 | p < 0.001 | p < 0.001 | |||||
| No | 5,812 (71.4) | - | 4,678 (89.3) | 19.9 (18.2–21.8) | 5.0 (4.2-6.0) | - | - | 3.6 (2.8–4.6) | 0.1 (0.0-0.5) |
| Yes | 2,331 (28.6) | - | 562 (10.7) | 53.7 (50.3–57.0) | 35.3 (32.1–38.6) | - | - | 31.5 (24.9–38.9) | 0.6 (0.2–2.3) |
CI Confidence interval, SES Socioeconomic status, OHI Oral health impact
(-) indicates data not available in the WHO STEPS dataset
aUnweighted sample sizes are presented
Prevalence of dental service utilization
Overall, the ever utilization of dental services was highest in Bhutan (48.8%), followed by Bangladesh (29.1%), and Nepal (5.6%) (Table 1). Recent (within one year) utilization followed a similar pattern, with Bhutan at 16.0%, Bangladesh at 13.2%, and Nepal at Just 0.1%. Across countries, DSU was generally higher among older adults, especially those aged 50–69 years. However, in Nepal, utilization remained uniformly low across all age groups. Preventive utilization of dental services was low across all three countries (Table S2). Utilization was consistently higher among individuals with secondary or higher education, most notably in Bhutan (Table 1), where over half of highly educated adults reported ever visiting a dentist (58%). However, even among this group, preventive use remained low (Table S2). Socioeconomic differences in DSU were also apparent, especially in Bhutan (Table 1). Individuals from the richest households in Bhutan had substantially higher utilization (65.2%) compared to the poorest (35.5%), with similar trends across rural and urban areas. In contrast, Nepal showed minimal SES-based differences.
Figure 1 and Table S3 present the utilization of dental services by SES and educational level across national, rural, and urban populations. In Bhutan, DSU was consistently higher among wealthier and more educated individuals, with the gap especially pronounced in rural areas (e.g., 69% ever DSU among the richest vs. 34.8% among the poorest). In contrast, Nepal showed minimal or slightly reversed disparities, with marginally higher DSU among the poorest and least educated groups.
Fig. 1.
Ever utilization of dental service among adults in Bangladesh, Bhutan, and Nepal by socioeconomic status and educational level
Socioeconomic-based inequality in dental service utilization
Table 2 presents both absolute and relative socioeconomic inequalities in DSU. Due to data limitations in the WHO STEPS dataset, socioeconomic inequality estimates for Bangladesh could not be generated and are therefore not reported. In Bhutan, significant absolute inequalities were found. The SII showed a 33.9 (95% CI: 27.8–40.1) percentage point difference in ever utilization and 19.5 (95% CI: 14.5–24.4) percentage points in utilization within one year between the most and least advantaged groups. These disparities were more pronounced in urban areas than rural areas.
Table 2.
Absolute and relative inequality in dental service utilization in Bangladesh, Bhutan, and Nepal
| Slope index of inequality (95% CI) | ||||||
|---|---|---|---|---|---|---|
| Bangladesh | Bhutan | Nepal | ||||
| Ever | Within one year | Ever | Within one year | Ever | Within one year | |
| Socioeconomic inequality | ||||||
| National | - | - | 33.9 (27.8 to 40.1)§ | 19.5 (14.5 to 24.4)§ | −2.6 (−5.3 to 0.1) | −2.3 (−4.2 to −0.4)* |
| Place of residence | ||||||
| Rural | - | - | 23.2 (15.1 to 31.3)§ | 13.6 (7.8 to 19.5)§ | −3 (−7.9 to 2) | −2.1 (−5.7 to 1.5) |
| Urban | - | - | 29.3 (17.9 to 40.6)§ | 22.2 (13.7 to 30.6)§ | −3.2 (−6.6 to 0.2) | −2.7 (−5 to −0.4)* |
| Education-based inequality | ||||||
| National | 4.6 (−0.8 to 10) | 0.8 (−3.1 to 4.7) | 28.5 (21.7 to 35.2)§ | 14.9 (9.5 to 20.4)§ | −5 (−8 to −1.9)$ | −1 (−3 to 1) |
| Place of residence | ||||||
| Rural | −1.3 (−7.7 to 5.1) | −2.1 (−6.6 to 2.5) | 22.1 (12.8 to 31.4)§ | 12.5 (5.9 to 19.2)§ | −4.5 (−9.2 to 0.1) | −0.9 (−4.2 to 2.4) |
| Urban | 17.3 (8.2 to 26.5)§ | 5.3 (−2.4 to 12.9) | 24.3 (12.4 to 36.2)§ | 12.3 (2.7 to 21.7)* | −5.3 (−9.2 to −1.4)$ | −1.1 (−3.6 to 1.4) |
| Relative concentration index (95% CI) | ||||||
| Socioeconomic inequality | ||||||
| National | - | - | 11.4 (9.1 to 13.7)§ | 20.5 (16.5 to 24.7)§ | −0.2 (−8.5 to 8.2) | −6.0 (−16.6 to 4.6) |
| Place of residence | ||||||
| Rural | - | - | 9.1 (6.1 to 12.2)§ | 16.6 (11.7 to 21.4)§ | −1.5 (−12.0 to 9.0) | −6.8 (−20.8 to 7.2) |
| Urban | - | - | 7.6 (4.7 to 10.5)§ | 16.8 (10.3 to 23.5)§ | 1.0 (−10.3 to 12.3) | −3.4 (−18.6 to 11.8) |
| Education-based inequality | ||||||
| National | 6.3 (4.1 to 8.5)§ | 4.4 (0.8 to 7.9)* | 7.8 (5.9 to 9.8)§ | 12.9 (8.6 to 17.2)§ | −1.0 (−7.2 to 5.2) | −1.3 (−9.9 to 7.3) |
| Place of residence | ||||||
| Rural | 0.7 (−2.4 to 3.7) | −0.3 (−5.3 to 4.7) | 6.2 (3.7 to 8.7)§ | 12.1 (5.7 to 18.6)§ | 1.1 (−8.0 to 10.3) | 1.3 (−10.2 to 12.9) |
| Urban | 9.2 (6.4 to 12.1)§ | 6.0 (1.3 to 10.8)* | 4.8 (2.4 to 7.2)§ | 6.6 (1.0 to 12.2)* | −1.8 (−10.1 to 6.4) | −2.4 (−14.7 to 10.0) |
| Relative index of inequality (95% CI) | ||||||
| Socioeconomic inequality | ||||||
| National | - | - | 2.11 (1.86 to 2.39)§ | 3.56 (2.65 to 4.78)§ | 0.63 (0.30 to 1.30) | 0.44 (0.16 to 1.26) |
| Place of residence | ||||||
| Rural | - | - | 2.00 (1.65 to 2.42)§ | 3.17 (2.06 to 4.88)§ | 0.63 (0.17 to 2.34) | 0.55 (0.09 to 3.35) |
| Urban | - | - | 2.07 (1.61 to 2.67)§ | 5.31 (2.82 to 9.99)§ | 0.57 (0.23 to 1.42) | 0.38 (0.09 to 1.43) |
| Education-based inequality | ||||||
| National | 1.17 (1.10 to 1.30)$ | 1.06 (0.89 to 1.26) | 1.85 (1.62 to 2.13)§ | 2.64 (1.93 to 3.61)§ | 0.41 (0.19 to 0.87)* | 0.69 (0.24 to 1.99) |
| Place of residence | ||||||
| Rural | 0.95 (0.84 to 1.07) | 0.83 (0.67 to 1.02) | 1.73 (1.42 to 2.09)§ | 2.77 (1.80 to 4.27)§ | 0.42 (0.11 to 1.53) | 0.74 (0.13 to 4.15) |
| Urban | 1.69 (1.38 to 2.06)§ | 1.39 (1.01 to 1.90)§ | 1.56 (1.26 to 1.94)§ | 1.82 (1.12 to 2.95)§ | 0.40 (0.16 to 1.01) | 0.67 (0.18 to 2.56) |
CI confidence interval
*p < 0.05, $p < 0.01, §p < 0.001, (-) indicates data not available in the WHO STEPS dataset. The most disadvantaged group (no education or poorest quintile) was used as the reference for all inequality measures. All estimates are weighted using country-specific sampling weights provided by the STEPs surveys
Relative measures further underscored the disparities. The RCI for Bhutan was 11.4 (95% CI: 9.1–13.7) for ever DSU and 20.5 (95% CI: 16.5–24.7) for utilization within one year, while the RII values showed that wealthier individuals were 2.11 (95% CI: 1.86–2.39) times more likely to report ever DSU and 3.56 (95% CI: 2.65–4.78) times more likely to have visited a dentist in the past year, compared to the poorest group. In contrast, Nepal showed no clear socioeconomic gradient, and the direction of association was inconsistent, with slightly higher or similar utilization among poorer groups, suggesting either minimal inequality or a potential reverse pattern.
Education-based inequality in dental service utilization
Education-related inequalities (Table 2) were observed in Bhutan and Bangladesh. In Bhutan, SII values indicated a 28.5 (95% CI: 21.7–35.2) percentage point higher ever DSU and a 14.9 (95% CI: 9.5–20.4) point difference in recent DSU among individuals with higher education. Urban areas showed slightly greater disparities than rural areas. RCI and RII values in Bhutan also showed higher DSU among more educated individuals. In Bangladesh, education-based disparities were evident only in urban areas (e.g., RII: 17.3, 95% CI: 8.2–26.5). In Nepal, however, there were no consistent education-based differences, and in some cases, slightly higher DSU was observed among those with lower educational attainment, indicating minimal or inverse inequality patterns (e.g., SII: −5, 95% CI: −8 to −1.9); most inequality measures were not statistically significant.
Determinants of dental service utilization
Table 3 presents the adjusted odds ratios from mixed-effects logistic regression models, identifying independent determinants of DSU in each country. In Bangladesh, adults aged 50–69 years were 2.77 (95% CI: 2.42–3.18) times more likely to use dental services than the youngest group (18–34 years). Higher education was associated with greater utilization, as individuals with secondary education or higher were 1.5 (95% CI: 1.30–1.74) times more likely to use the dental services as compared to those with no schooling. Urban residents also had an increased likelihood of DSU as compared to that in rural residents (odds ratio 1.42, 95% CI: 1.20–1.70). In sensitivity analysis, we observed individuals who are married and not tobacco users were more likely to utilize services (Table S4).
Table 3.
Determinants of ever dental service utilization among adults in Bangladesh, Bhutan, and Nepal
| Characteristics | Odds ratio (95% CI) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Bangladesh | Bhutan | Nepal | |||||||
| Crude model | Model 1 | Model 2 | Crude model | Model 1 | Model 2 | Crude model | Model 1 | Model 2 | |
| Predisposing factors | |||||||||
| Age group | |||||||||
| 18–34 years | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| 35–49 years | 1.69 (1.53–1.86)§ | 2.09 (1.86–2.35)§ | 2.19 (1.95–2.46)§ | 0.94 (0.80–1.10) | 1.36 (1.12–1.66)$ | 1.34 (1.10–1.64)$ | 2.04 (1.21–3.45)$ | 2 (1.12–3.56)* | 1.97 (1.11–3.51)* |
| 50–69 years | 1.87 (1.69–2.07)§ | 2.57 (2.24–2.94)§ | 2.77 (2.42–3.18)§ | 1.15 (0.95–1.39) | 2.18 (1.71–2.77)§ | 2.13 (1.68–2.71)§ | 2.63 (1.53–4.50)§ | 2.78 (1.44–5.35)$ | 2.72 (1.41–5.25)$ |
| Sex | |||||||||
| Male | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Female | 1 (0.92–1.08) | 1.04 (0.95–1.15) | 0.93 (0.84–1.03) | 1.13 (0.98–1.30) | 1.28 (1.09–1.49)$ | 1.12 (0.95–1.32) | 2.04 (1.28–3.25)$ | 2.13 (1.30–3.48)$ | 2.33 (1.35–4.04)$ |
| Marital status | |||||||||
| Single | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Married | 1.46 (1.28–1.67)§ | 1.15 (0.97–1.35) | 1.18 (1.00-1.39) | 0.73 (0.61–0.87)§ | 0.78 (0.64–0.97)* | 0.79 (0.64–0.97)* | 2.86 (1.10–7.40)* | 1.67 (0.61–4.59) | 1.64 (0.60–4.50) |
| Divorced/widowed | 1.98 (1.60–2.45)§ | 1.38 (1.05–1.82)* | 1.47 (1.11–1.93)$ | 0.81 (0.58–1.14) | 0.9 (0.61–1.33) | 0.94 (0.64–1.38) | 4.45 (1.11–17.82)* | 1.62 (0.36–7.24) | 1.54 (0.34–6.90) |
| Education | |||||||||
| No schooling | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| < Primary | 1.01 (0.88–1.15) | 1.32 (1.13–1.53)§ | 1.3 (1.12–1.51)§ | 1 (0.78–1.29) | 1.1 (0.83–1.44) | 1.12 (0.85–1.47) | 0.73 (0.34–1.57) | 1.09 (0.50–2.41) | 1.11 (0.50–2.45) |
| Primary | 1.22 (1.10–1.35)§ | 1.7 (1.49–1.93)§ | 1.61 (1.42–1.83)§ | 1.3 (0.97–1.75) | 1.5 (1.08–2.09)* | 1.58 (1.13–2.19)$ | 0.59 (0.31–1.10) | 0.98 (0.50–1.93) | 0.99 (0.50–1.96) |
| ≥ Secondary | 1.14 (1.02–1.27)* | 1.66 (1.44–1.91)§ | 1.5 (1.30–1.74)§ | 1.97 (1.69–2.30)§ | 1.89 (1.52–2.37)§ | 1.93 (1.55–2.42)§ | 0.55 (0.33–0.92)* | 1.23 (0.64–2.36) | 1.26 (0.66–2.43) |
| Enabling factors | |||||||||
| Household SES | |||||||||
| Poorest | - | - | - | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Poorer | - | - | - | 1.43 (1.13–1.81)$ | 1.5 (1.16–1.94)$ | 1.5 (1.16–1.94)$ | 0.7 (0.36–1.35) | 0.72 (0.37–1.41) | 0.73 (0.37–1.42) |
| Middle class | - | - | - | 1.28 (1.02–1.62)* | 1.25 (0.95–1.64) | 1.22 (0.93–1.60) | 0.78 (0.41–1.47) | 0.82 (0.42–1.58) | 0.83 (0.43–1.62) |
| Richer | - | - | - | 2.17 (1.71–2.75)§ | 1.96 (1.46–2.63)§ | 1.89 (1.41–2.54)§ | 0.65 (0.33–1.28) | 0.67 (0.33–1.36) | 0.68 (0.34–1.38) |
| Richest | - | - | - | 3.4 (2.69–4.30)§ | 2.55 (1.84–3.52)§ | 2.48 (1.79–3.42)§ | 0.65 (0.33–1.26) | 0.66 (0.32–1.38) | 0.68 (0.32–1.42) |
| Place of residence | |||||||||
| Rural area | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Urban area | 1.42 (1.30–1.56)§ | 1.43 (1.20–1.70)§ | 1.42 (1.20–1.70)§ | 1.7 (1.48–1.97)§ | 1.17 (0.90–1.52) | 1.2 (0.93–1.55) | 1.07 (0.68–1.68) | 1.13 (0.70–1.82) | 1.13 (0.70–1.81) |
| Health behaviors/practices | |||||||||
| Current tobacco use | |||||||||
| No | 1.00 | - | 1.00 | 1.00 | - | 1.00 | 1.00 | - | 1.00 |
| Yes | 0.87 (0.80–0.94)§ | - | 0.71 (0.64–0.79)§ | 0.57 (0.48–0.67)§ | - | 0.56 (0.47–0.68)§ | 1.02 (0.64–1.63) | - | 1.24 (0.72–2.14) |
CI Confidence interval, OHI Oral health impact, SES Socio-economic status
*p < 0.05, $p < 0.01, §p < 0.001; (-) indicates data not available in the WHO STEPS dataset. All estimates are weighted using country-specific sampling weights provided by the STEPs surveys
Model 1: Mutually adjusted model
Model 2: Additionally adjusted for smoking status
In Bhutan, age was a significant factor with participants aged 50–69 years having 2.13 (95% CI: 1.66–2.68) times higher likelihood of service use than younger adults (Table 3). SES strongly influenced utilization with individuals from the wealthiest households are more likely to use dental services than those from the poorest households. In Nepal, older individuals, particularly those aged 35–49 and 50–69 years and females had higher odds of ever using dental services.
Discussion
This study evaluated socioeconomic and education-based inequalities in DSU among adults in three South Asian countries, Bangladesh, Bhutan, and Nepal, and further identified key DSU determinants using nationally representative surveys. DSU was highest in Bhutan, followed by Bangladesh, while Nepal had notably low utilization across the population. Significant socioeconomic and educational inequalities in DSU were observed in Bhutan, while in Bangladesh, inequalities were primarily related to education. In both countries, individuals from more advantaged groups, particularly those with higher education or income, were more likely to utilize dental services than their disadvantaged counterparts. In contrast, Nepal showed minimal disparities, reflecting uniformly low access to care across all socioeconomic and educational groups. The key determinants of DSU varied across countries: in Bangladesh, higher utilization was associated with older age, higher education, and urban residence; in Bhutan, with older age, higher education and SES; and in Nepal, with older age and female sex.
Our findings showed notable differences in DSU prevalence across the three countries, reflecting broader trends seen in the LMICs [28–30]. The highest utilization of dental services was observed in Bhutan, which may be attributed to its relatively stronger healthcare infrastructure and integrated oral health services [31]. Bhutan has a unique primary healthcare model in which dental care is included in a wide range of publicly funded services, with curative dental care provided free of charge nationwide [14]. These differences in utilization also reflect the underlying disparities in health system capacity and oral health service provision across countries. Nepal had the lowest DSU, indicating more severe access barriers. Although Nepal introduced a national oral health policy in 2003 and offers services through both public and private sectors, the absence of a national health insurance system and limited public funding restrict access, particularly among poorer populations [32]. While the number of health centers in Nepal is increasing, shortages in the health workforce and limited domestic resources continue to challenge service delivery [33]. On the other hand, DSU in Bangladesh was higher than in Nepal but lower than in Bhutan. The country lacks a strategic national oral health policy, and public health financing does not cover routine, curative, or rehabilitative dental services [34]. Bangladesh also faces greater challenges due to its larger population and demographic diversity, which complicate the implementation of equitable, nationwide oral healthcare services [34].
Our study revealed significant educational-based inequalities in DSU in Bangladesh and with disparities being more pronounced in Bhutan. Individuals with higher education levels were consistently more likely to use dental services, reflecting global trends linking higher educational attainment with better access to healthcare [17]. This trend was also reported in a meta-analysis [15], revealing that individuals with lower educational attainment were 39% less likely to use dental services than those with higher education. These findings are consistent with evidence from another Asian country; for example, a recent meta-analysis from India estimated adult DSU at 24%, with marked regional and socioeconomic disparities [35].
Socioeconomic inequalities were also prominent in Bhutan and Nepal with significantly higher DSU rates in wealthier groups. Both absolute and relative inequality measures were higher in Bhutan, underscoring the substantial gap in DSU between the most and least advantaged households. These findings are consistent with previous studies. For example, Ghanbarzadegan et al. [36] reported substantial socioeconomic inequalities in dental care access. Tchicaya et al. [16] highlighted considerable variations in income-related inequalities across countries, influenced by differences in healthcare infrastructure. Similarly, Ju et al. [37] found that lower-income groups in Australia had significantly less access to dental services, a pattern also observed by Chaianant et al. [30] in Thailand, where persistent income-related inequalities were reported. These findings reinforce the role of financial barriers as a major constraint to equitable access to dental care.
Our analysis revealed that age significantly influence DSU in Bangladesh, Bhutan, and Nepal. Older adults are more likely to utilize dental services, likely due to the accumulation of dental issues with age [38]. Moreover, owing to better healthcare infrastructure and services in urban areas [39] reflect the association between urban residents had higher DSU rates. This may be due to the fact that, rural populations often have physical and financial barriers to accessing care, a trend reported in previous studies [40, 41].
The findings of this study highlight the need for targeted policies to support economically disadvantaged and less educated populations. Strategies such as expanding public dental services, subsidizing out-of-pocket costs, and integrating oral health into national insurance schemes can help reduce financial barriers. Health education campaigns that promote preventive care are also essential, particularly for underserved groups. Barriers to preventive care, including limited awareness, provider shortages, and financial constraints should be addressed through integrated service delivery and community-based outreach. These priorities align with the Sustainable Development Goals and UHC frameworks, which advocate for the inclusion of oral healthcare in broader health systems. Given the region’s demographic pressures, rural–urban disparities, and limited public financing, equity-focused reforms are especially critical in South Asia. Future research should further explore the drivers of Nepal’s extremely low DSU and identify effective strategies for improving access in similarly underserved settings.
Strengths and limitations
Our study has several strengths. First, we used large, nationally representative datasets from Bangladesh, Bhutan, and Nepal, which enhances the generalizability of our findings. Second, we applied well-established measures of inequality including the RCI, RII, and SII to comprehensively assess both relative and absolute disparities in DSU, which also allowed us to capture divergence across measures, particularly relevant in low-utilization settings like Nepal. Third, the Andersen behavioral model provided a structured framework for selecting relevant covariates. Finally, we employed mixed-effects logistic regression to account for the hierarchical structure of the survey data and conducted sensitivity analyses to test the robustness of our model estimates.
This study has several limitations. First, its cross-sectional design limits causal inference, restricting findings to associations at a single point in time. Second, reliance on self-reported DSU data may introduce recall bias, and the exclusion of participants with missing data could result in selection bias. Third, the extremely low DSU prevalence in Nepal (5.6% ever, 0.1% recent) raises concerns about potential data quality issues, cultural influences, or systemic access barriers. Fourth, limited availability of behavioral variables in Bhutan, such as brushing frequency and toothpaste use, constrained the scope of sensitivity analyses. Fifth, preventive care could not be robustly assessed due to the absence of detailed questions on preventive dental visits. Sixth, differences in healthcare infrastructure, service provision, and data collection procedures across countries may affect the comparability of results. Seventh, the lack of socioeconomic data for Bangladesh restricted inequality analyses and cross-country comparisons. Lastly, although the Andersen model offered a structured framework, residual confounding may persist due to unmeasured factors such as insurance coverage or the quality of available dental services.
Conclusion
This study found limited DSU across Bangladesh, Bhutan, and Nepal, with Nepal showing the lowest utilization and minimal or even reversed socioeconomic and educational inequalities. Disparities in DSU were pronounced in Bhutan and to a lesser extent in Bangladesh. These findings highlight the importance of equity-focused health system reforms. In Nepal, addressing access barriers is essential to improve DSU. Bangladesh requires a national oral health policy to ensure coverage and access to essential dental services. In Bhutan, enhancing outreach and service delivery for disadvantaged groups could help reduce existing equity gaps. Additionally, the consistently low rates of preventive DSU across all countries emphasize the need for public education campaigns and integrated service delivery that prioritize prevention.
Supplementary Information
Acknowledgements
We express our gratitude to the WHO NCD microdata repository for granting access to the datasets used in this study. Additionally, we extend our thanks to Mr. Bihungum Bista (Kathmandu University School of Medical Sciences and key team member of the Nepal 2019 STEPS survey; former employee of the Nepal Health Research Council) for his valuable suggestions and insightful clarifications regarding the Nepal STEPS data.
Data sharing
The WHO STEPS dataset is accessible through the WHO NCD microdata repository and can be downloaded following the repository’s guidelines. The data can be accessed from the following URL: https://extranet.who.int/ncdsmicrodata/index.php/home.
Role of the funding source
Not applicable.
Abbreviations
- CI
Confidence interval
- DSU
Dental service utilization
- LMICs
Low–and middle–income countries
- NCD
Non–communicable diseases
- OHI
Oral health impact
- PSU
Primary sampling unit
- RCI
Relative concentration index
- RII
Relative index of inequality
- SES
Socioeconomic status
- SII
Slope index of inequality
- STEPS
STEPwise approach to surveillance
- UHC
Universal Health Coverage
- WHO
World Health Organization
- OR
Odds ratio
Authors’ contributions
A.N. contributed to conceptualization, writing—original draft, writing—review and editing, project administration, and supervision. M.M.R. was responsible for data curation, formal analysis, visualization, and writing—review and editing T.G. and T.N. contributed to writing—review and editing, resources, and validation. A.A., A.S.M.S., and K.A. were involved in data curation, writing—review and editing, resources, and validation. Y.K. provided resources, validation, and writing—review and editing. M.S.R. contributed to conceptualization, formal analysis, writing—review and editing, project administration, and supervision.
Funding
This study received no external funding.
Data availability
The WHO STEPS dataset is accessible through the WHO NCD microdata repository and can be downloaded following the repository’s guidelines. The data can be accessed from the following URL: https://extranet.who.int/ncdsmicrodata/index.php/home.
Declarations
Ethics approval and consent to participate
This study involved secondary analysis of de-identified, publicly available data obtained from the WHO NCD Microdata Repository. The datasets were originally collected as part of the WHO STEPS surveys, which received ethical approval from institutional review boards in each participating country: the Bangladesh Medical Research Council (BMRC), the Research Ethics Board for Health (REBH) under the Ministry of Health in Bhutan, and the Nepal Health Research Council (NHRC). All participants provided written informed consent prior to data collection during the original surveys. Since the current analysis used de-identified data with no access to personal identifiers, and the dataset was publicly available for research use, no additional ethical approval was required for this secondary analysis. Data access permission was obtained from the WHO NCD Microdata Repository.
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.
Contributor Information
Abir Nagata, Email: abir-n@hama-med.ac.jp.
Md Shafiur Rahman, Email: rahman-2s5@kuhs.ac.jp.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The WHO STEPS dataset is accessible through the WHO NCD microdata repository and can be downloaded following the repository’s guidelines. The data can be accessed from the following URL: https://extranet.who.int/ncdsmicrodata/index.php/home.